The document discusses the integration of AI into Microsoft's technology platforms to enhance productivity and drive innovation. It outlines the AI DevOps lifecycle, emphasizing model development, deployment, and monitoring within an intelligent cloud and edge framework. Key components include automated machine learning capabilities, popular frameworks for deep learning, and tools to streamline data science processes.

1. “Ours is not an industry that respects tradition;
it only respects innovation.” – Satya Nadella
DevOps for AI

2. Computer on every desk
in every home

3. To empower every person and every organization
on the planet to achieve more
Build technology platforms and productivity services for an intelligent cloud and intelligent edge infused with AI

4. AI across Microsoft
Microsoft 365
AI for Good

7. AutomatedML capabilityin Azure Machine Learning

8. AI in Office 365
PowerPoint Designer Dictation & read aloud in Office Ideas in Excel

9. Automated AI in Power BI

10. Machine Learning on Azure
Domain Specific Pretrained Models
To reduce time to market
Azure
Databricks
Machine
Learning VMs
Popular Frameworks
To build machine learning and deep learning solutions
TensorFlowPytorch Onnx
Azure Machine
Learning
LanguageSpeech
…
SearchVision
Productive Services
To empower data science and development teams
Powerful Infrastructure
To accelerate deep learning
Scikit-Learn
PyCharm Jupyter
Familiar Data Science tools
To simplify model development
Visual Studio Code Command line
CPU GPU FPGA
From the Intelligent Cloud to the Intelligent Edge

12. AI DevOps lifecycle
Experiment
Data Acquisition
Business Understanding
Initial Modeling
Develop
Modeling
Operate
Continuous Delivery
Data Feedback Loop
System + Model Monitoring
Experiment
+ Testing
Continuous Integration
Continuous Deployment

13. • Profile and validate
• Data (changes to shape / profile)
• Model in isolation (offline A/B)
• Model + app (functional testing)
• Gate promotion of the model on validation
• Assess
• Functional behavior
• Performance characteristics

14. • Processes and procedures to make models
reproducible
• Code
• Data
• Config
• Capture
• Featurization code (w/ tests)
• Training pipeline
• Dependencies
• Training data persistence
• Evidence chain
• Model config
• Training job info
• Sample data
• Data profile

15. • Safe and efficient deployment & feedback
• Simplify consumption - code-generation, API
specifications / interfaces
• Support a variety of inferencing targets
• Cloud Services
• Mobile / Embedded Applications
• Edge Devices
• Convert / quantize / optimize models for
target platform
• Control the rollout of your models (with A/B)
• Feed telemetry back into your system on
service health and model behavior

16. • Track model versions & metadata with a centralized
model registry
• Leverage containers to capture runtime
dependencies for inference
• Leverage an orchestrator like Kubernetes to provide
scalable inference
• Capture model telemetry – health, performance,
inputs / outputs
• Encapsulate each step in the lifecycle to enable
CI/CD and DevOps
• Automatically optimize models to take advantage of
hardware acceleration