Who is this for?

Our MLOps course is designed for experienced professionals who are already familiar with machine learning concepts and want to learn how to deploy, monitor, and scale machine learning models in production. 

Program goal - What you will take away from the course

The goal of this course is to provide you with a comprehensive understanding of MLOps and its ecosystem. By the end of the course, you will be able to:

Understand the challenges of managing machine learning models in production

Use common tools to track and manage machine learning experiments and artifacts

Use Kubeflow to build and deploy machine learning pipelines

Deploy machine learning models to production and monitor them 

Recap of Machine learning and its Frameworks 

Revisit the fundamentals of machine learning and its frameworks, refreshing your understanding of the concepts and tools that drive modern AI solutions. 

Managing Data Science Artifacts and features with DVC and Feast 

Discover the world of MLOps, the practice that streamlines machine learning lifecycles, and learn how to manage the development, deployment, and maintenance of AI applications. 

Building and deploying machine learning pipelines with Kubeflow 

Explore Data Version Control (DVC) and Feast, the tools that facilitate efficient management of data science artifacts and features, ensuring seamless collaboration and reproducibility. 

Job orchestration with Airflow  

Discover Kubeflow, the platform that simplifies the creation and deployment of machine learning pipelines, boosting your ability to manage complex AI workflows. 

Deploying ML artifacts to production 

Gain insights into the power of Apache Airflow for orchestrating and scheduling machine learning jobs, optimizing the execution of your AI workloads. 

Deploying ML artifacts to production

Learn the best practices and techniques for deploying machine learning artifacts to production, ensuring the seamless integration of AI solutions into your applications and systems.