Azure Machine Learning Services provides an end-to-end, scalable platform for operationalizing machine learning models. It allows users to deploy models everywhere from containers and Kubernetes to SQL Datawarehouse and Cosmos DB. It also offers tools to boost data science productivity, increase experimentation, and automate model retraining. The platform seamlessly integrates with Azure services and is built to deploy models globally at scale with high availability and low latency.

1. Machine Learning and Deep Learning
Transform data into actionable insights
Date

2. © Microsoft Corporation
Azure data + AI
Azure active directory Visual studio toolsKMSExpressRoute OMS
Custom AI
Azure Databricks
Azure ML Services
Cloud Scale
Analytics
Azure SQL DW
Azure Databricks
Hybrid data
estate
Azure SQL Database
Azure DB for MySQL
& PostgreSQL
Modernization
on premises
SQL Server 2017
Cloud Native
Apps
Azure Cosmos DB
Seamlessly integrate with the Azure portfolio
Pre-Built AI
Cognitive Services
Conversational AI
Bot Services
AIData

3. © Microsoft Corporation
Advanced analytics is driving innovation across companies
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 Chatbots Financial forecasting Employee insights
Marketing Sales Service Finance Operations Workforce
Product recommendationProduct recommendation Predictive maintenancePredictive maintenance
Demand forecastingDemand forecasting

4. © Microsoft Corporation
ASOS delivers 15.4 million personalized
experiences with 33 orders per second
Distributed power generation increases
revenue by over €100 million
Leading to transformational changes
Hybrid solution predicts onboard water
usage, saving $200k/ship/year
The average size of a single
cart has decreased
Provide personalized digital
content to shoppers
Increase cart size
Unplanned downtime results
in cost overruns
Predict when maintenance
should be performed
Minimize downtime
Solar energy production
is inconsistent
Align energy supply
with the optimal markets
Maximize revenue
Product recommendation Predictive maintenance Demand forecasting
+

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But not everyone has seen success
85% of organizations have started data-driven initiatives,
but only 37%report success1
Why?
1:ESG, 2017
Technology demands
a power-intensive
infrastructure
Data is exponentially
increasing in volume
ML and AI models are
difficult to deploy to the
edge and applications
Security, latency, and
degradation concerns from
data transfer

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What approach should I take to
ensure success?

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Model and serveStore Prep and trainIngest
Microsoft has a recommended reference architecture
Batch data
Streaming data
Intelligent apps
Power BI
Azure analysis
services
Azure SQL data
warehouse
Azure
cosmos DB
Azure blob storageAzure data factory
Azure HDinsight
(Apache kafka)
Data science and AI
Data engineering
Azure databricks

8. © Microsoft Corporation
Prep and train
Collect and prepare data Train and evaluate model
A
B
C
Operationalize and manage

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Fast, easy, and collaborative Apache Spark™-based analytics platform
Introducing Azure Databricks
Seamlessly integrated with the Azure Portfolio
Built with your needs in mind
Role-based access controls
Effortless autoscaling
Live collaboration
Enterprise-grade SLAs
Best-in-class notebooks
Simple job scheduling
Increase productivity
Build on a secure, trusted cloud
Scale without limits

10. © Microsoft Corporation
Collect and prepare data

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Transform
Data preparation is no simple task
Understand
Incongruent data
+
Ineffective tools
=
Critical issues are missed
Flawed data is passed
downstream
Collect
and
prepare
Lack of automation
+
Lack of documentation
=
Manpower constraints
Misleading insights
Inability to replicate and revert
Collect
Legacy systems
+
Exponential data
=
Security
Latency
Degradation
Key blockers Resulting issues
1:TDWI, 2018
An overwhelming majority of technical decision
makers confirmed their teams spend 40-80%
of their time preparing data1

12. © Microsoft Corporation
Data collection and prep requirements
Ingest
TBD
TBD
TBD
Store
Capacity
Performance
Multiple tiers
Multiple object sizes
Parallel I/O
HDFS compatible
Encryption
Understand and transform
TBD
TBD
TBD
TBD
TBD

