Machine learning enables computers to learn from data and experiences to act without being explicitly programmed. The goal of machine learning is to use example data or past experience to solve problems. There are different styles of machine learning algorithms such as supervised learning where the training data is labeled, and unsupervised learning where the training data is unlabeled. Machine learning problems can involve regression, classification, or clustering. The machine learning process involves preparing data, applying learning algorithms to create models, and deploying chosen models through applications and APIs.
Deep Credit Risk Ranking with LSTM with Kyle GroveDatabricks
Find out how Teradata and some of world’s largest financial institutions are innovating credit risk ranking with deep learning techniques and AnalyticOps. With the AnalyticOps framework, these organization have built models with increased accuracy to drive more profitable lending decisions, while being explainable to regulators.
Join us for a live session and learn about:
A machine learning ensemble including LSTM that achieves 90%+ accuracy at predicting delinquency/default, exceeding conventional credit risk methods by more than 20%.
A model management accelerator that is used to build and deploy the models in an integrated cloud platform, based on TensorFlow and Spark, and supports Keras, DeepLearning4J and SparkML models.
An innovative technique for model interpretability that obviates LIME’s need to generate synthetic examples.
This presentation will cover how all aspects of marketing have evolved over the years. How AI will shape the landscape of marketing in the years to come and why marketers need AI to assist them for their jobs. The future lies in working towards better customer experience and especially customer retention seems to be the key.
Marketers have to be on the lookout throughout - need to keep learning and keep a continuous tab on the customer’s pulse in order to deliver the best.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
How Artificial Intelligence & Machine Learning Are Transforming Modern MarketingCleverTap
Join Almitra Karnik, Head of Marketing, CleverTap, and Jessie Paul, CEO, Paul Writer share their insights on how AI and ML are fundamentally changing the way we approach marketing and how we can harness these changes to further our businesses.
Azure Machine Learning and ML on PremisesIvo Andreev
Machine Learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis.Microsoft offers Azure Machine Learning, while Amazon offers Amazon Machine Learning and Google offers the Google Prediction API - now depricated and replaced by Google ML engine based on TensorFlow. Software products such as MATLAB support traditional, non-cloud-based ML modeling.
Deep Credit Risk Ranking with LSTM with Kyle GroveDatabricks
Find out how Teradata and some of world’s largest financial institutions are innovating credit risk ranking with deep learning techniques and AnalyticOps. With the AnalyticOps framework, these organization have built models with increased accuracy to drive more profitable lending decisions, while being explainable to regulators.
Join us for a live session and learn about:
A machine learning ensemble including LSTM that achieves 90%+ accuracy at predicting delinquency/default, exceeding conventional credit risk methods by more than 20%.
A model management accelerator that is used to build and deploy the models in an integrated cloud platform, based on TensorFlow and Spark, and supports Keras, DeepLearning4J and SparkML models.
An innovative technique for model interpretability that obviates LIME’s need to generate synthetic examples.
This presentation will cover how all aspects of marketing have evolved over the years. How AI will shape the landscape of marketing in the years to come and why marketers need AI to assist them for their jobs. The future lies in working towards better customer experience and especially customer retention seems to be the key.
Marketers have to be on the lookout throughout - need to keep learning and keep a continuous tab on the customer’s pulse in order to deliver the best.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
How Artificial Intelligence & Machine Learning Are Transforming Modern MarketingCleverTap
Join Almitra Karnik, Head of Marketing, CleverTap, and Jessie Paul, CEO, Paul Writer share their insights on how AI and ML are fundamentally changing the way we approach marketing and how we can harness these changes to further our businesses.
Azure Machine Learning and ML on PremisesIvo Andreev
Machine Learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis.Microsoft offers Azure Machine Learning, while Amazon offers Amazon Machine Learning and Google offers the Google Prediction API - now depricated and replaced by Google ML engine based on TensorFlow. Software products such as MATLAB support traditional, non-cloud-based ML modeling.
