Machine Learning Engineering

PT25471
Training Summary
Machine Learning is all the rage today. Most ML courses focus on building models. However, taking the ML models to production, involves quite a bit of extra work, as illustrated diagram below. This course will teach Machine Learning Engineering - the process of productionizing, monitoring and managing ML models. We will use a cloud environment (Google Cloud or Amazon Cloud or Microsoft Cloud) for our deployment.
Prerequisites
  • Some knowledge in Machine Learning or Deep Learning is highly recommended
You may take one of these courses: ‘Machine Learning in Python’, ‘Deep Learning’
  • Some basic knowledge of Python is highly recommended.
  • Our labs utilize Python language. But Python is a very easy language to learn. So even you don’t have previous exposure to Python, you will be able to complete the labs.
Duration
4 Days/Lecture & Lab
Audience
This course is designed for Data Scientists, DevOps, and Data Engineers.
Course Topics
  • ML Engineering overview
  • Overview of the AI capabilities of the Cloud ::Platform of choice
  • Storing large data in the cloud
  • Processing large data in the cloud using distributed tools
  • Training models at scale, using GPUs on the cloud
  • Deploying models as webservices
  • Logging and tracing of model runtime
  • Model metrics
  • Setting up alerts
  • A/B testing different models
  • Updating newer model versions

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