Microsoft DevOps for AI with GoDataDriven

Jul 6, 20190 likes323 views

Artificial Intelligence (AI) and machine learning (ML) technologies extend the capabilities of software applications that are now found throughout our daily life: digital assistants, facial recognition, photo captioning, banking services, and product recommendations. The difficult part about integrating AI or ML into an application is not the technology, or the math, or the science or the algorithms. The challenge is getting the model deployed into a production environment and keeping it operational and supportable. Software development teams know how to deliver business applications and cloud services. AI/ML teams know how to develop models that can transform a business. But when it comes to putting the two together to implement an application pipeline specific to AI/ML — to automate it and wrap it around good deployment practices — the process needs some effort to be successful.

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

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

AI across Microsoft
Microsoft 365
AI for Good

Microsoft DevOps for AI with GoDataDriven

AutomatedML capabilityin Azure Machine Learning

AI in Office 365
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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

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

• 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

• 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

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