High time to add machine learning to your information security stackMinhaz A V
Machine learning might never be the silver bullet for cybersecurity compared to areas where it is thriving. There will always be a person who tries to find issues in our systems and bypass them. They may even use it to assist the attacks.
But adding it to our general information security stack can surely help us be more prepared while defending. Different categories like regression, classification, clustering, recommendations & reinforcement learning can be leveraged to build efficient & faster monitoring, threat response, network traffic analysis and more.
Along with introduction to different aspects and how it can be leveraged - I'd like to present a case study on how ML/AI can be used in distinguishing between benign and Malicious traffic data by means of anomaly detection techniques with 100% True Positive Rate with live demo.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-parodi
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the "An Introduction to Machine Learning and How to Teach Machines to See" tutorial at the May 2019 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. Parodi then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. He also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
Machine Learning 101 - AWS Machine Learning Web DayAWS Germany
Vortrag "Machine Learning 101" von Michael Brückner beim AWS Machine Learning Web Day. Alle Videos und Präsentationen finden Sie hier: http://amzn.to/1XP3dz9
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Vortrag "Amazon Machine Learning im Einsatz: smartes Marketing " von Barbara Pogorzelska beim AWS Machine Learning Web Day. Alle Videos und Präsentationen finden Sie hier: http://amzn.to/1XP3dz9
Artificial Intelligence for Automating Data AnalysisManuel Martín
The requirements for analysing big volumes of data have increased over the last few decades. The process of selecting, cleaning, modelling and interpreting data is called the KDD process. The decision of how to approach each step in this process has often been made manually by experts. However, experts cannot be aware of all methods, nor is it feasible to try all of them. Researchers have proposed different approaches for automating, or at least advising, the stages of the KDD process. This talk will outline the different types of Intelligent Discovery Assistants as described in the work of Serban et al. “A survey of intelligent assistants for data analysis” and point out some future directions.
demo on own dataset (csv, dicom, image...etc) for each service how to apply, in practice ,data science with various Azure machine learning services vs when this service should be used in what scenario/datasets, demo azure services include -
Azure TSQL in database analytics
Azure Batch Service for multiple dataset + parallel model training
Azure BatchAI service for deep learning models with GPU acceleration
Azure databrick for deep learning + opencv (computer vision tasks) + sklearn (normal machine learning models)
Azure Data science virtual machine <-- a sandbox & shared environment for data science experiments
"Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand."
In this talk we will discuss how QuSandbox and the Model Analytics Studio can be used in the selection of machine learning models. We will also illustrate AutoML frameworks through demos and examples and show you how to get started
Use Machine learning to solve classification problems through building binary and multi-class classifiers.
Does your company face business-critical decisions that rely on dynamic transactional data? If you answered “yes,” you need to attend this free event featuring Microsoft analytics tools. We’ll focus on Azure Machine Learning capabilities and explore the following topics: - Introduction of two class classification problems.
- Classification Algorithms (Two Class Classification)
- Available algorithms in Azure ML.
- Real business problems that is solved using two class classification.
Deep learning goes beyond the traditional machine learning of big data and analytics. In this session, we will review the AWS offering, Amazon Machine Learning, and the AWS GPU-intensive family of servers that run native machine learning and deep-learning algorithms. We will also cover some basic deep-learning algorithms using open source software. Session sponsored by Day1 Solutions.
AWS re:Invent Deep Learning: Goin Beyond Machine Learning (BDT311)Chida Chidambaram
Deep Learning goes beyond the traditional machine learning of big data and analytics. In this session, we will review the AWS offering, Amazon Machine Learning, and the AWS GPU-intensive family of servers that run native machine learning and deep-learning algorithms. We will also cover some basic deep-learning algorithms using open source software.
High time to add machine learning to your information security stackMinhaz A V
Machine learning might never be the silver bullet for cybersecurity compared to areas where it is thriving. There will always be a person who tries to find issues in our systems and bypass them. They may even use it to assist the attacks.
But adding it to our general information security stack can surely help us be more prepared while defending. Different categories like regression, classification, clustering, recommendations & reinforcement learning can be leveraged to build efficient & faster monitoring, threat response, network traffic analysis and more.
