Deployment of Machine Learning Models

• 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

Who this course is for:

• Software Engineers
• Data Scientists
• Computer Vision Engineers
• Machine Learning Engineers