One of the biggest challenges which customers face is how to productionize machine learning for enterprises. Once the Data scientist, Data Engineers, Business analyst, Machine learning engineer have successfully built their Machine Learning Models, they need model management a system that manages and orchestrates the entire lifecycle of machine learning models.

2. Productionizing ML with Apache
Spark, MLflow and ONNX from the
ground to cloud using SQL Server
Daniel Coelho
Senior Program Manager – Microsoft SQL Server

3. Agenda
Introducing SQL Server BDC
Understand the possibilities with SQL Server
Big Data Clusters 2019
End-to-end applied use case
Let’s apply a common pattern to a scenario using a
comprehensive demo
Conclusion and moving forward
Key learnings and opportunities

4. Feedback
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5. SQL Server Big Data Clusters
Hybrid Transactional Processing
Platform (HTAP)

6. Data Volumes and Data Sources at Scale
▪ Transactional Data
▪ Historical Tracking
▪ Increased Metrics
▪ Multi-Department and Organization
Data
▪ Data Marts and Warehouses
▪ Data Lakes
▪ Business Intelligence Systems
▪ Compute Optimization
▪ Data Virtualization
▪ Scale Innovations
▪ Storage Optimizations and Cost
Decreases
▪ Flexible Platform Choices
▪ Data Interface Enhancements
Technology EnhancementsMore Sources

7. SQL Server Big Data Clusters

8. Applied Use Case

9. HTAP
HTAP
BI
Data and Scale AI Scoring
IT
Data Scientist
AI Models
BDC Scenario – Real State

10. Conclusion and moving forward

11. Conclusion
▪ Leverages what you know and need
▪ SQL Server T-SQL with scale out compute and storage
▪ Active Directory Integration
▪ Governance and Compliance features most organizations requires
▪ On-Premises, Private and Hybrid Cloud
▪ Differentiate with Big Data and Machine Learning
▪ Apache Spark and HDFS are de-facto differentiators for Big Data Engineering and Data Science at scale.
▪ Either attach remotely or move data. Flexible to any scenario.
▪ Use either Spark or Machine Learning Services as platforms for AI augmentation.
▪ Operationalize on modern standards
▪ Kubernetes and DevOps at its core
▪ Mlflow and App Deployment possibilities, but far from an exhaustive option set
SQL Server Big Data Clusters is a modern, comprehensive and flexible platform to provide end-to-end
analytics to every organization

12. Moving Forward
▪ Developer edition is free and easy to deploy on AKS or local k8s cluster
▪ Get started: https://aka.ms/sql-bdc-docs
▪ Join our community: https://aka.ms/sql-bdc-community
▪ MSSQL-Spark Connector: https://aka.ms/mssql-spark-connector
▪ Scenarios and Samples: https://aka.ms/sql-bdc-samples
▪ HealthCare: https://aka.ms/sql-bdc-length-of-stay
▪ Retail: https://aka.ms/sql-bdc-retail-ai
▪ Forecasting: https://github.com/microsoft/forecasting
▪ Recommendation: https://github.com/microsoft/recommenders
There are a lot of resources out there! Here are the main takeaways.

13. Feedback
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