Along with introduction to different aspects and how it can be leveraged - I'd like to present a case study on how ML/AI can be used in distinguishing between benign and Malicious traffic data by means of anomaly detection techniques with 100% True Positive Rate with live demo.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-parodi
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the "An Introduction to Machine Learning and How to Teach Machines to See" tutorial at the May 2019 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. Parodi then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. He also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
Machine Learning 101 - AWS Machine Learning Web DayAWS Germany
Vortrag "Machine Learning 101" von Michael Brückner beim AWS Machine Learning Web Day. Alle Videos und Präsentationen finden Sie hier: http://amzn.to/1XP3dz9
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Vortrag "Amazon Machine Learning im Einsatz: smartes Marketing " von Barbara Pogorzelska beim AWS Machine Learning Web Day. Alle Videos und Präsentationen finden Sie hier: http://amzn.to/1XP3dz9
Artificial Intelligence for Automating Data AnalysisManuel Martín
The requirements for analysing big volumes of data have increased over the last few decades. The process of selecting, cleaning, modelling and interpreting data is called the KDD process. The decision of how to approach each step in this process has often been made manually by experts. However, experts cannot be aware of all methods, nor is it feasible to try all of them. Researchers have proposed different approaches for automating, or at least advising, the stages of the KDD process. This talk will outline the different types of Intelligent Discovery Assistants as described in the work of Serban et al. “A survey of intelligent assistants for data analysis” and point out some future directions.
demo on own dataset (csv, dicom, image...etc) for each service how to apply, in practice ,data science with various Azure machine learning services vs when this service should be used in what scenario/datasets, demo azure services include -
Azure TSQL in database analytics
Azure Batch Service for multiple dataset + parallel model training
Azure BatchAI service for deep learning models with GPU acceleration
Azure databrick for deep learning + opencv (computer vision tasks) + sklearn (normal machine learning models)
Azure Data science virtual machine <-- a sandbox & shared environment for data science experiments
"Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand."
In this talk we will discuss how QuSandbox and the Model Analytics Studio can be used in the selection of machine learning models. We will also illustrate AutoML frameworks through demos and examples and show you how to get started
Use Machine learning to solve classification problems through building binary and multi-class classifiers.
Does your company face business-critical decisions that rely on dynamic transactional data? If you answered “yes,” you need to attend this free event featuring Microsoft analytics tools. We’ll focus on Azure Machine Learning capabilities and explore the following topics: - Introduction of two class classification problems.
- Classification Algorithms (Two Class Classification)
- Available algorithms in Azure ML.
- Real business problems that is solved using two class classification.
Deep learning goes beyond the traditional machine learning of big data and analytics. In this session, we will review the AWS offering, Amazon Machine Learning, and the AWS GPU-intensive family of servers that run native machine learning and deep-learning algorithms. We will also cover some basic deep-learning algorithms using open source software. Session sponsored by Day1 Solutions.
AWS re:Invent Deep Learning: Goin Beyond Machine Learning (BDT311)Chida Chidambaram
Deep Learning goes beyond the traditional machine learning of big data and analytics. In this session, we will review the AWS offering, Amazon Machine Learning, and the AWS GPU-intensive family of servers that run native machine learning and deep-learning algorithms. We will also cover some basic deep-learning algorithms using open source software.
Azure Machine Learning and its real-world use casesMichaela Murray
Is Machine Learning still a buzz word or can we easily put it to use to gain actionable insights from our data? In this demo-heavy, hands-on session, Ram will explain how to find hidden patterns in the data, find outliers and predict results using real-world datasets.
By the end of this session, you will be familiar with Machine Learning concepts (regression, classification, over-fitting, cross-validation etc.) and you should be able to build, deploy and consume Machine Learning models with ease.
About the Presenter
Based in Brisbane, Ram Katepally is a Microsoft Certified Solutions Expert and Data Analytics consultant at WARDY IT Solutions. As a consultant, Ram has significant experience working with companies of all sizes across Australia, empowering them to make data-driven business decisions. He’s passionate about Machine Learning, Internet of Things, Office 365 and Power BI. In his free time, he is an avid player of chess.