13. © Microsoft Corporation
Data collection and preparation architecture
Ingest
Azure Data
Factory
Store
Azure Blob
Storage
Understand and transform
Azure
Databricks

14. © Microsoft Corporation
Data collection and prep hero services
Azure Data Factory
Build scalable data flow with codeless UI,
or write with your language of choice
Access and ingest data with built-in
connectors
Schedule, run, and monitor your
pipelines with comprehensive control
Azure Blob Storage
Economically store any amount and
type of data
Get up and
running quickly
Leverage high bandwidth, high
throughput, and low-latency writes
Azure Databricks
Quickly visualize data and apply
transformations in an intuitive
notebook environment
Easily process structured
and unstructured data from
distributed sources
Securely collaborate across varied
roles and access levels with native
Azure Active Directory integration

15. © 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

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Train and evaluate

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Modeling requirements
Modeling
Scale for training
Choice of language
Choice of algorithms
Capture training history
Enable collaboration & review
Graphical tools
Support for deep learning

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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
Services
MS ML
Services

19. © Microsoft Corporation
For on-premises environments
Training and evaluating with structured data
Server
ML
SQL
Server
Scale out clusters
ML
HDFSMachine learning
ML
Server

20. © Microsoft Corporation
For on-premises environments
Train and evaluate with unstructured data
Machine learning
ML
Server
Scale out clusters
ML
HDFS

21. © 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

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Build in your environment of choice
Custom IDEs
Visual studio tools for AINotebooksR studio PyCharm

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Operationalize and manage

24. © Microsoft Corporation
Operationalize and manage requirements
Operationalize
Scale for scoring
Accessible
Available everywhere
Manage
TBD
TBD
TBD
A
B
C

25. © 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
Services
Data warehouse
SQL DW
Operational stores
Cosmos
DB

26. © Microsoft Corporation
Bring AI to everyone with an end-to-end, scalable, trusted platform
Azure Machine Learning Services
Boost your data science productivity
Increase your rate of experimentation
Deploy and manage your
models everywhere
Built with your needs in mind
GPU-enabled virtual machines
Low latency predictions at scale
Integration with popular Python IDEs
Role-based access controls
Model versioning
Automated model retraining
Seamlessly integrated with the Azure Portfolio

27. © Microsoft Corporation
Deployment environments
Get turnkey global distribution and replicate data to any
number of regions for fast, responsive access
Elastically scale throughput and storage worldwide and only
pay for what you need
Use a database built for low latency and massively
scalable applications
Azure Cosmos DB
Provision thousands of compute cores in under five minutes
and scale to petabyte in hours
Seamlessly create your analytics hub with native connectivity
to data integration and visualization services
Improve query performance with up to 128 concurrent
queries and scale to unlimited queries with Azure Analysis
Services integration
Azure SQL Datawarehouse
Applications
and BI tools
Expose your containers directly to the internet with an IP
address and fully qualified domain
Run containers without having to manage servers, virtual
machines, or higher-level services
Secure your applications with guaranteed hypervisor-level
isolation
Azure Container Instances
Use your favorite Kubernetes tools like Helm for service
deployments and Draft for app deployments
Quickly and efficiently scale to maximize your resource
utilization without having to take your applications offline
Create a Kubernetes cluster in minutes with just a few clicks
Azure Kubernetes Service
Containers

28. © 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

29. © Microsoft Corporation
2022202120202019
What are companies looking to do next?
Deep neural networks will
be a standard tool for
80% of data scientists1
More than 40% of data
science tasks will be automated1
20% of companies will
dedicate workers to
monitor neural networks1
90% of modern analytics
platforms will feature natural-
language generation1
30% of net new revenue
growth from industry-
specific solutions will
include AI1
1 in 5 workers engaged in
mostly nonroutine tasks will
rely on AI to do their jobs2
2018
1 “100 Data and Analytics Predictions Through 2021”, Gartner, 2017. 2 “Predicts 2018: AI and the Future of Work”, Gartner, 2018.

