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jserra@microsoft.com
Machine learning is a data science technique that allows computers to
use existing data to forecast future behaviors, outcomes, and trends.
Enterprise AI Maturity Model
Foundational
Wrong expectations or disappointment
Low digitization
Basic analytical capabilities
Questioning what AI is
and how to apply it
Approaching
Digitization underway
Looking to increase or optimize processes
Cautious about disruption
Hopeful on AI and
its promise
Aspirational
High digitization
Desires new business models
Achieved a data culture
Experimented and
applied AI
Mature
Understands model lifecycle
Building a foundational
data architecture
Emerging data science
and operational capability
© Microsoft Corporation
Helping you innovate across your business
Customer insights Sales insights Virtual assistants Cash flow forecasting HR insights
Churn analytics Dynamic pricing Waiting line optimization Risk management Quality assurance Resource planning
Lead scoring Intelligent chatbots Financial forecasting Employee insights
Marketing Sales Service Finance Operations Workforce
Product recommendationProduct recommendation Predictive maintenancePredictive maintenance
Demand forecastingDemand forecasting
© Microsoft Corporation
Advanced analytics pattern in Azure
Azure Data
Lake store
Azure
Storage
HDInsightAzure Databricks
Azure ML
service
ML server
Model training
Long-term storage Data processing
Azure Data
Lake Analytics
Azure ML
Studio
SQL Server
(in-database ML)
Azure Databricks
(Spark ML)
Data Science
VM
Cosmos DB
Serving storage
SQL DB
SQL DW
Azure Analysis
Services
Cosmos DB
Batch AI
SQL DB
Azure Data
Factory
Orchestration
Azure Container
Service
Trained model hosting
SQL Server
(in-database ML)
Data collection and understanding, modeling, and deployment
Sensors and IoT
(unstructured)
Logs, files, and media
(unstructured)
Business/custom apps
(structured)
Applications
Dashboards
Power BI
Azure Machine Learning Studio
© Microsoft Corporation
Azure machine learning studio
Use Azure Machine
Learning to deploy your
model into production as
a web service in minutes
Work with familiar coding
languages such as R and
Python while benefitting
from hundreds of built-in
packages and support for
custom code
Deploy models to production
as web services that can be
called from any device,
anywhere, using any data
source
Easily share your solution
with the world on the
Azure Marketplace
A fully-managed cloud service that enables you to easily build,
deploy and share predictive analytics solutions
AZURE MACHINE LEARNING STUDIO
Platform for emerging data scientists
to graphically build and deploy
experiments
• Rapid experiment composition
• > 100 easily configured modules
for data prep, training, evaluation
• Extensibility through R & Python
• Serverless training and
deployment
Some numbers:
• 100’s of thousands of deployed
models serving billions of requests
https://gallery.azure.ai/
Data Science Virtual Machines (DSVM)
Data Science Virtual Machines
Azure Machine Learning service
WHAT IS AZURE MACHINE LEARNING SERVICE?
Set of Azure Cloud
Services
Python
SDK
 Prepare Data
 Build Models
 Train Models
 Manage Models
 Track Experiments
 Deploy Models
That enables
you to:
Workspace – Top level container for model management and
experimentation. Also, creates and manages storage, container
registry, key vault and app insights
Experiments – Grouping of training runs for a given script
Pipelines – Stitch together multiple stages to create machine
learning workflows
Compute – Compute resources for training or image
deployment
Models – Encapsulation of trained models
Images – Docker images containing trained models to be
deployed to a compute target
Deployments – Deployment of an image to web services, IoT
Edge and FPGAs
Data Stores – Abstraction layer over Azure storage
Azure Machine Learning
service features
Custom AI
Typical E2E Process
…
Prepare Experiment Operationalize/Deploy
Orchestrate
Azure Databricks
Machine Learning Compute
Data Science Virtual Machine
Azure ML service Azure Container Instances
Azure Kubernetes ServiceAzure Databricks
Azure Data Factory
Azure ML Concept
Model Management
Model Management in Azure ML
usually involves these four steps
Step 1:
Step 2:
Step 3:
Step 4:
Azure ML Artifact
Compute Target
Compute Target Training Deployment
Local Computer ✓
A Linux VM in Azure (such as the Data
Science Virtual Machine)
✓
Azure ML Compute ✓
Azure Databricks ✓
Azure Data Lake Analytics ✓
Apache Spark for HDInsight ✓
Azure Container Instance ✓
Azure Kubernetes Service ✓
Azure IoT Edge ✓
Field-programmable gate array (FPGA) ✓
Currently supported compute targets
Azure ML service Artifacts
Image and Registry
Image contains
Two types of images
Image Registry
Azure ML Artifact
Deployment
Deployment is an instantiation of an image. Two options:
Web service IoT Module
Why Intelligent Edge?
High-speed data processing,
analytics and shorter response
times are more essential than ever.
Intelligent Cloud
• Business agility and scalability: unlimited computing
power available on demand.
Intelligent Edge
• Can handle priority-one tasks locally
even without cloud connection.
• Can handle generated data that is too
large to pull rapidly from the cloud.
• Enables real-time processing through
intelligence in or near to local devices.
• Flexibility to accommodate data privacy related
requirements.
Azure ML service
Lets you easily implement this AI/ML Lifecycle
Azure
Machine Learning
Workflow Steps
Azure ML: How to deploy models at scale
Services
+
Customer journey Data Prep Build and Train Manage and Deploy
Apache Spark / Big Data
Python ML developer
Azure ML service
(Pandas, NumPy etc. on AML Compute)
Azure ML service
(OSS frameworks, Hyperdrive, Pipelines,
Automated ML, Model Registry)
Azure ML service
(containerize, deploy,
inference and monitor)
Azure ML service
(containerize, deploy,
inference and monitor)
Azure Databricks
(Apache Spark Dataframes,
Datasets, Delta, etc.)
Azure Databricks + Azure ML service
(Spark MLib and OSS frameworks +
Automated ML, Model Registry)
Comparable Table
Azure Machine Learning Studio Azure Machine Learning Services
Pros • Rapid development (Drag and Drop)
• Works well with relatively simple
datasets
• Pre-built ML algorithms
• Cheap
• Fast (VMs with GPUs)
• Different optimization methods,
CI/CD pipeline
• Full control during training
• Manage computing resources
(choose VM size)
• Use open source ML libraries
Cons • Can be slow
• Limited optimization methods,
operationalized architecture
• Less control during training
• Fixed computing resources
• More elaborate to build, require
deeper knowledge of machine
learning
• Deeper models need much more
data with much more memory
• Higher costs for VM with GPU
Azure Databricks
Databricks Spark as a managed service on Azure
Azure Databricks key audiences & benefits
Unified analytics platform
Integrated workspace
Easy data exploration
Collaborative experience
Interactive dashboards
Faster insights
• Best Spark & serverless
• Databricks managed Spark
Improved ETL performance
• Zero management clusters, serverless
Easy to schedule jobs
Automated workflows
Enhanced monitoring & troubleshooting
• Automated alerts & easy access to logs
Zero Management Spark
Cluster democratization (serverless)
Fast, collaborative analytics platform
accelerating time to market
No dev-ops required
Enterprise grade security
• Encryption
• End-to-end auditing
• Role-based control
• Compliance
Data scientist Data engineer CDO, VP of analytics
Provided by Microsoft and Databricks under NDA
A Z U R E D A T A B R I C K S
Microsoft Azure
A Z U R E D A T A B R I C K S C O R E A R T I F A C T S
Azure
Databricks
P R O V I S I O N I N G A Z U R E D A T A B R I C K S W O R K S P A C E
© Microsoft Corporation
Comparing Notebooks in Azure Databricks against other IDEs
Data understanding services
Notebooks in Azure Databricks Other IDEs
No
Azure Databricks only
Yes
Spark
Python, Scala, R, SQL, Bash Shell
Provides extensive visualizations library in
addition to supporting 3rd party libraries.
Full Azure Active Directory integration
Simultaneous, multi-user collaboration
Yes
GitHub, Bitbucket
Requires software installation Yes
Pieced together, disparate solutions
No
Python, PySpark
Python, SQL, Bash Shell
Supports standard Jupyter Notebook
visualizations and libraries like Matplotlib
No
No
No
Yes, but not optimal
Execution environment
Serverless service
Kernels supported
Languages supported
Visualizations
Supports role-based access control
Collaborative workspaces
Run notebooks as scheduled jobs
Source control
M I X I N G L A N G U A G E S I N N O T E B O O K S
You can mix multiple languages in the same notebook
Normally a notebook is associated with a specific language. However, with
Azure Databricks notebooks, you can mix multiple languages in the same
notebook. This is done using the language magic command:
• %python Allows you to execute python code in a notebook (even if that notebook is not python)
• %sql Allows you to execute sql code in a notebook (even if that notebook is not sql).
• %r Allows you to execute r code in a notebook (even if that notebook is not r).
• %scala Allows you to execute scala code in a notebook (even if that notebook is not scala).
• %sh Allows you to execute shell code in your notebook.
• %fs Allows you to use Databricks Utilities - dbutils filesystem commands.