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
Slide about working of federated learning and the introduction of machine learning and how user privacy is preserved in future machine learning approach.
PREDICT THE FUTURE , MACHINE LEARNING & BIG DATADotNetCampus
Scopri come utilizzare Azure Machine Learning, un servizio cloud che consente alle aziende, università, centri di ricerca e sviluppatori di incorporare e sfrutturare nelle loro applicazioni funzionalità di apprendimento automatico e analisi predittiva su enormi set di dati. Tramite Azure ML Studio possiamo creare, testare, attuare e gestire soluzioni di analisi predittiva e apprendimento automatico nel cloud tramite un qualunque web browser. Durante la sessione si darà un saggio attraverso un esempio di analisi predittiva sul Flight Delay.
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
IBM i & digital transformation - Presentation & basic demo
IBM Watson Studio, IBM DSX Local w/ Open Source (Spark) & IBM Technology (OpenPower, CAPI, NVLINK)
In this session, we will take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.
At the end of this session, you will have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.
Similar to Borys Rybak “Azure Machine Learning Studio & Azure Workbench & R + Python” (20)
Artem Bykovets: Чому люди не стають раптово кросс-функціональними, хоча в нас...Lviv Startup Club
Artem Bykovets: Чому люди не стають раптово кросс-функціональними, хоча в нас Agile? (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
Natalia Renska & Roman Astafiev: Нарциси і психопати в організаціях. Як це вп...Lviv Startup Club
Natalia Renska & Roman Astafiev: Нарциси і психопати в організаціях. Як це впливає на розробку продуктів та реалізацію інноваційних рішень (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
Igor Protsenko: Difference between outsourcing and product companies for prod...Lviv Startup Club
Igor Protsenko: Difference between outsourcing and product companies for product managers and related challenges (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
Kseniya Leshchenko: Shared development support service model as the way to ma...Lviv Startup Club
Kseniya Leshchenko: Shared development support service model as the way to make small projects with small budgets profitable for the company (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
Anna Kompanets: Проблеми впровадження проєктів, про які б ви ніколи не подума...Lviv Startup Club
Anna Kompanets: Проблеми впровадження проєктів, про які б ви ніколи не подумали (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
Anton Hlazkov: Впровадження змін – це процес чи проєкт? Чому важливо розуміти...Lviv Startup Club
Anton Hlazkov: Впровадження змін – це процес чи проєкт? Чому важливо розуміти різницю і як це впливає на результат (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
Yana Bort: Ритм організації. Чи можливо синхронізувати великий ентерпрайз за ...Lviv Startup Club
Yana Bort: Ритм організації. Чи можливо синхронізувати великий ентерпрайз за допомогою Agile практик? (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
15. “The goal of machine learning is to
program computers
to use example data or past
experience to solve a given problem.
16. Machine Learning in a Nutshell
Machine
learning
algorithm
Model
Application
Data
Contains
patterns
Finds
patterns
Recognizes
patterns
Provides new data to
see if it matches
known patterns
17. Finding Patterns: A Simple Example
Name
$2,600.45
$2,294.58
$1,003.30
$8,488.32
Amount Fraudulent
Smith
Potter
Peters
Adams
No
Yes
Yes
No
What’s the pattern for
fraudulent
transactions?
18. Finding Patterns: Another Example
$2,600.45
$2,294.58
$1,003.30
$8,488.32
Name Amount Fraudulent
Smith
Potter
Peters
Adams
No
Yes
Yes
No
Where
Issued
Where
Used Age
$200.12
$3,250.11
$8,156.20
$7,475.11
Pali
Jones
Hanford
Marx
USA
RUS
USA
FRA
AUS
USA
USA
UK
22
29
25
64
58
43
27
32
No
No
Yes
No
USA
USA
RUS
USA
JAP
RUS
UK
GER
$540.00
$7,475.11
Norse
Edson
USA
USA
27
20
No
Yes
ITA
RUS
What’s the
pattern for
fraudulent
transactions?