30. © Microsoft Corporation
Deep learning with Azure

31. © Microsoft Corporation
Train and evaluate AI and DL models
Azure ML Services
Scale out clusters
Azure
databricks
Notebooks
Azure
databricks
Scale out clusters
Batch AI
PyTorch
MS cognitive
toolkit
Keras
TensorFlow

32. © Microsoft Corporation
Fast, easy, and collaborative Apache Spark-based analytics platform
Azure Databricks for deep learning modeling
Tools InfrastructureFrameworks
Leverage powerful GPU-enabled VMs
pre-configured for deep neural
network training
Use HorovodEstimator via a native
runtime to enable build deep learning
models with a few lines of code
Full Python and Scala support for
transfer learning on images
Automatically store metadata in
Azure Database with geo-replication
for fault tolerance
Use built-in hyperparameter tuning
via Spark MLLib to quickly drive
model progress
Simultaneously collaborate within
notebooks environments to streamline
model development
Load images natively in Spark
DataFrames to automatically decode
them for manipulation at scale
Improve performance 10x-100x over
traditional Spark deployments with
an optimized environment
Seamlessly use TensorFlow, Microsoft
Cognitive Toolkit, Caffe2, Keras, and more

33. © Microsoft Corporation
Additional deep learning tools
Automatically scale virtual machine clusters with GPUs
or CPUs
Develop your models with long-running batch jobs,
iterative experimentation, and interactive training
Support for any deep learning or machine learning
framework
Designed and pre-configured specifically for GPU-
enabled instances
Fully integrated with Azure AI training service to provide
capacity for parallelized AI training at scale
Get started in seconds with example scripts and sample
data sets
Batch AI Deep Learning Virtual Machine

34. © Microsoft Corporation
Deploy AI models to devices on the edge
IoT EdgeModel managementMachine learning
Azure
Databricks
Azure ML
Services
Qualcomm
QCS603
Vision AI dev. kit
Pre trained
Solutions
IDEs
AI Frameworks

35. © Microsoft Corporation
Native
TensorFlow
Caffe
Deploy AI models to devices on the edge flow
Message passing
Co-located
Scoring file
Service or app
Trained/retrained
model
Additional
images
Original NW
e.g. MobileNet

36. © Microsoft Corporation
Machine learning and deep learning, when to use what?
Code first
(On-prem)
ML Server
On-prem
Hadoop
SQL Server
(cloud)
AML (Preview)
SQL Server Hadoop Azure Batch DSVM Spark
Visual tooling
(cloud)
AML Studio
Notebooks Jobs
Azure Databricks
Spark
What engine(s) do
you want to use?
Deployment target
Which experience
do you want?
Build with Spark
or other engines?
Python
TensorFlow, Keras, MS Cognitive
Toolkit, ONNX, Caffe2
Spark ML, SparkR,
SparklyR
TensorFlow, Keras, MS Cognitive Toolkit,
ONNX, Caffe2

37. © Microsoft Corporation
Microsoft’s comprehensive AI portfolio
Pre-built AI
AI on data
Custom AI
Deep learning frameworks
Train Deploy
Bot Framework Cognitive Services
CPU
Cosmos DB SQL DB SQL DW Data Lake
Azure Databricks Azure ML Services
Notebooks Other IDEs, CLI VS Tools for AI
Cognitive
Toolkit
TensorFlow ONNX PyTorch Others
CPU, GPU, FPGA
Spark DSVM
Batch
AI
IoT Edge
ACS, AKS, ACI

38. © Microsoft Corporation
Get started quickly with AI

39. © Microsoft Corporation
Additional tools to start today
Author models in a browser-based, drag-and-drop
environment
Use best-in-class algorithms, hundreds of built-in R and
Python packages, and support for custom code
Deploy your model into production as a web service
in minutes
Quickly spin up short-term experimentation and
evaluation with near-zero setup
Scale vertically and horizontally with on-demand,
elastic capacity
Get started in seconds with examples, templates,
and sample notebooks
Azure Machine Learning Studio Data Science Virtual Machine

