1. MLOps - what it is and why you can't do without it
2. data versioning and experiment tracking
3. data pipeline orchestration
4. experiment tracking
5. CI/CD for machine learning
6. deployment and serving
7. monitoring
8 MLOps in the cloud
9. machine learning platforms
10. excursus: LLMOps
This training will provide you with in-depth knowledge of the concepts and methods of MLOps (Machine Learning Operations). You will get to know the basic concepts and tools and learn how to work practically with the most important tools (DVC, Dagster, MLflow, FastAPI, ONNX and many more).
You will gain valuable tools for designing, planning, implementing and maintaining scalable data and machine learning pipelines.
After completing this training, you will not only have sound theoretical knowledge in the operationalization of machine learning models, but also practical experience in the application of the methods and tools. You will be able to develop, customize, monitor and productively deploy your own machine learning pipelines. This will qualify you for advanced tasks in ML engineering and AI development.
The content of this training supports the obligation to provide evidence of the promotion of AI competence within the meaning of Art. 4 EU AI Regulation.
This training training is conducted in a group of a maximum of 12 participants using the Zoom video conferencing software.
The trainers are on hand to help you carry out the practical exercises - in the virtual classroom or individually in break-out sessions.
Once you have registered, you will find all the information, downloads and extra services for this training course in your online learning environment.
This training is aimed at anyone who wants to create, operate, monitor, expand and fine-tune machine learning models and applications.
Basic technical knowledge of machine learning models and algorithms is assumed. Previous mathematical and statistical knowledge is helpful, but not required.
This course is a valuable building block in the qualification as an MLOps Expert, Machine Learning Engineer, Data Scientist and Data Engineer.
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