Free Serverless ML Course with Python

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Ready to learn how to use Python to build a free serverless service? This free course has you covered – Serverless ML Course

You shouldn’t need to be a Kubernetes or cloud computing expert to build an end-to-end service that makes smart decisions using an ML model. Serverless ML makes it easy to build a system that uses ML models to make predictions. You don’t need to install, upgrade or operate systems. You only need to be able to write Python programs that can be programmed to run as pipelines. The features and models produced by your pipelines are managed by a serverless feature store/model registry. We’ll also show you how to create a user interface for your prediction service by writing Python and some HTML.

Learning outcomes:

  • Learn how to develop and operate AI-enabled (prediction) services on a serverless infrastructure
  • Develop and run serverless feature pipelines
  • Deploy features and models on a serverless infrastructure
  • Train models and run batch/inference pipelines
  • Develop a serverless user interface for your prediction service
  • Learn the fundamentals of MLOps: release management, testing, data validation and operations
  • Develop and run a real-time serverless machine learning system

Course content

  • Pandas and ML pipelines in Python. Write your first serverless application.
  • The feature store for machine learning. Feature engineering for a credit card fraud serverless application.
  • Training pipelines and inference pipelines
  • Bring a prediction service to life with a user interface (Gradio, Github Pages, Streamlit)
  • Automated testing and release management of features and models
  • Real-time serverless machine learning systems. Project display.

Who is the target audience?

You have received training in machine learning (ML) and you know how to program in Python. You want to take the next step beyond training models on static datasets in notebooks. You want to be able to build a prediction service around your model. Maybe you work in a company and want to demonstrate the value of your models to stakeholders in their own language. You may want to embed ML into an existing application or system.

Why is this course different?

You don’t need any operational experience beyond using GitHub and writing Python code. You will learn the essentials of MLOps: versioning artifacts, testing artifacts, validating artifacts, and monitoring and upgrading running systems. You will work with raw and live data – you will need to design features into pipelines. You will learn how to select, extract, calculate and transform entities.

Will this course cost me money?

No. You will become a machine learning engineer without having to pay to run your serverless pipelines or manage your features/models/UI. We will be using Github Actions and Hopsworks which both have generous time-limited free tiers.

Register now for the Serveless ML course

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