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Vitalii Bondarenko and Eugene Berko "Cloud AI Platform as an accelerator of enterprise digital transformation"


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Vitalii Bondarenko and Eugene Berko "Cloud AI Platform as an accelerator of enterprise digital transformation"

  1. 1. 2018 Cloud AI Platform as an accelerator of enterprise digital transformation Vitaliy Bondarenko Eugene Berko
  2. 2. AGENDA 1. Azure AI / ML services 2. Data Processing Architecture Approaches 3. Data Science Platform 4. Lessons learned
  3. 3. Office in the USA New York Office in the UK London, UK Office in Eastern Europe Rzeszow, Poland Offices in Ukraine Headquarters Lviv Delivery centres Lviv Kyiv Ivano-Frankivsk Ternopil a Top 100 Global Outsourcing Company largest IT companies in Ukraine TOP 10 years experience of delivering solutions 27 professionals 1200+ ELEKS FACT SHEET Among the
  4. 4. SPEAKERS Vitaliy Bondarenko Head of Enterprise Cloud Solutions Office 20+ years of experience; conference speaker; ELEKS competency manager and community lead; Solution Architect for Big Data, Fast Data and AI projects Eugene Berko Data Architect 7+ years of experience in BI / Big Data / DB / DWH. For the last couple of years has been developing data solutions of various nature focusing mostly on high-load systems and performing enterprise integration
  5. 5. Azure AI / ML services
  6. 6. Offering Overview Tools and technologies: ● Data Science Virtual Machines (both Windows and Linux based) ● Azure Machine Learning Studio ● Azure Machine Learning Service (preview) ● Azure Batch AI (preview) Deployment options: ● Azure Machine Learning web service (only for models built using Azure Machine Learning Studio ) ● Python web service in a Docker container ● Apache Spark in Azure HDInsight ● Machine Learning Server (previously Microsoft R Server) ● As T-SQL functions in Microsoft SQL Server 2
  7. 7. Azure Cognitive Services Vision APIs • Computer Vision • Custom Vision Service (Preview) • Content Moderator • Face API • Emotion API (Preview) • Video Indexer Speech APIs Language APIs Search APIs Knowledge APIs • Speech Service (Preview) • Custom Speech Service (Preview) • Bing Speech API • Translator Speech • Speaker Recognition API (Preview) • Bing Spell Check • Language Understanding LUIS • Linguistic Analysis (Preview) • Text Analytics • Translator Text • Web Language Model (Preview) • Bing News Search • Bing Video Search • Bing Web Search • Bing Autosuggest • Bing Custom Search • Bing Entity Search • Bing Image Search • Bing Visual Search • Custom Decision Service (Preview) • QnA Maker
  8. 8. Sample implementations
  9. 9. Stream Analytics Using Native Azure Services Key points ● Real-time image processing ● Face / objects recognition ● Real-time dashboards for alerting ● Dashboards for retrospective analysis and stats Components ● Computer Vision and Face API ● Event Hub for image injection ● Stream Analytics for communication with Cognitive Services ● Blob Storage for storing images ● Cosmos DB for storing model output ● Power BI for dashboarding / reporting
  10. 10. Batch Analytics Using Native Azure Services Key points ● Future sales prediction based on years of data ● Diverse visualization options Components ● Data Lake Storage as scalable storage ● Azure Functions to transform data into format more suitable for machine learning ● SQL Data Warehouse ● Analysis Services to provide single semantic model and in-memory cashing ● Azure SQL Database ● Data Factory
  11. 11. Lambda Architecture with Azure Databricks Key points ● Both streaming and batch analytics of online orders ● Anomaly detection Components ● HDInsight Kafka for stream injection and real-time processing ● Azure Databricks as Apache Spark–based analytics service with machine learning capabilities ● Data Factory for extracting data and injection into main storage
  12. 12. ELEKS Data Science Platform
  13. 13. AI Solutions Challenges: • How to feed data to the AI model? • How to control access to output of the models that can contain critical business information? • Hot to react to increased data velocity? • How to deploy models to production environment? • How to retrain models in an efficient way without data scientists? • How to measure actual effectiveness of the model on actual data? • How to integrate with existing enterprise infrastructure? • How to make instant decisions according to AI predictions?
  14. 14. Data Science Platform Target Customers Enterprises in the state of digital transformation which are building strategies of AI and Big Data implementations. Key points in solution vision: • Real-time analytics and lightning-fast response to incoming data no matter how big it is • Removing the pain of model management and deployment from developers • Easy model scaling for both scoring and training
  15. 15. Trained Models Registry and Deployment Registry of trained models ● Metadata for Models ● Versions ● Unified UI for Deployment Deployment ● Create pod on Kubernetes ● Flask for Python ● POJO unified model ● Schema Registry Monitoring ● Scoring Statistics ● Automatic Validation
  16. 16. Visualisation for Anomaly Detection and Real-Time Scoring Capabilities ● Machine Learning models training on historical data ● Real-time models scoring ● Integration with Enterprise applications ● Real-time Data Visualisation Real-time Machine Learning ● Shopping Behaviour Analysis ● Logs Anomaly Detection ● Fraud Prediction ● Product Recommendation ● Campaign Recommendation ● Demand Prediction ● Route Optimization ● Customer Segmentation Benefits ● Expert controlled model training ● Validation jobs for all models ● UI for models deployment and monitoring ● Latency in 1 second
  17. 17. Deployment and Scalability with Docker and Kubernetes Capabilities ● Deployment to Cloud and On-Premises ● Docker containerization ● Kubernetes Cluster ● Automated continuous integration ● Unified cluster for all Platforms ● UI for Cluster Management Benefits of Kubernetes ● Scalable on level of VMs ● Integration with Enterprise Network ● Enterprise Level Security ● REST API ● Platform for Model deployments
  18. 18. Demo
  19. 19. Stream analytics using HDInsight clusters
  20. 20. Lessons Learned Key points ● Azure is a mature environment for Data Engineering, Machine Learning, and Platform Building ● HDInsight is a powerful Hadoop-based System for real-time and batch data processing ● Cosmos DB is quite sophisticated data base and needs more panels for configurations ● AKS is an excellent tool for microservices and better than native Kubernetes ● PowerBI is very helpful for real-time analytics ● Databricks is a power Data Platform and has bright future.
  21. 21. Inspired by Technology. Driven by Value. Have a question? Write to Find us at