20. Terminology
Training data
The prepared data used to
create a model
Creating a model is called
training a model
Supervised learning
The value you want to
predict is in the training
data
The data is labeled
Unsupervised learning
The value you want to predict is
not in the training data
The data is unlabeled
The most common
approach
25. Too complex:
(When you can’t code it….)
NLP
H-W recognition
CV
Too specialized:
(When you have to adapt...)
Amazon
Netflix
Predictive typing
Too much:
(When you can’t scale it….)
Spam detection
Fraud detection
Healthcare
Too robotic:
(When you can’t track it….)
AI gaming
Robot control
28. Collaborative
Filtering:
Not relying on machine analyzable content
Similarity alg's: k-NN & Pearson
Explicit vs. Implicit data collection
• Explicit: rating movies after watch
• Implicit: how often you watch movies,
how often you have watched season in
period of time.
Used in: Facebook, MySpace, LinkedIn,
Twitter
Challenges: Cold start, scalability, sparsity
Algorithms: matrix factorization
Content-Based
Filtering:
History: Information Retrieval
Algorithms for abstracting: tf-idf
Algorithms: Bayesian classifiers, cluster analysis,
decision trees, articial neural networks
Scenarios: Netflix
32. Azure ML Studio
Allows running
“experiments”
Candidate
Model
Raw
Dat
a
Raw
Dat
a
Prepared
Data
Apply pre-
processing
to data
Deploy
chosen
model
Apply learning
algorithm
to data
Chosen
Model
ML Studio
Preprocessing
Modules
Machine
Learning
Algorithms
Data
Preprocessing
Modules
Azure
ML
API
33. Azure ML Data Preprocessing
Example modules
Candidate
Model
Raw
Dat
a
Raw
Dat
a
Prepared
Data
Apply pre-
processing
to data
Deploy
chosen
model
Apply learning
algorithm
to data
Chosen
Model
Preprocessing
Modules
Machine
Learning
Algorithms
Data
Preprocessing
Modules
Azure
ML
API
Clean Missing Data
Removes or fills in missing
values in a dataset
Example: Replace each
missing value with the mean
of the other values in this
column
Select Columns in Dataset
Creates a view of a dataset
that includes or excludes
specific columns
Example: Delete a column
whose data is highly
correlated with data in
another column
Partition and Sample
Divides or extracts a subset of a
dataset
Example: Select a specific
number of rows from the data
There are dozens more
data preprocessing modules
34. Azure ML Learning
Example algorithms
Candidate
Model
Raw
Dat
a
Raw
Dat
a
Prepared
Data
Apply pre-
processing
to data
Deploy
chosen
model
Apply learning
algorithm
to data
Chosen
Model
Preprocessing
Modules
Machine
Learning
Algorithms
Data
Preprocessing
Modules
Azure
ML
API
Regression
Linear regression
Ordinal regression
Bayesian linear regression
Neural network regression
Decision forest regression
Boosted decision tree
regression
Classification
Two-class neural network
Two-class decision forest
Multiclass neural network
Multiclass decision forest
Multiclass decision jungle
Clustering
K-means
Azure ML is designed
for data scientists
35.
36.
37.
38. Microsoft Azure
Azure ML API
Chosen
Model
Azure ML API
Deploying and using a model
1) Deploy chosen model
ML Studio 2) Call model with
values for features the
model requires
3) Get back value
predicted by the model
using those features
Application
Candidate
Model
Raw
Dat
a
Raw
Dat
a
Prepared
Data
Apply pre-
processing
to data
Deploy
chosen
model
Apply learning
algorithm
to data
Chosen
Model
Preprocessing
Modules
Machine
Learning
Algorithms
Data
Preprocessing
Modules
Azure
ML
API
42. Scenario
Predicting equipment failure
MICROSOFTAZURE
Azure ML
Model
Azure IoT
Hub
Streaming
Data
ONPREMISES
INTERNETCONNECTED
Maintenance
StaffDevices
Azure Stream Analytics,
HDInsight Storm,
Spark Streaming
Notification
Application
43.