• %md To include rendered markdown
© Microsoft Corporation
For Cloud environments
Train and evaluate machine learning architecture
Raw data
Reference data
• 0
1
Machine learning
Azure
Databricks
Scale out clusters
Azure
Databricks
Operational stores
Data
warehouse
SQL DW
Cosmos
DB
Model MGMT, experimentation,
and run history
Azure ML
Service
(preview)
Azure ML
service
(preview)
© Microsoft Corporation
Fast, easy, and collaborative Apache Spark™-based analytics platform
Azure Databricks for machine learning modeling
Tools InfrastructureFrameworks
Provision autoscaling clusters on-demandUse best-in-class notebooks to quickly
access model performance and revert
when needed
TensorFlow, Keras, and XGBoost, all
installed and configured for Spark clusters
Enable distributed, multi-GPU training
with Horovod via a native runtime
Get seamless updates of the Spark stack
to ensure uninterrupted operations
Capture model telemetry at every
stage to enable reproduceable results
Schedule notebook activities as jobs in
and let Azure Data Factory orchestrate
the rest
Take advantage of Azure ML Services to for
simple Kubernetes Cluster deployments
Leverage parallelized ML algorithms from
battle-tested libraries
© Microsoft Corporation
Build in your environment of choice
Custom IDEs
Visual studio tools for AINotebooksR studio PyCharm
S P A R K M A C H I N E L E A R N I N G ( M L ) O V E R V I E W
 Offers a set of parallelized machine learning algorithms (see next
slide)
 Supports Model Selection (hyperparameter tuning) using Cross
Validation and Train-Validation Split.
 Supports Java, Scala or Python apps using DataFrame-based API (as
of Spark 2.0). Benefits include:
• An uniform API across ML algorithms and across multiple languages
• Facilitates ML pipelines (enables combining multiple algorithms into a
single pipeline).
• Optimizations through Tungsten and Catalyst
• Spark MLlib comes pre-installed on Azure Databricks
• 3rd Party libraries supported include: H20 Sparkling Water, SciKit-
learn and XGBoost
Enables Parallel, Distributed ML for large datasets on Spark Clusters
M M L S P A R K
Microsoft Machine Learning Library for Apache Spark (MMLSpark) lets
you easily create scalable machine learning models for large datasets.
It includes integration of SparkML pipelines with the Microsoft
Cognitive Toolkit and OpenCV, enabling you to:
 Ingress and pre-process image data
 Featurize images and text using pre-trained deep learning models
 Train and score classification and regression models using implicit
featurization
S P A R K M L A L G O R I T H M S
Spark ML
Algorithms
D E E P L E A R N I N G
 Supports Deep Learning Libraries/frameworks including:
• Microsoft Cognitive Toolkit (CNTK)
• TensorFlow
• Keras
• Theano
 Offers Spark Deep Learning Pipelines, a suite of tools for working with
and processing images using deep learning using transfer learning. It
includes high-level APIs for common aspects of deep learning so they
can be done efficiently in a few lines of code:
Azure Databricks supports and integrates with a number of Deep Learning libraries and
frameworks to make it easy to build and deploy Deep Learning applications
Distributed Hyperparameter Tuning
Transfer Learning
D E E P L E A R N I N G P I P E L I N E S

 Transfer Learning APIs for common aspects of deep learning so they can be done
efficiently in a few lines of code

Transfer Learning
T R A N S F E R L E A R N I N G
 ImageNet –
 InceptionV3 – Created @ Google Brain
 Xception – François Chollet
 ResNet50 –
ImageNet Large Scale Visual Recognition Competition
 VGG16/VGG19 – Introduced in 2014. This network is characterized by its simplicity,
using only 3×3 convolutional layers stacked on top of each other. The 16 and 19
stand for the number of weight layers in the network. VGG stands for Visual
Geometry Group.
Pre-Trained Libraries
Ready to use clusters with built-in ML Frameworks
HorovodEstimator
for simplified distributed training on TensorFlow with Apache Spark
GPU support
49
A D V A N C E D A N A L Y T I C S O N B I G D A T A
INGEST STORE PREP & TRAIN MODEL & SERVE
Cosmos DB
Business/custom apps
(structured)
Files (unstructured)
Media (unstructured)
Logs (unstructured)
Azure Data Lake Store Gen2Azure Data Factory Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
PolyBase
SparkR
Azure Databricks
Microsoft Azure also supports other Big Data services like Azure HDInsight, Azure Machine Learning to allow customers to tailor the above architecture to meet
their unique needs.
Real-time apps
© Microsoft Corporation
Operationalization and management architecture
Scale out clusters
Notebooks
Azure
Databricks
Containers
AKS ACI
IoT edge
Intelligent Apps
Power BI
Docker
Model MGMT, experimentation,
and run history
Azure ML
service
(Preview)
Data warehouse
SQL DW
Operational stores
Cosmos
DB
C L I C K T O E D I T M A S T E R T I T L E S T Y L E
B I G D A T A S C A L E W I T H M I C R O S O F T M L S E R V E R
f(x)
Task
Task
Task
 Full support for R and Python
 Scale via parallel & distributed computation
 On-premises deploys to multiple platforms:
 Machine Learning Server for Hadoop
 Machine Learning Server for Linux
 Machine Learning Server for Windows
 In Azure, available within:
 ML Server on HDInsight (Hadoop + Spark)
 Machine Learning Server on Linux VM
 Data Science VM (Linux and Windows)
 ML Server provides the underlying functionality integrated into
SQL Server as Machine Learning Services (In-Database), which is
available to:
 All editions of SQL Server 2017
 It is also available in Azure SQL Database
Machine Learning Server at a glance
Platforms & Data
Tools
Languages
Algorithms
Data Sources
Rattle Mrsdeploy
RESTful API
deployment
Real-Time
Scoring
Visualization
Tool
Integration
.csv Microsoft .XDF
In-database
deployment
Operationalization
Distributed Parallelized Algorithms:
•RevoScaleR library
•MicrosoftML library
•Custom parallelization frameworks
Open source R algorithms
& visualizations:
•CRAN
•bioconductor
Plus:
•Deep Learning
•Pretrained Models
•Prebuilt Featurizers
ODBC/JDBC
ML Server for Hadoop/Spark:
In-memory, massive parallelism
User
Workstation
ML Server for Hadoop
Data
Frames
YARN Resource
Management
Spark
Executor
Worker
Task
Spark
Executor
Worker
Task
Spark
Executor
Worker
Task
.csv, xdf, hive,
parquet, ORC
ScaleR
Master Task
Finalize
r
Initiator
Edge Node
Spark
Driver
Ver. 1.6 or 2.0
ML Server
components
installed
In-database analytics with SQL Server 2017
Build intelligent apps and
AI with all your data
Analyzing all data
Easily and securely manage
data big and small
Managing all data
Simplified management and analysis through a unified deployment, governance, and tooling
Unified access to all your data with
unparalleled performance
Integrating all data
Including Relational, noSQL, Hadoop
Using Spark and SQL
With Azure Data Studio
SQL Server 2019 big data, analytics, and AI
Managed SQL Server, Spark,
and data lake
Store high volume data in a data lake and access
it easily using either SQL or Spark
Management services, admin portal, and
integrated security make it all easy to manage
SQL
Server
Data virtualization
Combine data from many sources without
moving or replicating it
Scale out compute and caching to boost
performance
T-SQL
Analytics Apps
Open
database
connectivity
NoSQL Relational
databases
HDFS
Complete AI platform
Easily feed integrated data from many sources to
your model training
Ingest and prep data and then train, store, and
operationalize your models all in one system
SQL Server External Tables
Compute pools and data pools
Spark
Scalable, shared storage (HDFS)
External
data sources
Admin portal and management services
Integrated AD-based security
SQL Server
ML Services
Spark &
Spark ML
HDFS
REST API containers
for models
Compute pool
SQL Compute
Node
SQL Compute
Node
SQL Compute
Node
…
Compute pool
SQL Compute
Node
IoT data
Directly
read from
HDFS
Persistent storage
…
Storage pool
SQL
Server
Spark
HDFS Data Node
SQL
Server
Spark
HDFS Data Node
SQL
Server
Spark
HDFS Data Node
Kubernetes pod
Analytics
Custom
apps BI
SQL Server
master instance
Node Node Node Node Node Node Node
SQL
Data mart
SQL Data
Node
SQL Data
Node
Compute pool
SQL Compute
Node
Storage Storage
Learn more.
R
Integration with Azure services
Azure and Power BI have the built-in
connectivity and integration to bring business
intelligence efforts to life
Integration can be achieved without the need
to develop complex solutions:
 Direct connect:
 Azure SQL Database
 Azure SQL Data Warehouse
 Spark on Azure HDInsight
 Power BI Desktop
 Real-time dashboards with Azure Stream Analytics
HDInsight
Storage
Event
Hubs
Machine
Learning
SQL Server
Stream
Analytics
Power BI
Integration with Azure services
Power BI Desktop
Connect to various Azure
services, and create queries
 Queries can be integrated with other
data source types, not necessarily
Azure
 Datasets sourced from a Power BI
Desktop file can be refreshed
Integration with Azure services
Real-time dashboards with Azure Stream Analytics
Use Azure Stream Analytics to push live, streaming data to
Power BI
 Enables real-time dashboards at scale, over data from devices and applications
 Can stream millions of events per second
 Can perform aggregation over time windows
Integration with Azure services
Advanced analytics
Coalesce Azure services
together to drive advanced
analytics:
 Azure HDInsight:
Big Data processing
 Azure Machine Learning:
Predictive analytics
 Azure Data Factory:
Orchestration at scale
Describing additional capabilities
Running R and Python scripts
R Python scripts can be ran directly in
Power BI Desktop, and resulting
datasets imported into a Power BI
Desktop data model
 R, Python must be installed on the local
machine
 Only data frames are imported
 Columns typed as Complex and Vector are not
imported
 Can be refreshed with a gateway
Describing additional capabilities
Generating R and Python visuals
R and Python visuals render from R script,
accepting input fields
Benefits:
 Leverage the voluminous and growing number of out-of-
the-box plots available in R and Python
 Easily customize R and Python visuals by developing the
script
 Combine advanced analytics in visuals
 Interact with R and Python visuals in Power BI Desktop
(filter, and cross-filter are supported)
Transform your data
Using R or Python
Point and click to explain the increase/decrease
Data transformations “by example”
Relationship and data type detection
Built in integration with R and Python
Power BI AutoML
The flow suggests default set of inputs for the ML model,
including those from related entities in the Dataflow.
