The document outlines the Global AI Bootcamp featuring a presentation on machine learning (ML) and its application within Microsoft Azure's services and tools. Key highlights include the Azure ML integrated platform, various machine learning frameworks, challenges of data preparation, and the importance of iterative processes in ML modeling. The document also discusses artificial intelligence as a service (AIaaS) and the anticipated growth of this market, providing insights into how businesses can leverage these technologies for data science and predictive modeling.

1. DECEMBER 15
GLOBAL AI BOOTCAMP IS POWERED BY:
The Data Science Process in ML
How to Apply It and When do We Need It?

2. Thanks to our Sponsors:
Global Sponsor:
Venue Sponsor:

3. About me
• Software Architect @
o 16+ years professional experience
• Microsoft Azure MVP
• External Expert Horizon 2020
• External Expert Eurostars-Eureka, InnoFund Denmark
• Business Interests
o Web Development, SOA, Integration
o IoT, Machine Learning, Computer Intelligence
o Security & Performance Optimization
• Contact
ivelin.andreev@icb.bg
www.linkedin.com/in/ivelin
www.slideshare.net/ivoandreev

4. AGENDA
Major Tools
The Purpose of ML
AI as a Service
Iterative ML Process
Takeways
Demo

5. Machine Learning and Microsoft
• Azure ML integrated, end-to-end data science and advanced analytics
• Microsoft ML related services/tools
• Highlights
o Built on open source technologies (Jupyter Notebook, Spark, Python, Docker)
o Execute experiments in isolated environments and GPU-enabled VMs
DEPRECATED MAINTAINED AND IMPROVED
• (Azure ML Workbench) • Azure ML Studio • Visual Studio Code Tools for AI
• (Azure ML Experimentation Service) • Data Science VM • Microsoft Cognitive Services, LUIS.ai
• (Azure ML Model Management Service) • Azure Databricks • Libraries for Apache Spark (MMLSpark)
Now called • Cognitive Toolkit (CNTK) • ML Services for SQL Server (R, Python)
“Machine Learning Service” (preview) • Azure Batch AI Training

6. Azure ML Workbench
Desktop application (Windows, macOS) with
• Built-in Jupyter Notebook services and Git integration
• End-to-end process support
o Model development and experimentation (Python)
o Powerful inspectors for data analysis
o Data transformations by example
o Model history and deployment
• Easy to use
and resource hungry 
* Replaced in Sept 24 2018 release to make way for an improved architecture
(ref. to Azure ML SDK for Python or Azure Databricks for big datasets)

7. Azure ML Studio
• Visual workspace to build, test and deploy ML solutions
• Highlights
o X-browser drag and drop, no programming
o Rich set of modules
o Fits beginners and advanced users
o Unlimited extensibility (R Script, Python Script)
o Enterprise grade cloud service (SLA 99.95%)
o ML REST web services consumption
o Jupyter Notebook
o Azure AI Gallery (9000+ samples)
• At what price?
o Free plan available (10GB storage, 2 web services, 1000 requests/month)
o $10 seat/month + $1 experiment/hour

8. Azure Data Science VM
• Pre-configured cloud environment for AI & Data Science
• Highlights
o Fully operational environment
o 50+ tools DEV, ML, BigData, Data management
o Windows and Linux (Ubuntu/CentOS)
o Updated every few months
o On-demand elastic capacity
o GPU optimized VMs for deep learning
o Up to 4x GPUs NV K80 or V100
o Up to 128 vCPU, up to 6’144 GiB RAM
• At what price?
o From $11.76/month to $14’314/month

9. • Cloud-based environment to develop, train, test, deploy,
manage, and track ML models
• Highlights
• Model management
• Distributed deep learning
• Version control and reproducibility
• Hybrid deployment (Local, Cloud, Edge)
• Automated ML (data prep, algorithm, parameters)
• Latest open source technologies (TensorFlow, PyTorch, Jupyter, Docker)
• Scale up or out with large GPU-enabled clusters in the cloud
• At what price?
• From $23.51/month to $29’143.94/month
Azure ML Service (preview)

