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The Data Science Process - Do we need it and how to apply?

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Machine learning is not black magic but a discipline that involves statistics, data science, analysis and hard work. From searching patterns and data preparation through applying and optimizing algorithms to obtaining usable predictions, one would need background and appropriate tools.
But do we need it, when there is already available AI as a service solution out there? Do we need to try hard with artificial neural networks? And if we decide to do so, what tools would be a safe bet?
In this session we will go through real world examples, mention key tools from Microsoft and open source world to do data science and machine learning and most importantly - we will provide a workflow and some best practices.

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The Data Science Process - Do we need it and how to apply?

  1. 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. 2. Thanks to our Sponsors: Global Sponsor: Venue Sponsor:
  3. 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
  4. 4. AGENDA Major Tools The Purpose of ML AI as a Service Iterative ML Process Takeways Demo
  5. 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, • (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. 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. 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. 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. 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. 10. The purpose of ML modelling is: • Generate predictions • Understand true relations
  11. 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. 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. 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. 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. 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) • Apply custom ML intelligence to conversational natural language Custom Decision (experimental) • Learn behavioural patterns of users
  16. 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. 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) configure-auto-train
  18. 18. Iterative ML Process
  19. 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. 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. 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
  22. 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. 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. 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. 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. 26. Appropriate Algorithms are Determined by Data
  27. 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. 28. False AlarmsFalse Alarms have serious impact • Degraded confidence in the system • Loss of revenue • Loss of brand image
  29. 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. 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. 31. Takeaways • Team Data Science Process o • ML in the Microsoft World o • Python for AI o • Data Science Blog o • Starter Books o Free e-books download link:
  32. 32. Azure ML StudioAzure ML Workbench