Machine Learning Engineering
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Machine Learning Engineering
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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
- 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.
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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