44. The Reality of Machine Learning
No model is an island
Azure ML is commonly used
with other Azure technologies
For ingesting data
For storing data
For displaying data
More …
A complete solution often
contains many different parts
Which can make the story
complex for customers
Solution: Cortana Intelligence
Suite
A group of related Azure data
technologies for analytics and
intelligence
46. Cortana Intelligence Gallery
Allows access to ML APIs, e.g.,
Face API, Translator API, etc.
Offers example models
for many industries, e.g.,
retail, healthcare, etc.
48. Business Leaders
Want solutions to
business problems
Data Scientists
Want powerful, easy-
to-use tools
Software Developers
Want to create better
applications
Who can be interested in ML?
52. • Choosing what
question to ask is
the most important
part of the process
• Ask yourself: Do
you have the right
data to answer this
question?
• Ask yourself: Do
you know how
you’ll measure
success?
? $ %
+
Thanks for invitation, excitement about city, venue and confernce
Tell what will be said.
--> Machine Learning - Why? How? What?
--> Machine Learning in action - Short Demo (basic algorithm, Python or R)
--> Azure Machine Learning - Why? How? What?
--> Azure Machine Learning Studio - showcase (demo1, demo2, demo3, summary)
--> Azure Machine Learning Workbench - showcase (demo1, demo2, summary)
Introduction of myself (background, profession )
Introduction of myself (personal)
-> Ask Questions for better communication and knowledge about the public.
How many of you already tried Machine Learning? (basics scenario, complex one)
Which one basics?
So the rest complex?
So how many of you want to find out something new about Machine Learning and maybe Learn about it?
Customers can build Artificial Intelligence (AI) applications that intelligently:
sense,
process,
and act on information (augmenting human capabilities, increasing speed and efficiency,
and helping organizations achieve more)
Literka 'A' = 1 byte
Strona z literami = 1000x Literka 'A' = 1 kilobyte
Ksiazka ze stronami = 1000x strona z literkami (500 obustronnie) = 1 megabyte
Human genome 1000x 1 megabyte (you can encode a whole human being on) = 1 gigabyte
Putting every second of human life for 80 years in HD video = 1 terabyte
1.4 miliarda akrów (1 acre = ~4k m^2), kazdy akr ma ~500 drzew, wiec w sumie 700 miliardow drzew w lasach tropikalnych Amazonii- jesli wiec wytnie sie wszystkie drzewa i zamieni na kartki papieru, umiesci na kazdej kartce po literce 'A' x1000 to bedzie sie miec od 1 do 2 petabyte danych.
[zdjecie ziemii] jesli pomnozymy to x1000 to bedziemy miec exabyte.
~Introduction to Machine Learning, 2nd Edition, MIT Press
Too complex: When you can't code it.
(NLP, hand-writing recognition, Computer Vision…)
Too much: When you can't scale it.
(e.g. Spam & fraud detection, healthcare)
Too specialized: When you have to adapt/personalize.
(Amazon, Netflix, predictive typing)
Autonomous: When you can't track it.
(AI gaming, robot control)
Netflix Example
Movies already watched
Same actor
Same director
Period of time (watched whole season per night or whole series)
Basic Recommenders Systems
- Collaborative Filtering Scanario
- Content-Based Filtering Scenario
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users or the films being identified except by numbers assigned for the contest.
The competition was held by Netflix, an online DVD-rental and video streaming service, and was open to anyone who is neither connected with Netflix (current and former employees, agents, close relatives of Netflix employees, etc.) nor a resident of certain blocked countries (such as Cuba or North Korea).[1] On September 21, 2009, the grand prize of US$1,000,000 was given to the BellKor's Pragmatic Chaos team which bested Netflix's own algorithm for predicting ratings by 10.06%.[2]
What Music Do I Want to Listen to?
last.fm:
Collaborative filtering
User's past behavior
Cold start
PANDORA:
Content-based filtering
Characteristics of items, i.e. properties of songs, artists,…
Little information needed at the beginning
TFIDFIn - information retrieval, tf–idf or TFIDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.