10. The purpose of ML modelling is:
• Generate predictions
• Understand true relations

11. Machine Learning Challenges
• Asking the right questions
• Typically 1 Model = 1 Question
• Requires training data
o Real-world data is messy (wrong or missing data)
o Feature engineering transforms to predictive features
o Feature extraction ( i.e. IP Address -> population density)
o Feature selection for informative features
• Overfitting model
o “Kicks ass” while training ,
o fails badly on real predictions
• Model validation
o “Sense” how well model works on new data

12. Users’ expectations:
• Engaging experience
• Effortless interaction
• High performance
• Relevant content
Businesses aim:
• Provide high value
• Faster and at low cost
o Data science talent
o Powerful infrastructure
o Continuous improvement
The developer role is to
bridge the gap:

13. Artificial Intelligence as a Service (AIaaS)
Def: Artificial intelligence off the shelf
• Bots and NLP – commands and guidance
• Cognitive APIs – speech, vision, translation, knowledge
• ML frameworks – build own model w/o infrastructure (i.e. Azure ML Service)
• Fully managed ML – templates, deployment, drag-drop (i.e. Azure ML Studio)
• Innovation w/o upfront costs and expertise
• Usability – easy learning curve
• Scalability – start PoC, grow big
• Flexi cost – know what you pay for
• Share data with vendors
• Data regulations (i.e. GDPR)
• Reduced transparency
• Breaking changes

14. AIaaS market expected to
grow from $1.5Bil (2018) to
10.9Bil (2023)
(ResearchAndMarket Apr’ 2018)
1 year ago this was not as
achievable as it is now.

15. Some Key Azure AIaaS
Computer Vision
• Advanced algorithms for processing images for information
Face API
• Detect and analyze facial attributes
Custom Vision API
• Build, deploy, improve custom image classifiers (on tags)
LUIS.ai
• Apply custom ML intelligence
to conversational natural language
Custom Decision (experimental)
• Learn behavioural patterns of users

16. • Appealing
o 64% believe they are working in this century’s most “sexiest” job
• In demand
o 90% contacted at least once a month with job offer
o 50% - weekly, 30% - several times/week, 35% have <2y experience
• The dark side…
o All models are wrong, some are useful
o 80% time is data preparation
o Real life, not academic problems
o Non-linear hypothesis testing
o No full automation
• No one cares how you do it
The Data Scientist Job

17. Automated ML (AML)
AML is a recommender system for ML pipelines to achieve accuracy with less time
• Problem: Complexity scales faster that time available
• Highlights
o Designed to not look at customer data
o Only each pipeline result is sent to automated ML service
o Data pre-processing, algorithm experimentation, hyperparameters tunings
• How it Works
o Select algorithm: classification(11), regression(9), forecasting(9)
o Specify labeled data source and format (Numpy array, Pandas dataframe)
o Configure target for training (local, remote VM, AML Compute)
o Set AML configuration
automl_classifier = AutoMLConfig(
task='classification',
primary_metric='AUC_weighted',
max_time_sec=12000,
iterations=50, X=F_Train,
y=F_Label,
n_cross_validations=2)
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-
configure-auto-train

18. Iterative ML Process

19. Data Understanding (Titanic Dataset)
• Mosaic plot
o Categorical distribution
o Visualizes the relation between X and Y
o Strong relation = Y-splits are far apart
o Conclusion: Women have higher survival rate
• Box plot
o Continuous distribution of numeric var
o IQR = middle 50%
o Identify outliers [Q1-1.5 IQR; Q3+1.5 IQR]
o Conclusion: High fares have higher survival rate
• Scatter plot
o How much a variable determines another
o Conclusion: Infants and men 25-45 y
have higher survival rate

20. • Make features usable
o Numerical
o Categorical (i.e. week day)
o PCA dimensionality reduction
o Dummy variables
• Handle missing data
• Normalize data
o Standard range of numerical scale (i.e. from [-1000;1000] -> [0;1], [-1;1])
o Value range influence the importance of the feature compared to other
Data Preprocessing