• The model accuracy preview
report includes the top
influencers for the model,
including a breakdown of how
the different values for that
influencer affects the
outcome.
www.botframework.com
Role of Cognitive Services
making it easier to infuse AI
frameworks
TensorFlow KerasPytorch Onnx
ML
Extract intent Detect empty shelvesExtract identity
generic
specialized
effort
pioneer
expert
developer
everyone
Pre-built,
customizable
services
face OCR text vision speech translation
QnA
......
use case featuresF1 F2 F3 F4
how deep in the stack?
Cognitive Services capabilities
Infuse your apps, websites, and bots with human-like intelligence
https://azure.microsoft.com/en-us/services/cognitive-services
A variety of real-world applications
Vision Speech
Intent: PlayCall
Language Knowledge Search
Face Detection
Face API detects up to 64 human faces with high precision face location in an image. And the image can be
specified by file in bytes or valid URL.
Face Recognition
Face recognition is widely used in many scenarios including security, natural user interface, image content
analysis and management, mobile apps, and robotics. Four face recognition functions are provided: face
verification, finding similar faces, face grouping, and person identification.
Key features:
• Detect human faces and compare similar ones
• Organize images into groups based on similarity
• Identify previously tagged people in images
Vision – Face
Azure Cognitive Services
Visual Intelligence Made Easy
Easily customize your own state-of-the-art computer
vision models that fit to your unique use case. Just bring
a few examples of labeled images and let Custom Vision
do the hard work.
Custom Vision
Azure Cognitive Services
Speech
Azure Cognitive Services
For optimal result in Speech to Text, customize:
• acoustic models for your use environments, such as vehicles
• field-specific vocabulary and grammar, such as medical or IT
• pronunciation of abbreviations and acronyms, such as "IOU" for "I owe you."
• Speaker Recognition
• Speech to Text
• Text to Speech
LUIS – Language Understanding
Azure Cognitive Services
LUIS aims to be the most comprehensive cloud-based service for conversational understanding,
and the easiest to use for developers with no AI expertise:
• extracts intent/action and entities from user utterances
• includes dictionaries, which can be extended
with customer-specific terms
Roadmap highlights coming up:
• 2H2018 easier to use together with
Azure Bot, Speech and QnA Maker services
Q&A
QnA Maker
Azure Cognitive Services
Microsoft & NDA Customers only—do not share
Extract insights from customer feedback and social network postings.
I had a wonderful trip to Seattle and
enjoyed seeing the Space Needle!
• submit up to 100 calls per minute, each with up to 1,000 documents of up to 5,000 characters each
Links backed by Wikipedia
in select languages
Text Analytics
Azure Cognitive Services
translate dynamic content in your mobile, desktop and web apps
 automatically detect languages
 transliterate into different alphabets
Text Translator –
Translation
Azure Cognitive Services
60+ supported languages: https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/language-support
Resources:
• Blog
• Case Study
• Documentation
• Samples
Content Moderator
Azure Cognitive Services
Content Moderator helps businesses manage risks associated with user generated content
(UGC) by using machine-assisted content moderation APIs and a human review tool.
Features include:
 Detection of potential adult, racy, and offensive, illegal, and unwanted image & video content
 Identification of possible profanity and undesirable text
 Built-in human review tool for improving the results
 Workflows that allow you to add other API’s and extract more content insights
Customers using Content Moderator technologies include:
 Online marketplaces/e-Commerce sites for moderating catalogs and chatbots
 Social media/messaging/gaming platforms for moderating user content and digital assets
 Enterprises/K-12 solution providers moderating user content and information chatbots
 Global content moderation service providers using AI to augment human moderation teams
Bing Search
Azure Cognitive Services
– Ingest paper
– Identify customers and staff
– Customer care
– Customer understanding
? Searchable content
image + markup
{ “Bank Statement": [
{ “bank”: “Rural Bank”,
“acc name”: “Joe Sample”,
“opening”:
{ “balance”: $7,701.18”,
“date”: “01/02/2011”
}
“deposits”: “$70,062.31”
…
}]
}
Applications
•
•
•
•


example Solution Architecture
Custom Vision
tuned for docs
training
live ops
learned parameters
Doc classifier
(per industry)
...
pre-processor
tagged extraction mask
Form extractors
(10s-100s per industry)
...
Domain expert
describes regions for extraction
(1 per doc class)
SSN: ###-##-#### Income: #######.## ...
...
1000’s – millions
of docs per company
{ “W2 Tax Form":
{ “year”: “2018”,
“name”: “Joe Sample”,
“address”:
{ “number”: “921”,
“street”: “Getaway Lane”
}
“1 Wages”: “$77,500.34”
…
}
}
Markup Content
...
Applications
•
•
•
•


train identities
live ops
Face
Verify
EmotionSpeaker
Verify
Assess and react:
•
•
"disgust": 0.6847,
"sadness": 0.2135,
"anger": 0.0955…
"happy": 0.999…

"happy": 0.992…
Extra security:
•


Applications
•
•
•
•


pass if needed,
provide context
$$$
chasm
no-code
management
Azure Search
Fuzzy matching
+
QnA Maker
live ops
Azure Indexing
+
QnA Editor
QnA Maker
Knowledgebase (KB)
FAQs, user manuals
channels:
training
live ops
https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/tutorials/integrate-qnamaker-luis
Operations
live data
Stores
Products
Policies
QnA Maker
QnA Maker
QnA Maker
Products KB
Stores KB
...
...
Operation utterances
LUIS
LUIS
Intent
Classifier
One intent:
- per KB
- per Operation
natural language
documents( )
dispatch
on intent
...
live ops
QnA Maker KB
...
Operations
https://github.com/Azure-Samples/cognitive-services-csharp-call-center
as needed
Azure Bot
...
intent
+
entities
troubleshoot
Sentiment
Analysis
red flag
LUISLanguage
Detection
Translation
as needed
Speech
to Text
Text to
Speech
QnA Ma
...
Operati
Azure Bot
...
Annotated logs:
+ sentiment
+ intent + entities
Applications
•
•

Key phrase
extraction
Speech
to Text
Recordings
(archival)
troubleshoot
Sentiment
Analysis
Social Media
Reviews
Voice of Customer
Evolve:
•
•
•
Automated Machine Learning
(in Machine Learning Services)
What is automated
machine learning?
© Microsoft Corporation
AutoML
Azure Automated ML
Current Capabilities
Category Value
Compute
Target
Web UI [Azure portal - Preview]
Private Preview - 2/11
•Getting started experience
•Start an automated ml training job on your data
(upload/Blob)
•Explore data
•View progress and results for training job and each iteration
•View automated ML dashboard for an overview
Public Preview - April 2019
•Set advanced settings for the training job
•Forecasting support
•Automatically deploy a model
•Advanced data exploration (visualizations, correlations, etc.)
•View model Explainability
•Support additional 1p data sources (e.g. Azure SQL, ADLS, …)
May 2019 – General Availability
•Apply data transformations
•Support additional 1p & 3p data source
•Continue experiment in Jupyter Notebook
Microsoft confidential (NDA)
Create automated ML experiments from UI only (“no code”)
• Get started with ML quickly
• Powered by the automated ML SDK – easily customize advanced settings
• Automate the end-to-end ML workflow from the UI
• Easily explore and monitor all your experiments from a single place
Roadmap
“Day 0” (First time) experience
Create your first experiment with automated machine learning to
produce quality models with zero effort
Automate the process of algorithm selection, hyperparameter tuning, and best model selection
with automated machine learning, and accelerate your productivity. Select your data and let
automated ML do the rest to provide the best model from endless possible options
Create a new automated machine learning experiment
* Training compute 
amlcompute-4a57de20b3f1
Next
* Experiment name
myfirstautomlexperiment
Cancel
Create new
Create a new automated machine learning experiment
* Training compute 
Select existing…
* Experiment name
myfirstautomlexperiment
Create new
Select a file from Azure blob storage account
Data Preview Data Profile
Include in training Include in training Include in training Include in training Include in training Include in training Include in training Include in training
Use first row as header
Select a storage account and a container to view the files list
mytestmlexperimentsto… automl-blob-d8b2d4df-…* Storage account * Container
Include in training Include in training Include in training Include in training Include in training Include in training Include in training Include in training
Data Preview Data Profile
Training job settings:
* Training job type  Classification
* Target column  Freight cost
* Primary metric  Accuracy
Number of iterations: 100
Include in training Include in training Include in training Include in training Include in training Include in training Include in training Include in training
Data Preview Data Profile
Training job settings:
* Training job type  Classification
* Target column  Freight cost
* Primary metric  Accuracy
Number of iterations: 100
Data Preview Data Profile
Condensed view
Training job settings:
* Training job type  Classification
* Target column  Freight cost
* Primary metric  Accuracy
Number of iterations: 100
StartCancel
Myfirstautomlexperiment | Run #XXX
Automated machine learning dashboard
#
Running
#
Completed
#
Failed
#
Cancelled
“Day 30” experience
0
5
10
15
Run status history (All)
Cancelled
Failed
Completed
Running
All dates
© Microsoft Corporation
A side-by-side comparison of capabilities and features
Model deployment options
Scoring interface
provided
Deployment
environments
Scalability of
scoring interface
Scoring
requirements
Model packaging
SQL Server or SQL Database
T-SQL stored procedure
SQL Server 2017 database instance
on-premises or in Azure VM
Need to author Python or R code
within a T-SQL stored procedure that
loads the trained model from a table
where it is stored and applies it in
scoring.
