What you’ll learn
- Define and understand the different deployment scenarios, being it Edge or Server deployment
- Understand the constraints on each deployment scenario
- Be able to choose the scenario suitable to your practical case and put the proper system architecture for it
- Deploy ML models into Edge and Mobile devices using TLite tools
- Deploy ML models into Browsers using TFJS
- Define the different model serving qualities and understand their settings for production-level systems
- Define the landscape of model serving options and be able to choose the proper one based on the needed qualities
- Build a server model that uses Cloud APIs like TFHub, Torchhub or TF-API and customize it on custom data, or even build it from scratch
- Serve a model using Flask, Django or TFServing, using custom infrastructure or in the Cloud like AWS EC2 and using Docker containers
- Convert different models built in any framework to a common runtime format using ONNX
- Understand the full ML development cycle and phases
- Be able to define MLOps, model drift and monitoring
How to Enroll Deployment of Machine Learning Models course?
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