Deployment of Machine Learning Models
Deployment of Machine Learning Models
4.72 (130 reviews)
11 479
students
10 hours
content
Aug 2022
last update
$59.99
regular price
What you will 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
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Related Topics
4833004
udemy ID
15/08/2022
course created date
22/08/2022
course indexed date
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