Serialized to table
Limited to capacity of single server
Azure Databricks
Notebook or Job
Load the trained model from storage
and apply to scoring in notebook in
Python, Scala, R, or SQL.
Serialized to storage
Azure Databricks cluster, model export
Can scale across cluster resources
Web service
Create a Docker image that contains
scoring service, model, and
dependencies
Docker image
SQL Server, Hadoop
Scales by deploying more instances
in Azure Container Services
Azure Machine Learning
AKS, ACI edge via AML
IoT, IoT edge via AML
AKS, ACI
IoT, IoT edge
Spark and Batch AI
© Microsoft Corporation
Machine learning and AI portfolio
What engines do you want to use?
Deployment target
Which experience do you want?
Build your own or consume
pre-trained models?
Microsoft ML &
AI products
Build your own
Azure Machine
Learning
Code first
(On-prem)
ML Server
On-prem
Hadoop
SQL Server
(cloud)
BYOT
SQL Server Hadoop Azure Batch DSVM Spark
Visual tooling
(cloud)
AML Studio
Consume
Cognitive
services, bots
Spark ML,
SparkR, SparklyR
Notebooks Jobs
Azure Databricks
Spark
When to use what
A D V A N C E D A N A L Y T I C S O N B I G D A T A
INGEST STORE PREP & TRAIN MODEL & SERVE
Cosmos DB
Business/custom apps
(structured)
Files (unstructured)
Media (unstructured)
Logs (unstructured)
Azure Data Lake Store Gen2Azure Data Factory Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
PolyBase
SparkR
Azure Databricks
Microsoft Azure also supports other Big Data services like Azure HDInsight, Azure Machine Learning to allow customers to tailor the above architecture to meet
their unique needs.
Real-time apps
Artificial Intelligence Decision Tree
Big Data Decision Tree v4
Business Intelligence Solutions Decision Tree
Q & A ?
James Serra, Big Data Evangelist
Email me at: jamesserra3@gmail.com
Follow me at: @JamesSerra
Link to me at: www.linkedin.com/in/JamesSerra
Visit my blog at: JamesSerra.com (where this slide deck is posted via the “Presentations” link on the top menu)
Customer
analytics
Financial
modeling
Risk, fraud,
threat detection
Credit
analytics
Marketing
analytics
Faster innovation
for a better
customer experience
Improved consumer
outcomes and
increased revenue
Enhanced customer
experience with
machine learning
Transform growth
with predictive
analytics
Improved customer
engagement with
machine learning
Customer profiles
Credit history
Transactional data
LTV
Loyalty
Customer segmentation
CRM data
Credit data
Market data
CRM
Credit
Risk
Merchant records
Products and services
Clickstream data
Products
Services
Customer service data
Customer 360
degree evaluation
Customer segmentation
Reduced customer churn
Underwriting, servicing and
delinquency handling
Insights for new products
Commercial/retail banking,
securities, trading and
investment models
Decision science, simulations
and forecasting
Investment recommendations
Real-time anomaly detection
Card monitoring and fraud
detection
Security threat identification
Risk aggregation
Enterprise DataHub
Regulatory and
compliance analysis
Credit risk management
Automated credit analytics
Recommendation engine
Predictive analytics and
targeted advertising
Fast marketing and multi-
channel engagement
Customer sentiment analysis
Transaction data
Demographics
Purchasing history
Trends
Effective customer
engagement
Decision services
management
Risk and revenue
management
Risk and compliance
management
Recommendation
engine
Genomics and
precision medicine
Clinical and
claims data
GPU image
processing
IoT device
analytics
Social
analytics
Faster innovation
for drug
development
Improved outcomes
and increased
revenue
Diagnostics
leveraging
machine learning
Predictive analytics
transforms quality
of care
Improved patient
communications
and feedback
FAST-Q
BAM
SAM
VCF
Expression
HL7/CCD
837
Pharmacy
Registry
EMR
Readings
Time series
Event data
Social media
Adverse events
Unstructured
Single cell sequencing
Biomarker, genetic, variant
and population analytics
ADAM and HAIL on Databricks
Claims data warehouse
Readmission predictions
Efficacy and comparative
analytics
Prescription adherence
Market access analysis
Graphic intensive workloads
Deep learning using
Tensor Flow
Pattern recognition
Aggregation of
streaming events
Predictive maintenance
Anomaly detection
Real-time patient feedback
via topic modelling
Analytics across
publication data
MRI
X-RAY
CT
Ultrasound
DNA
sequences
Real world
analytics
Image
deep learning
Sensor
data
Social data
listening
Content
personalization
Customer churn
prevention
Recommendation
engine
Predictive
analytics
Sentiment
analysis
Faster innovation
for customer
experience
Improved consumer
outcomes and
increased revenue
Enhance user
experience with
machine learning
Predictive
analytics
transforms growth
Improved consumer
engagement with
machine learning
Customer profiles
Viewing history
Online activity
Content sources
Channels
Customer profiles
Online activity
Content distribution
Services data
Transactions
Subscriptions
Demographics
Credit data
Content metadata
Ratings
Comments
Social media activity
Personalized viewing and
engagement experience
Click-path optimization
Next best content analysis
Improved real time
ad targeting
Quality of service and
operational efficiency
Market basket analysis
Customer behavior analysis
Click-through analysis
Ad effectiveness
Content monetization
Fraud detection
Information-as-a-service
High value user engagement
Predict audience interests
Network performance
and optimization
Pricing predictions
Nielsen ratings and
projections
Mobile spatial analytics
Demand-elasticity
Social network analysis
Promotion events
time-series analysis
Multi-channel marketing
attribution
Consumption logs
Clickstream and devices
Marketing campaign
responses
Personalized
recommendations
Effective
customer retention
Information
optimization
Inventory
allocation
Consumer engagement
analysis
Next best and
personalized offers
Store design and
ergonomics
Data-driven stock,
inventory, ordering
Assortment
optimization
Real-time pricing
optimization
Faster innovation
for customer
experience
Improved consumer
outcomes and
increased revenue
Omni-channel
shopping
experience with
machine learning
Predictive
analytics
transforms growth
Improved consumer
engagement with
machine learning
Customer profiles
Shopping history
Online activity
Social network analysis
Shopping history
Online activity
Floor plans
App data
Demographics
Buyer perception
Consumer research
Market/competitive analysis
Historical sales data
Price scheduling
Segment level price changes
Customer 360/consumer
personalization
Right product, promotion,
at right time
Multi-channel promotion
Path to purchase
In-store experience
Workforce and manpower
optimization
Predict inventory positions
and distribution
Fraud detection
Market basket analysis
Economic modelling
Optimization for foot traffic,
Online interactions
Flat and declining categories
Demand-elasticity
Personal pricing schemes
Promotion events
Multi-channel engagement
Demand plans
Forecasts
Sales history
Trends
Local events/weather
patterns
Recommendation
engine
Effective customer
engagement
Inventory
optimization
Inventory
allocation
Consumer
engagement
Customer value
analytics
Next best and
personalized offers
Risk and fraud
management
Sales and campaign
optimization
Sentiment
analysis
Faster innovation
for customer
growth
Improved outcomes
and increased
revenue
Risk management
with machine
learning
Predictive
analytics
transforms growth
Improved customer
engagement with
machine learning
Customer profiles
Online history
Transaction data
Loyalty
Customer segmentation
CRM data
Credit data
Market data
CRM
Merchant records
Products
Services
Marketing data
Social media
Online history
Customer service data
Customer 360, segmentation
aggregation and attribution
Audience modelling/index
report
Reduce customer churn
Insights for new products
Historical bid opportunity
as a service
Right product, promotion,
at right time
Real time ad bidding platform
Personalized ad targeting
Ad performance reporting
Real-time anomaly detection
Fraud prevention
Customer spend and
risk analysis
Data relationship maps
Optimizing return on ad
spend and ad placement
Multi-channel promotion
Ideal customer traits
Optimized ad placement
Opinion mining/social
media analysis
Deeper customer insights
Customer loyalty programs
Shopping cart analysis
Transaction data
Demographics
Purchasing history
Trends
Effective customer
engagement
Recommendation
engine
Risk and fraud
analysis
Campaign reporting
analytics
Brand promotion and
customer experience
Digital oil field/
oil production
Industrial
IoT
Supply-chain
optimization
Safety and
security
Sales and marketing
analytics
Faster innovation
for revenue
growth
Improved outcomes
and increased
revenue
Optimizing supply-
chain with machine
learning
Predictive analytics
transforms safety
and security
Improved customer
engagement with
machine learning
Field data
Asset data
Demographics
Production data
Sensor stream data
UAVs images
Inventory data
Production data
Sensor stream data
Transport
Retail data
Grid production data
Refinery tuning parameters
Clickstream data
Products
Services
Market data
Competitive data
Demographics
Production optimization
Integrate exploration
and seismic data
Minimize lease
operating expenses
Decline curve analysis
Pipeline monitoring
Preventive maintenance
Smart grids and microgrids
Grid operations, field service
Asset performance
as a service
Trade monitoring,
optimization
Retail mobile applications
Vendor management -
construction, transportation,
truck and delivery
optimization
Real-time anomaly detection
Predictive analytics
Industrial safety
Environment health and safety
Fast marketing and
multi-channel engagement
Develop new products and
monitor acceptance of rates
Predictive energy trading
Deep customer insights
Transaction data
Demographics
Purchasing history
Trends
Upstream optimization,
maximize well life
Grid operations, asset
inventory optimization
Supply-chain
optimization
Risk
optimization
Recommendations
engine
Intrusion detection
and predictive
analytics
Security
intelligence
Fraud detection
and prevention
Security compliance
reporting
Fine-grained data
analytics security
Prevent complex
threats with
machine learning
Faster innovation
for threat
prevention
Risk management
with machine
learning
Transform security
with improved
visibility
Limit malicious
insiders to
transform growth
Firewall/network logs
Apps
Data access layers
Firewall/network logs
Network flows
Authentications
Firewall/network logs
Web
Applications
Devices
OS
Files
Tables
Clusters
Reports
Dashboards
Notebooks
Prevention of DDoS attacks
Threat classifications
Data loss/anomaly detection
in streaming
Cybermetrics and changing
use patterns
Real-time data correlation
Anomaly detection
Security context, enrichment
Offence scoring, prioritization
Security orchestration
e-Tailing
Inventory monitoring
Social media monitoring
Phishing scams
Piracy protection
Ad-hoc/historic incident
reports
SOC/NOC dashboards
Deep OS auditing
Data loss detection in IoT
User behavior analytics
Role-based access controls
Auditing and governance
File integrity monitoring
Row level and column level
access permissions
Firewall/network logs
Web/app logs
Social media content
Security controls to leverage
all data
Actionable threat
intelligence
Risk and fraud
analysis
Compliance
management
Identity and access
management for analytics

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Machine Learning and AI

  • 2.