40. © Microsoft Corporation
Azure ML Packages
Computer vision Forecasting
Quickly build and deploy models for…
Python pip-installable extensions for Azure Machine Learning
Text analytics

41. © Microsoft Corporation
Easily tap into functionality
with Developer AI

42. © Microsoft Corporation
Developer AI pattern
User inputChannelsBusiness process AIData
Text
Speech
Image
App services
LOB apps
Dynamics365
On-premises
IFTTT
Conversation AI
Pre-built AI
Custom AI
Azure Tools Security Logging Auditing Integration

43. © Microsoft Corporation
Developer AI reference architecture
Direct
Azure Cosmos DB
(Data for search)
Azure Cosmos DB
Bing Image Search API
Custom Vision Service
LUIS Bing Spell Check API
Text Analytics API
API Management
Azure Web Apps
Bot Connector
Direct Line
Azure Functions
(Communication Log)
Search Azure Storage Power BI
Bot Framework
Cognitive
services
Azure services
App

44. © Microsoft Corporation
Process natural language
Make intelligent recommendations
Develop in integrated environments
Leverage pre-trained models
Empower apps with add-free search
Enable apps to classify images
Infuse your
applications with intelligence
Build and manage
bots to interact with users
Enable apps to
harness web-scale search
Deploy models using Developer AI services
Azure Cosmos DB
(Data for search)
API Management
Azure Web Apps
Bot Connector
Direct Line
Azure Functions
Search Azure Storage Power BI
Bot Framework
Cognitive Services
Azure Cosmos DB (Communication Log)
App

45. © Microsoft Corporation
Leverage out-of-the-box AI tools and services
Create a seamless developer experience across desktop, cloud, or at the edge using Visual Studio AI Tools
Cognitive services
Map complex
information and data
Use pre-built AI services
to solve business problems
Allow your apps to
process natural language
Azure search
Reduce complexity with
a fully-managed service
Get up and
running quickly
Use artificial intelligence
to extract insights
Bot services
Infuse intelligence into your bot using
cognitive services
Speed development with a purpose-built
environment for bot creation
Integrate across multiple
channels to reach more customers

46. © Microsoft Corporation
Intelligent apps
Leverage AI to create the future
of business applications
Conversational agents
Transform your engagements with
customers and employees
Business processes
Transform critical business
processes with AI
Enterprise scenarios for AI
Of customer interactions
powered by AI bots by 2025
Applications to include
AI by the end of this year
75% 85%
Of enterprises using
AI by 202095%

47. © Microsoft Corporation
AI-enabled devices
Smart factory
Smart kitchen
Smart bath
Voice-activated speakers
Smart cameras
Drones
Smart kiosks

48. © Microsoft Corporation
Microsoft’s comprehensive AI portfolio
Pre-built AI
AI on data
Custom AI
Deep learning frameworks
Train Deploy
Bot Framework Cognitive Services
CPU
Cosmos DB SQL DB SQL DW Data Lake
Azure Databricks Azure ML Services
Notebooks Other IDEs, CLI VS Tools for AI
Cognitive
Toolkit
TensorFlow ONNX PyTorch Others
CPU, GPU, FPGA
Spark DSVM
Batch
AI
IoT Edge
ACS, AKS, ACI

49. © Microsoft Corporation
It’s all on

50. © Microsoft Corporation
Microsoft Azure
Microsoft Azure
Accelerate time to market
Productive
Optimize your infrastructure
Hybrid
Innovate at scale
Intelligent
Develop with confidence
Trusted

51. © Microsoft Corporation
Companies using Azure for ML and AI

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Rich partner network
System integrationData integration BI & analytics

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What do I do next?

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Learn Advanced Analytics in a few easy steps
Watch
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