Azure ML Workbench
Azure for Students
Showing Portal (AZURE)
Showing DSVM + DLVM
Showing demo Workbench
Why Deep Learning Virtual Machine?
Increasingly, deep learning algorithms / deep neural networks are becoming one of the popular methods employed in many machine learning problems. They are especially good at machine cognition tasks like image, text, audio/video understanding often approaching human cognitive levels in some specific domains with advanced deep neural network architectures and access to large set of data to train models. Deep learning requires large amount of computational power to train models with these large datasets. With the cloud and availability of Graphical Processing Units (GPUs), it is becoming possible to build sophisticated deep neural architectures and train them on a large data set on powerful computing infrastructure on the cloud. The Data Science Virtual Machine has provided a rich set of tools and samples for data preparation, machine learning, and deep learning. But one of the challenges faced by the users is to discover the tools and samples for specific scenarios like deep learning easily and also more easily provision GPU-based VM instances. This Deep Learning Virtual Machine (DLVM) addresses these challenges.
What is Deep Learning Virtual Machine?
The Deep Learning Virtual Machine is a specially configured variant of the Data Science Virtual Machine (DSVM) to make it more straightforward to use GPU-based VM instances for training deep learning models. It is supported on Windows 2016 and the Ubuntu Data Science Virtual Machine. It shares the same core VM images (and hence all the rich toolset) as the DSVM but is configured to make deep learning easier. We also provide end-to-end samples for image and text understanding, that are broadly applicable to many real life AI scenarios. The deep learning virtual machine also tries to make the rich set of tools and samples on the DSVM more easily discoverable by surfacing a catalog of the tools and samples on the virtual machine. In terms of the tooling, the Deep Learning Virtual Machine provides several popular deep learning frameworks, tools to acquire and pre-process image, textual data. For a comprehensive list of tools, you can refer to the Data Science Virtual Machine Overview Page.
Simply put, customer churn occurs when customers or subscribers stop doing business with a company or service. Also known as customer attrition, customer churn is a critical metric because it is much less expensive to retain existing customers than it is to acquire new customers
Challenges for ML
-Skilled Data Scientists
Like unicorns, PhD in maths, collaborative, cs, statistics background, business approach understanding.
-Infrastructure
Cheaper to store data, you can scale it – cloud
-Time
Running algorithms at classes so long (leaving laptop by night) - with cloud you can increment.
-Global scalable
How to scale those algorithms globaly
What Machine Learning Does?
Finds patterns in data
Then uses those patterns to predict the future
Examples:
Detecting credit card fraud
Determining whether a customer is likely to switch to a competitor
Deciding when to do preventive maintenance on a factory robot
Why is Machine Learning So Hot?
Doing machine learning well requires:
Lots of data
Lots of compute power
Effective machine learning algorithms
All of those things are now more available than ever
What Do I Need for Data Science?
Very well defined question / business objective
"which of my machines / is this part of my machine going to fail within the next two weeks?" //what is the probability of failing in next two weeks?
You need to have data - Relevant data
Data that is relevant to the business objective
That data need to be accurate - Accurate data
Connected data
Large data
It is not necessarily Big Data, but large is good.
What Do I Need for Data Science?
Very well defined question / business objective
"which of my machines / is this part of my machine going to fail within the next two weeks?" //what is the probability of failing in next two weeks?
You need to have data - Relevant data
Data that is relevant to the business objective
That data need to be accurate - Accurate data
Connected data
Large data
It is not necessarily Big Data, but large is good.
Machine learning has its own jargon
Such as training data, features, and supervised learning
Machine learning problems often fall into three areas:
Regression
Classification
Clustering
Many different types of machine learning algorithms are used today
Using these algorithms to create good models requires effort
Azure Machine Learning
Machine learning isn’t hard to understand
Although it can be hard to do well
Azure ML is a strong offering
Especially for data scientists
Machine learning can probably help your customers
Watch Stephen Hawking speak about the future of AI at Web Summit.