  • 3. Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends.
  • 4. Enterprise AI Maturity Model Foundational Wrong expectations or disappointment Low digitization Basic analytical capabilities Questioning what AI is and how to apply it Approaching Digitization underway Looking to increase or optimize processes Cautious about disruption Hopeful on AI and its promise Aspirational High digitization Desires new business models Achieved a data culture Experimented and applied AI Mature Understands model lifecycle Building a foundational data architecture Emerging data science and operational capability
  • 5. © Microsoft Corporation Helping you innovate across your business Customer insights Sales insights Virtual assistants Cash flow forecasting HR insights Churn analytics Dynamic pricing Waiting line optimization Risk management Quality assurance Resource planning Lead scoring Intelligent chatbots Financial forecasting Employee insights Marketing Sales Service Finance Operations Workforce Product recommendationProduct recommendation Predictive maintenancePredictive maintenance Demand forecastingDemand forecasting
  • 6.
  • 7. © Microsoft Corporation Advanced analytics pattern in Azure Azure Data Lake store Azure Storage HDInsightAzure Databricks Azure ML service ML server Model training Long-term storage Data processing Azure Data Lake Analytics Azure ML Studio SQL Server (in-database ML) Azure Databricks (Spark ML) Data Science VM Cosmos DB Serving storage SQL DB SQL DW Azure Analysis Services Cosmos DB Batch AI SQL DB Azure Data Factory Orchestration Azure Container Service Trained model hosting SQL Server (in-database ML) Data collection and understanding, modeling, and deployment Sensors and IoT (unstructured) Logs, files, and media (unstructured) Business/custom apps (structured) Applications Dashboards Power BI
  • 9. © Microsoft Corporation Azure machine learning studio Use Azure Machine Learning to deploy your model into production as a web service in minutes Work with familiar coding languages such as R and Python while benefitting from hundreds of built-in packages and support for custom code Deploy models to production as web services that can be called from any device, anywhere, using any data source Easily share your solution with the world on the Azure Marketplace A fully-managed cloud service that enables you to easily build, deploy and share predictive analytics solutions
  • 10. AZURE MACHINE LEARNING STUDIO Platform for emerging data scientists to graphically build and deploy experiments • Rapid experiment composition • > 100 easily configured modules for data prep, training, evaluation • Extensibility through R & Python • Serverless training and deployment Some numbers: • 100’s of thousands of deployed models serving billions of requests
  • 12. Data Science Virtual Machines (DSVM)
  • 14.
  • 15.
  • 17. WHAT IS AZURE MACHINE LEARNING SERVICE? Set of Azure Cloud Services Python SDK  Prepare Data  Build Models  Train Models  Manage Models  Track Experiments  Deploy Models That enables you to:
  • 18. Workspace – Top level container for model management and experimentation. Also, creates and manages storage, container registry, key vault and app insights Experiments – Grouping of training runs for a given script Pipelines – Stitch together multiple stages to create machine learning workflows Compute – Compute resources for training or image deployment Models – Encapsulation of trained models Images – Docker images containing trained models to be deployed to a compute target Deployments – Deployment of an image to web services, IoT Edge and FPGAs Data Stores – Abstraction layer over Azure storage Azure Machine Learning service features
  • 19. Custom AI Typical E2E Process … Prepare Experiment Operationalize/Deploy Orchestrate Azure Databricks Machine Learning Compute Data Science Virtual Machine Azure ML service Azure Container Instances Azure Kubernetes ServiceAzure Databricks Azure Data Factory
  • 20. Azure ML Concept Model Management Model Management in Azure ML usually involves these four steps Step 1: Step 2: Step 3: Step 4:
  • 21. Azure ML Artifact Compute Target Compute Target Training Deployment Local Computer ✓ A Linux VM in Azure (such as the Data Science Virtual Machine) ✓ Azure ML Compute ✓ Azure Databricks ✓ Azure Data Lake Analytics ✓ Apache Spark for HDInsight ✓ Azure Container Instance ✓ Azure Kubernetes Service ✓ Azure IoT Edge ✓ Field-programmable gate array (FPGA) ✓ Currently supported compute targets
  • 22. Azure ML service Artifacts Image and Registry Image contains Two types of images Image Registry
  • 23. Azure ML Artifact Deployment Deployment is an instantiation of an image. Two options: Web service IoT Module
  • 24. Why Intelligent Edge? High-speed data processing, analytics and shorter response times are more essential than ever. Intelligent Cloud • Business agility and scalability: unlimited computing power available on demand. Intelligent Edge • Can handle priority-one tasks locally even without cloud connection. • Can handle generated data that is too large to pull rapidly from the cloud. • Enables real-time processing through intelligence in or near to local devices. • Flexibility to accommodate data privacy related requirements.
  • 25. Azure ML service Lets you easily implement this AI/ML Lifecycle Azure Machine Learning Workflow Steps
  • 26. Azure ML: How to deploy models at scale
  • 27. Services + Customer journey Data Prep Build and Train Manage and Deploy Apache Spark / Big Data Python ML developer Azure ML service (Pandas, NumPy etc. on AML Compute) Azure ML service (OSS frameworks, Hyperdrive, Pipelines, Automated ML, Model Registry) Azure ML service (containerize, deploy, inference and monitor) Azure ML service (containerize, deploy, inference and monitor) Azure Databricks (Apache Spark Dataframes, Datasets, Delta, etc.) Azure Databricks + Azure ML service (Spark MLib and OSS frameworks + Automated ML, Model Registry)
  • 28. Comparable Table Azure Machine Learning Studio Azure Machine Learning Services Pros • Rapid development (Drag and Drop) • Works well with relatively simple datasets • Pre-built ML algorithms • Cheap • Fast (VMs with GPUs) • Different optimization methods, CI/CD pipeline • Full control during training • Manage computing resources (choose VM size) • Use open source ML libraries Cons • Can be slow • Limited optimization methods, operationalized architecture • Less control during training • Fixed computing resources • More elaborate to build, require deeper knowledge of machine learning • Deeper models need much more data with much more memory • Higher costs for VM with GPU
  • 29. Azure Databricks Databricks Spark as a managed service on Azure
  • 30. Azure Databricks key audiences & benefits Unified analytics platform Integrated workspace Easy data exploration Collaborative experience Interactive dashboards Faster insights • Best Spark & serverless • Databricks managed Spark Improved ETL performance • Zero management clusters, serverless Easy to schedule jobs Automated workflows Enhanced monitoring & troubleshooting • Automated alerts & easy access to logs Zero Management Spark Cluster democratization (serverless) Fast, collaborative analytics platform accelerating time to market No dev-ops required Enterprise grade security • Encryption • End-to-end auditing • Role-based control • Compliance Data scientist Data engineer CDO, VP of analytics Provided by Microsoft and Databricks under NDA
  • 31. A Z U R E D A T A B R I C K S Microsoft Azure
  • 32. A Z U R E D A T A B R I C K S C O R E A R T I F A C T S Azure Databricks
  • 33. P R O V I S I O N I N G A Z U R E D A T A B R I C K S W O R K S P A C E
  • 34. © Microsoft Corporation Comparing Notebooks in Azure Databricks against other IDEs Data understanding services Notebooks in Azure Databricks Other IDEs No Azure Databricks only Yes Spark Python, Scala, R, SQL, Bash Shell Provides extensive visualizations library in addition to supporting 3rd party libraries. Full Azure Active Directory integration Simultaneous, multi-user collaboration Yes GitHub, Bitbucket Requires software installation Yes Pieced together, disparate solutions No Python, PySpark Python, SQL, Bash Shell Supports standard Jupyter Notebook visualizations and libraries like Matplotlib No No No Yes, but not optimal Execution environment Serverless service Kernels supported Languages supported Visualizations Supports role-based access control Collaborative workspaces Run notebooks as scheduled jobs Source control
  • 35. M I X I N G L A N G U A G E S I N N O T E B O O K S You can mix multiple languages in the same notebook Normally a notebook is associated with a specific language. However, with Azure Databricks notebooks, you can mix multiple languages in the same notebook. This is done using the language magic command: • %python Allows you to execute python code in a notebook (even if that notebook is not python) • %sql Allows you to execute sql code in a notebook (even if that notebook is not sql). • %r Allows you to execute r code in a notebook (even if that notebook is not r). • %scala Allows you to execute scala code in a notebook (even if that notebook is not scala). • %sh Allows you to execute shell code in your notebook. • %fs Allows you to use Databricks Utilities - dbutils filesystem commands. • %md To include rendered markdown
  • 36. © Microsoft Corporation For Cloud environments Train and evaluate machine learning architecture Raw data Reference data • 0 1 Machine learning Azure Databricks Scale out clusters Azure Databricks Operational stores Data warehouse SQL DW Cosmos DB Model MGMT, experimentation, and run history Azure ML Service (preview) Azure ML service (preview)