21. Feature Engineering, Feature Extraction
Increase predictive power by creating features on raw data
• Features closely related to target (predict default –> debt / balance ratio)
• Easier interpretation (Date to Year/Month/Day/Hour)
• Lag features to “look back” before the date (1, 2,… N days ago)
• Categorical features - identify discrete features
• Rolling aggregates
• smoothening over time window
• Check Azure team data science process
https://docs.microsoft.com/en-gb/azure/machine-learning/team-data-science-process/create-features

22. Note: All information is encoded in the digital media
• Images
o Step 1: Colour statistics, EXIF metadata, edges, shapes
o Step 2: Extract knowledge in fixed set of numeric characteristics
• Text
o Step 1:
• Bagging, N-grams, term frequency, topic modelling, stemming
• Named entity recognition (i.e. Wikipedia)
o Step 2: Extract knowledge in fixed set of numeric characteristics
Digital Media Feature Engineering

23. Feature Selection - select the
most predictive features
For many ML problems, having
a lot of data is a good thing;
but it can sometimes be a curse

24. Selecting Good Features
• Motivation
o Not only prediction but identification of predictive features
o Computational costs are related to number of features
o Limit external sensors and data sources
• Approach
o Trying all combinations of features? ( that would be infeasible)
• Methods
o Forward selection & Backward elimination
o Filter - Independent from the ML algorithm
o Embedded – Built-in search for predictive features in ML algorithm
o Wrapper – Measure feature usefulness while ML training

25. Tuning Model Parameters
• Model parameters control inner behaviour
o More sophisticated algorithm, more parameters
o i.e. Locally Deep SVM with kernel
o Kernel type, kernel coefficient
• How parameter tuning works?
1. Choose metric for evaluation (AUC - classification, R2-regression, etc.)
2. Select parameters for optimization
3. Define a grid as Cartesian product between arrays
4. For each combination, cross-validate on training set
5. Select the parameters for the best evaluation
Note: Expected improvement is 3%-8%

26. Appropriate Algorithms are
Determined by Data

27. Types of Algorithms
• Linear Algorithms
• Classification - classes separated by straight line
• Support Vector Machine – wide gap from line
• Regression – linear relation variables-label
• Non-Linear Algorithms
• Decision Trees and Jungles - divide space into regions
• Neural Networks – complex and irregular boundaries
• Special Algorithms
• Ordinal Regression – ranked values (i.e. race)
• Poisson Regression - discrete distribution (i.e. nr. of events)
• Bayesian – normal distribution of errors (bell curve)

28. False AlarmsFalse Alarms have serious impact
• Degraded confidence in the
system
• Loss of revenue
• Loss of brand image

29. Performance Metrics
• Regression model
o Root Mean Squared Error (RMSE)
o Coefficient of Determination, R2 ϵ [0;1]
• Multi-class classification model
o Confusion matrix
• Binary classification model
o Accuracy based on correct answers
o Area under ROC curve (AUC)
o Threshold
o Precision = TP / (TP + FP)
o Recall = TP / (TP + FN)
o Cost-Balanced (F1)

30. Handling Imbalanced Data
• Imbalanced: more examples of one class than others (0.001%)
• Errors are not the same
o Prediction of minority class (failures) is more important
o Asymmetric cost (false negative can cost more than false positive)
• Compromised performance of standard ML algorithms
o For 1% minority class, Accuracy of 99% does not mean useful model
o PR-curve is better for imbalanced data
• Oversampling
o SMOTE – allows better learning
o Generate examples combining features of target with features of neighbours

31. Takeaways
• Team Data Science Process
o https://azure.microsoft.com/en-gb/documentation/learning-paths/data-science-process/
• ML in the Microsoft World
o https://docs.microsoft.com/en-us/azure/machine-learning/
• Python for AI
o https://wiki.python.org/moin/PythonForArtificialIntelligence
• Data Science Blog
o https://data-flair.training/blogs/category/machine-learning/
• Starter Books
o Free e-books download link:
https://www.manning.com/books/exploring-data-science

32. Azure ML StudioAzure ML Workbench