MLOps - in just one day

You will learn:  

• Automating Machine Learning Workflows: Understand the application of DevOps principles in automating the machine learning lifecycle, from data preparation to model training. 

• End-to-End Lifecycle Management: Master the skills to manage the entire machine learning lifecycle, including deployment and monitoring of models. 

• Navigating Production Challenges: Learn to effectively handle the complexities and challenges of managing machine learning models in a production environment. 

• Utilizing Tracking and Management Tools: Gain proficiency in using common tools for tracking machine learning experiments and managing artifacts such as datasets and models. 

• Building and Deploy ML Pipelines: Explore the role and learn how to construct and deploy robust machine learning pipelines. 

Prerequisites:  

• Basic knowledge of machine learning and Python programming: Participants should have a basic understanding of machine learning concepts and principles as well as a good grasp of Python programming and development lifecycles. This skill is essential for understanding code examples and for hands-on sessions. 

• The intention and motivation to lift your MLOps cycle to the next level   

• Happy to share feedback with us - so that we can learn as well   

Hardware & Infrastructure 

You will need a PC or Mac with a web browser and MS Teams. During the training, we will provide you with a virtual machine with the required local dependencies, services, and root access. That VM will have a running Kubernetes cluster where you can test and run the training instructions. You can access the machine via a browser or SSH if you’d like, and your network's restrictions allow you to.