  • 37. © Microsoft Corporation Fast, easy, and collaborative Apache Spark™-based analytics platform Azure Databricks for machine learning modeling Tools InfrastructureFrameworks Provision autoscaling clusters on-demandUse best-in-class notebooks to quickly access model performance and revert when needed TensorFlow, Keras, and XGBoost, all installed and configured for Spark clusters Enable distributed, multi-GPU training with Horovod via a native runtime Get seamless updates of the Spark stack to ensure uninterrupted operations Capture model telemetry at every stage to enable reproduceable results Schedule notebook activities as jobs in and let Azure Data Factory orchestrate the rest Take advantage of Azure ML Services to for simple Kubernetes Cluster deployments Leverage parallelized ML algorithms from battle-tested libraries
  • 38. © Microsoft Corporation Build in your environment of choice Custom IDEs Visual studio tools for AINotebooksR studio PyCharm
  • 39. S P A R K M A C H I N E L E A R N I N G ( M L ) O V E R V I E W  Offers a set of parallelized machine learning algorithms (see next slide)  Supports Model Selection (hyperparameter tuning) using Cross Validation and Train-Validation Split.  Supports Java, Scala or Python apps using DataFrame-based API (as of Spark 2.0). Benefits include: • An uniform API across ML algorithms and across multiple languages • Facilitates ML pipelines (enables combining multiple algorithms into a single pipeline). • Optimizations through Tungsten and Catalyst • Spark MLlib comes pre-installed on Azure Databricks • 3rd Party libraries supported include: H20 Sparkling Water, SciKit- learn and XGBoost Enables Parallel, Distributed ML for large datasets on Spark Clusters
  • 40. M M L S P A R K Microsoft Machine Learning Library for Apache Spark (MMLSpark) lets you easily create scalable machine learning models for large datasets. It includes integration of SparkML pipelines with the Microsoft Cognitive Toolkit and OpenCV, enabling you to:  Ingress and pre-process image data  Featurize images and text using pre-trained deep learning models  Train and score classification and regression models using implicit featurization
  • 41. S P A R K M L A L G O R I T H M S Spark ML Algorithms
  • 42. D E E P L E A R N I N G  Supports Deep Learning Libraries/frameworks including: • Microsoft Cognitive Toolkit (CNTK) • TensorFlow • Keras • Theano  Offers Spark Deep Learning Pipelines, a suite of tools for working with and processing images using deep learning using transfer learning. It includes high-level APIs for common aspects of deep learning so they can be done efficiently in a few lines of code: Azure Databricks supports and integrates with a number of Deep Learning libraries and frameworks to make it easy to build and deploy Deep Learning applications Distributed Hyperparameter Tuning Transfer Learning
  • 43. D E E P L E A R N I N G P I P E L I N E S   Transfer Learning APIs for common aspects of deep learning so they can be done efficiently in a few lines of code  Transfer Learning
  • 44. T R A N S F E R L E A R N I N G  ImageNet –  InceptionV3 – Created @ Google Brain  Xception – François Chollet  ResNet50 – ImageNet Large Scale Visual Recognition Competition  VGG16/VGG19 – Introduced in 2014. This network is characterized by its simplicity, using only 3×3 convolutional layers stacked on top of each other. The 16 and 19 stand for the number of weight layers in the network. VGG stands for Visual Geometry Group. Pre-Trained Libraries
  • 45. Ready to use clusters with built-in ML Frameworks HorovodEstimator for simplified distributed training on TensorFlow with Apache Spark GPU support 49
  • 46.
  • 47. A D V A N C E D A N A L Y T I C S O N B I G D A T A INGEST STORE PREP & TRAIN MODEL & SERVE Cosmos DB Business/custom apps (structured) Files (unstructured) Media (unstructured) Logs (unstructured) Azure Data Lake Store Gen2Azure Data Factory Azure SQL Data Warehouse Azure Analysis Services Power BI PolyBase SparkR Azure Databricks Microsoft Azure also supports other Big Data services like Azure HDInsight, Azure Machine Learning to allow customers to tailor the above architecture to meet their unique needs. Real-time apps
  • 48. © Microsoft Corporation Operationalization and management architecture Scale out clusters Notebooks Azure Databricks Containers AKS ACI IoT edge Intelligent Apps Power BI Docker Model MGMT, experimentation, and run history Azure ML service (Preview) Data warehouse SQL DW Operational stores Cosmos DB
  • 49. C L I C K T O E D I T M A S T E R T I T L E S T Y L E
  • 50. B I G D A T A S C A L E W I T H M I C R O S O F T M L S E R V E R f(x) Task Task Task  Full support for R and Python  Scale via parallel & distributed computation  On-premises deploys to multiple platforms:  Machine Learning Server for Hadoop  Machine Learning Server for Linux  Machine Learning Server for Windows  In Azure, available within:  ML Server on HDInsight (Hadoop + Spark)  Machine Learning Server on Linux VM  Data Science VM (Linux and Windows)  ML Server provides the underlying functionality integrated into SQL Server as Machine Learning Services (In-Database), which is available to:  All editions of SQL Server 2017  It is also available in Azure SQL Database
  • 51. Machine Learning Server at a glance Platforms & Data Tools Languages Algorithms Data Sources Rattle Mrsdeploy RESTful API deployment Real-Time Scoring Visualization Tool Integration .csv Microsoft .XDF In-database deployment Operationalization Distributed Parallelized Algorithms: •RevoScaleR library •MicrosoftML library •Custom parallelization frameworks Open source R algorithms & visualizations: •CRAN •bioconductor Plus: •Deep Learning •Pretrained Models •Prebuilt Featurizers ODBC/JDBC
  • 52. ML Server for Hadoop/Spark: In-memory, massive parallelism User Workstation ML Server for Hadoop Data Frames YARN Resource Management Spark Executor Worker Task Spark Executor Worker Task Spark Executor Worker Task .csv, xdf, hive, parquet, ORC ScaleR Master Task Finalize r Initiator Edge Node Spark Driver Ver. 1.6 or 2.0 ML Server components installed
  • 53.
  • 54. In-database analytics with SQL Server 2017
  • 55.
  • 56. Build intelligent apps and AI with all your data Analyzing all data Easily and securely manage data big and small Managing all data Simplified management and analysis through a unified deployment, governance, and tooling Unified access to all your data with unparalleled performance Integrating all data
  • 57. Including Relational, noSQL, Hadoop Using Spark and SQL With Azure Data Studio
  • 58. SQL Server 2019 big data, analytics, and AI Managed SQL Server, Spark, and data lake Store high volume data in a data lake and access it easily using either SQL or Spark Management services, admin portal, and integrated security make it all easy to manage SQL Server Data virtualization Combine data from many sources without moving or replicating it Scale out compute and caching to boost performance T-SQL Analytics Apps Open database connectivity NoSQL Relational databases HDFS Complete AI platform Easily feed integrated data from many sources to your model training Ingest and prep data and then train, store, and operationalize your models all in one system SQL Server External Tables Compute pools and data pools Spark Scalable, shared storage (HDFS) External data sources Admin portal and management services Integrated AD-based security SQL Server ML Services Spark & Spark ML HDFS REST API containers for models
  • 59. Compute pool SQL Compute Node SQL Compute Node SQL Compute Node … Compute pool SQL Compute Node IoT data Directly read from HDFS Persistent storage … Storage pool SQL Server Spark HDFS Data Node SQL Server Spark HDFS Data Node SQL Server Spark HDFS Data Node Kubernetes pod Analytics Custom apps BI SQL Server master instance Node Node Node Node Node Node Node SQL Data mart SQL Data Node SQL Data Node Compute pool SQL Compute Node Storage Storage
  • 61. Integration with Azure services Azure and Power BI have the built-in connectivity and integration to bring business intelligence efforts to life Integration can be achieved without the need to develop complex solutions:  Direct connect:  Azure SQL Database  Azure SQL Data Warehouse  Spark on Azure HDInsight  Power BI Desktop  Real-time dashboards with Azure Stream Analytics HDInsight Storage Event Hubs Machine Learning SQL Server Stream Analytics Power BI
  • 62. Integration with Azure services Power BI Desktop Connect to various Azure services, and create queries  Queries can be integrated with other data source types, not necessarily Azure  Datasets sourced from a Power BI Desktop file can be refreshed
  • 63. Integration with Azure services Real-time dashboards with Azure Stream Analytics Use Azure Stream Analytics to push live, streaming data to Power BI  Enables real-time dashboards at scale, over data from devices and applications  Can stream millions of events per second  Can perform aggregation over time windows
  • 64. Integration with Azure services Advanced analytics Coalesce Azure services together to drive advanced analytics:  Azure HDInsight: Big Data processing  Azure Machine Learning: Predictive analytics  Azure Data Factory: Orchestration at scale
  • 65. Describing additional capabilities Running R and Python scripts R Python scripts can be ran directly in Power BI Desktop, and resulting datasets imported into a Power BI Desktop data model  R, Python must be installed on the local machine  Only data frames are imported  Columns typed as Complex and Vector are not imported  Can be refreshed with a gateway
  • 66. Describing additional capabilities Generating R and Python visuals R and Python visuals render from R script, accepting input fields Benefits:  Leverage the voluminous and growing number of out-of- the-box plots available in R and Python  Easily customize R and Python visuals by developing the script  Combine advanced analytics in visuals  Interact with R and Python visuals in Power BI Desktop (filter, and cross-filter are supported)
  • 68. Point and click to explain the increase/decrease Data transformations “by example” Relationship and data type detection Built in integration with R and Python
  • 70.
  • 71.
  • 72.
  • 73.
  • 74. The flow suggests default set of inputs for the ML model, including those from related entities in the Dataflow.
  • 75.
  • 76.
  • 77.
  • 78.
  • 79. • The model accuracy preview report includes the top influencers for the model, including a breakdown of how the different values for that influencer affects the outcome.
  • 80.
  • 81.
  • 82.
  • 84. Role of Cognitive Services making it easier to infuse AI frameworks TensorFlow KerasPytorch Onnx ML Extract intent Detect empty shelvesExtract identity generic specialized effort pioneer expert developer everyone Pre-built, customizable services face OCR text vision speech translation QnA ...... use case featuresF1 F2 F3 F4 how deep in the stack?
  • 85.
  • 86. Cognitive Services capabilities Infuse your apps, websites, and bots with human-like intelligence https://azure.microsoft.com/en-us/services/cognitive-services
  • 87. A variety of real-world applications Vision Speech Intent: PlayCall Language Knowledge Search
  • 88.
  • 89. Face Detection Face API detects up to 64 human faces with high precision face location in an image. And the image can be specified by file in bytes or valid URL. Face Recognition Face recognition is widely used in many scenarios including security, natural user interface, image content analysis and management, mobile apps, and robotics. Four face recognition functions are provided: face verification, finding similar faces, face grouping, and person identification. Key features: • Detect human faces and compare similar ones • Organize images into groups based on similarity • Identify previously tagged people in images Vision – Face Azure Cognitive Services
  • 90. Visual Intelligence Made Easy Easily customize your own state-of-the-art computer vision models that fit to your unique use case. Just bring a few examples of labeled images and let Custom Vision do the hard work. Custom Vision Azure Cognitive Services
  • 91. Speech Azure Cognitive Services For optimal result in Speech to Text, customize: • acoustic models for your use environments, such as vehicles • field-specific vocabulary and grammar, such as medical or IT • pronunciation of abbreviations and acronyms, such as "IOU" for "I owe you." • Speaker Recognition • Speech to Text • Text to Speech
  • 92.
  • 93.
  • 94.
  • 95. LUIS – Language Understanding Azure Cognitive Services LUIS aims to be the most comprehensive cloud-based service for conversational understanding, and the easiest to use for developers with no AI expertise: • extracts intent/action and entities from user utterances • includes dictionaries, which can be extended with customer-specific terms Roadmap highlights coming up: • 2H2018 easier to use together with Azure Bot, Speech and QnA Maker services
  • 96.
  • 97.
  • 98.
  • 99.
  • 100.
  • 101.
  • 102.
  • 103. Q&A QnA Maker Azure Cognitive Services Microsoft & NDA Customers only—do not share
  • 104. Extract insights from customer feedback and social network postings. I had a wonderful trip to Seattle and enjoyed seeing the Space Needle! • submit up to 100 calls per minute, each with up to 1,000 documents of up to 5,000 characters each Links backed by Wikipedia in select languages Text Analytics Azure Cognitive Services
  • 105. translate dynamic content in your mobile, desktop and web apps  automatically detect languages  transliterate into different alphabets Text Translator – Translation Azure Cognitive Services 60+ supported languages: https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/language-support
  • 106. Resources: • Blog • Case Study • Documentation • Samples Content Moderator Azure Cognitive Services Content Moderator helps businesses manage risks associated with user generated content (UGC) by using machine-assisted content moderation APIs and a human review tool. Features include:  Detection of potential adult, racy, and offensive, illegal, and unwanted image & video content  Identification of possible profanity and undesirable text  Built-in human review tool for improving the results  Workflows that allow you to add other API’s and extract more content insights Customers using Content Moderator technologies include:  Online marketplaces/e-Commerce sites for moderating catalogs and chatbots  Social media/messaging/gaming platforms for moderating user content and digital assets  Enterprises/K-12 solution providers moderating user content and information chatbots  Global content moderation service providers using AI to augment human moderation teams
  • 108. – Ingest paper – Identify customers and staff – Customer care – Customer understanding
  • 109. ? Searchable content image + markup { “Bank Statement": [ { “bank”: “Rural Bank”, “acc name”: “Joe Sample”, “opening”: { “balance”: $7,701.18”, “date”: “01/02/2011” } “deposits”: “$70,062.31” … }] } Applications • • • •  
  • 110. example Solution Architecture Custom Vision tuned for docs training live ops learned parameters Doc classifier (per industry) ... pre-processor tagged extraction mask Form extractors (10s-100s per industry) ... Domain expert describes regions for extraction (1 per doc class) SSN: ###-##-#### Income: #######.## ... ... 1000’s – millions of docs per company { “W2 Tax Form": { “year”: “2018”, “name”: “Joe Sample”, “address”: { “number”: “921”, “street”: “Getaway Lane” } “1 Wages”: “$77,500.34” … } } Markup Content ...
  • 111. Applications • • • •   train identities live ops Face Verify EmotionSpeaker Verify Assess and react: • • "disgust": 0.6847, "sadness": 0.2135, "anger": 0.0955… "happy": 0.999…  "happy": 0.992… Extra security: •  
  • 113. no-code management Azure Search Fuzzy matching + QnA Maker live ops Azure Indexing + QnA Editor QnA Maker Knowledgebase (KB) FAQs, user manuals channels:
  • 114. training live ops https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/tutorials/integrate-qnamaker-luis Operations live data Stores Products Policies QnA Maker QnA Maker QnA Maker Products KB Stores KB ... ... Operation utterances LUIS LUIS Intent Classifier One intent: - per KB - per Operation natural language documents( ) dispatch on intent ...
  • 115. live ops QnA Maker KB ... Operations https://github.com/Azure-Samples/cognitive-services-csharp-call-center as needed Azure Bot ... intent + entities troubleshoot Sentiment Analysis red flag LUISLanguage Detection Translation as needed Speech to Text Text to Speech
  • 116. QnA Ma ... Operati Azure Bot ... Annotated logs: + sentiment + intent + entities Applications • •  Key phrase extraction Speech to Text Recordings (archival) troubleshoot Sentiment Analysis Social Media Reviews Voice of Customer Evolve: • • •
  • 117. Automated Machine Learning (in Machine Learning Services)
  • 120. Azure Automated ML Current Capabilities Category Value Compute Target
  • 121. Web UI [Azure portal - Preview] Private Preview - 2/11 •Getting started experience •Start an automated ml training job on your data (upload/Blob) •Explore data •View progress and results for training job and each iteration •View automated ML dashboard for an overview Public Preview - April 2019 •Set advanced settings for the training job •Forecasting support •Automatically deploy a model •Advanced data exploration (visualizations, correlations, etc.) •View model Explainability •Support additional 1p data sources (e.g. Azure SQL, ADLS, …) May 2019 – General Availability •Apply data transformations •Support additional 1p & 3p data source •Continue experiment in Jupyter Notebook Microsoft confidential (NDA) Create automated ML experiments from UI only (“no code”) • Get started with ML quickly • Powered by the automated ML SDK – easily customize advanced settings • Automate the end-to-end ML workflow from the UI • Easily explore and monitor all your experiments from a single place Roadmap
  • 122. “Day 0” (First time) experience Create your first experiment with automated machine learning to produce quality models with zero effort Automate the process of algorithm selection, hyperparameter tuning, and best model selection with automated machine learning, and accelerate your productivity. Select your data and let automated ML do the rest to provide the best model from endless possible options
  • 123. Create a new automated machine learning experiment * Training compute  amlcompute-4a57de20b3f1 Next * Experiment name myfirstautomlexperiment Cancel Create new
  • 124. Create a new automated machine learning experiment * Training compute  Select existing… * Experiment name myfirstautomlexperiment Create new Select a file from Azure blob storage account Data Preview Data Profile Include in training Include in training Include in training Include in training Include in training Include in training Include in training Include in training Use first row as header Select a storage account and a container to view the files list mytestmlexperimentsto… automl-blob-d8b2d4df-…* Storage account * Container
  • 125. Include in training Include in training Include in training Include in training Include in training Include in training Include in training Include in training Data Preview Data Profile Training job settings: * Training job type  Classification * Target column  Freight cost * Primary metric  Accuracy Number of iterations: 100
  • 126. Include in training Include in training Include in training Include in training Include in training Include in training Include in training Include in training Data Preview Data Profile Training job settings: * Training job type  Classification * Target column  Freight cost * Primary metric  Accuracy Number of iterations: 100 Data Preview Data Profile Condensed view
  • 127. Training job settings: * Training job type  Classification * Target column  Freight cost * Primary metric  Accuracy Number of iterations: 100 StartCancel
  • 129. Automated machine learning dashboard # Running # Completed # Failed # Cancelled “Day 30” experience 0 5 10 15 Run status history (All) Cancelled Failed Completed Running All dates
  • 130. © Microsoft Corporation A side-by-side comparison of capabilities and features Model deployment options Scoring interface provided Deployment environments Scalability of scoring interface Scoring requirements Model packaging SQL Server or SQL Database T-SQL stored procedure SQL Server 2017 database instance on-premises or in Azure VM Need to author Python or R code within a T-SQL stored procedure that loads the trained model from a table where it is stored and applies it in scoring. Serialized to table Limited to capacity of single server Azure Databricks Notebook or Job Load the trained model from storage and apply to scoring in notebook in Python, Scala, R, or SQL. Serialized to storage Azure Databricks cluster, model export Can scale across cluster resources Web service Create a Docker image that contains scoring service, model, and dependencies Docker image SQL Server, Hadoop Scales by deploying more instances in Azure Container Services Azure Machine Learning AKS, ACI edge via AML IoT, IoT edge via AML AKS, ACI IoT, IoT edge Spark and Batch AI
  • 131. © Microsoft Corporation Machine learning and AI portfolio What engines do you want to use? Deployment target Which experience do you want? Build your own or consume pre-trained models? Microsoft ML & AI products Build your own Azure Machine Learning Code first (On-prem) ML Server On-prem Hadoop SQL Server (cloud) BYOT SQL Server Hadoop Azure Batch DSVM Spark Visual tooling (cloud) AML Studio Consume Cognitive services, bots Spark ML, SparkR, SparklyR Notebooks Jobs Azure Databricks Spark When to use what
  • 132. A D V A N C E D A N A L Y T I C S O N B I G D A T A INGEST STORE PREP & TRAIN MODEL & SERVE Cosmos DB Business/custom apps (structured) Files (unstructured) Media (unstructured) Logs (unstructured) Azure Data Lake Store Gen2Azure Data Factory Azure SQL Data Warehouse Azure Analysis Services Power BI PolyBase SparkR Azure Databricks Microsoft Azure also supports other Big Data services like Azure HDInsight, Azure Machine Learning to allow customers to tailor the above architecture to meet their unique needs. Real-time apps
  • 133. Artificial Intelligence Decision Tree Big Data Decision Tree v4 Business Intelligence Solutions Decision Tree
  • 134. Q & A ? James Serra, Big Data Evangelist Email me at: jamesserra3@gmail.com Follow me at: @JamesSerra Link to me at: www.linkedin.com/in/JamesSerra Visit my blog at: JamesSerra.com (where this slide deck is posted via the “Presentations” link on the top menu)
  • 135.
  • 136. Customer analytics Financial modeling Risk, fraud, threat detection Credit analytics Marketing analytics Faster innovation for a better customer experience Improved consumer outcomes and increased revenue Enhanced customer experience with machine learning Transform growth with predictive analytics Improved customer engagement with machine learning Customer profiles Credit history Transactional data LTV Loyalty Customer segmentation CRM data Credit data Market data CRM Credit Risk Merchant records Products and services Clickstream data Products Services Customer service data Customer 360 degree evaluation Customer segmentation Reduced customer churn Underwriting, servicing and delinquency handling Insights for new products Commercial/retail banking, securities, trading and investment models Decision science, simulations and forecasting Investment recommendations Real-time anomaly detection Card monitoring and fraud detection Security threat identification Risk aggregation Enterprise DataHub Regulatory and compliance analysis Credit risk management Automated credit analytics Recommendation engine Predictive analytics and targeted advertising Fast marketing and multi- channel engagement Customer sentiment analysis Transaction data Demographics Purchasing history Trends Effective customer engagement Decision services management Risk and revenue management Risk and compliance management Recommendation engine
  • 137. Genomics and precision medicine Clinical and claims data GPU image processing IoT device analytics Social analytics Faster innovation for drug development Improved outcomes and increased revenue Diagnostics leveraging machine learning Predictive analytics transforms quality of care Improved patient communications and feedback FAST-Q BAM SAM VCF Expression HL7/CCD 837 Pharmacy Registry EMR Readings Time series Event data Social media Adverse events Unstructured Single cell sequencing Biomarker, genetic, variant and population analytics ADAM and HAIL on Databricks Claims data warehouse Readmission predictions Efficacy and comparative analytics Prescription adherence Market access analysis Graphic intensive workloads Deep learning using Tensor Flow Pattern recognition Aggregation of streaming events Predictive maintenance Anomaly detection Real-time patient feedback via topic modelling Analytics across publication data MRI X-RAY CT Ultrasound DNA sequences Real world analytics Image deep learning Sensor data Social data listening
  • 138. Content personalization Customer churn prevention Recommendation engine Predictive analytics Sentiment analysis Faster innovation for customer experience Improved consumer outcomes and increased revenue Enhance user experience with machine learning Predictive analytics transforms growth Improved consumer engagement with machine learning Customer profiles Viewing history Online activity Content sources Channels Customer profiles Online activity Content distribution Services data Transactions Subscriptions Demographics Credit data Content metadata Ratings Comments Social media activity Personalized viewing and engagement experience Click-path optimization Next best content analysis Improved real time ad targeting Quality of service and operational efficiency Market basket analysis Customer behavior analysis Click-through analysis Ad effectiveness Content monetization Fraud detection Information-as-a-service High value user engagement Predict audience interests Network performance and optimization Pricing predictions Nielsen ratings and projections Mobile spatial analytics Demand-elasticity Social network analysis Promotion events time-series analysis Multi-channel marketing attribution Consumption logs Clickstream and devices Marketing campaign responses Personalized recommendations Effective customer retention Information optimization Inventory allocation Consumer engagement analysis
  • 139. Next best and personalized offers Store design and ergonomics Data-driven stock, inventory, ordering Assortment optimization Real-time pricing optimization Faster innovation for customer experience Improved consumer outcomes and increased revenue Omni-channel shopping experience with machine learning Predictive analytics transforms growth Improved consumer engagement with machine learning Customer profiles Shopping history Online activity Social network analysis Shopping history Online activity Floor plans App data Demographics Buyer perception Consumer research Market/competitive analysis Historical sales data Price scheduling Segment level price changes Customer 360/consumer personalization Right product, promotion, at right time Multi-channel promotion Path to purchase In-store experience Workforce and manpower optimization Predict inventory positions and distribution Fraud detection Market basket analysis Economic modelling Optimization for foot traffic, Online interactions Flat and declining categories Demand-elasticity Personal pricing schemes Promotion events Multi-channel engagement Demand plans Forecasts Sales history Trends Local events/weather patterns Recommendation engine Effective customer engagement Inventory optimization Inventory allocation Consumer engagement
  • 140. Customer value analytics Next best and personalized offers Risk and fraud management Sales and campaign optimization Sentiment analysis Faster innovation for customer growth Improved outcomes and increased revenue Risk management with machine learning Predictive analytics transforms growth Improved customer engagement with machine learning Customer profiles Online history Transaction data Loyalty Customer segmentation CRM data Credit data Market data CRM Merchant records Products Services Marketing data Social media Online history Customer service data Customer 360, segmentation aggregation and attribution Audience modelling/index report Reduce customer churn Insights for new products Historical bid opportunity as a service Right product, promotion, at right time Real time ad bidding platform Personalized ad targeting Ad performance reporting Real-time anomaly detection Fraud prevention Customer spend and risk analysis Data relationship maps Optimizing return on ad spend and ad placement Multi-channel promotion Ideal customer traits Optimized ad placement Opinion mining/social media analysis Deeper customer insights Customer loyalty programs Shopping cart analysis Transaction data Demographics Purchasing history Trends Effective customer engagement Recommendation engine Risk and fraud analysis Campaign reporting analytics Brand promotion and customer experience
  • 141. Digital oil field/ oil production Industrial IoT Supply-chain optimization Safety and security Sales and marketing analytics Faster innovation for revenue growth Improved outcomes and increased revenue Optimizing supply- chain with machine learning Predictive analytics transforms safety and security Improved customer engagement with machine learning Field data Asset data Demographics Production data Sensor stream data UAVs images Inventory data Production data Sensor stream data Transport Retail data Grid production data Refinery tuning parameters Clickstream data Products Services Market data Competitive data Demographics Production optimization Integrate exploration and seismic data Minimize lease operating expenses Decline curve analysis Pipeline monitoring Preventive maintenance Smart grids and microgrids Grid operations, field service Asset performance as a service Trade monitoring, optimization Retail mobile applications Vendor management - construction, transportation, truck and delivery optimization Real-time anomaly detection Predictive analytics Industrial safety Environment health and safety Fast marketing and multi-channel engagement Develop new products and monitor acceptance of rates Predictive energy trading Deep customer insights Transaction data Demographics Purchasing history Trends Upstream optimization, maximize well life Grid operations, asset inventory optimization Supply-chain optimization Risk optimization Recommendations engine
  • 142. Intrusion detection and predictive analytics Security intelligence Fraud detection and prevention Security compliance reporting Fine-grained data analytics security Prevent complex threats with machine learning Faster innovation for threat prevention Risk management with machine learning Transform security with improved visibility Limit malicious insiders to transform growth Firewall/network logs Apps Data access layers Firewall/network logs Network flows Authentications Firewall/network logs Web Applications Devices OS Files Tables Clusters Reports Dashboards Notebooks Prevention of DDoS attacks Threat classifications Data loss/anomaly detection in streaming Cybermetrics and changing use patterns Real-time data correlation Anomaly detection Security context, enrichment Offence scoring, prioritization Security orchestration e-Tailing Inventory monitoring Social media monitoring Phishing scams Piracy protection Ad-hoc/historic incident reports SOC/NOC dashboards Deep OS auditing Data loss detection in IoT User behavior analytics Role-based access controls Auditing and governance File integrity monitoring Row level and column level access permissions Firewall/network logs Web/app logs Social media content Security controls to leverage all data Actionable threat intelligence Risk and fraud analysis Compliance management Identity and access management for analytics