MLOps in practice: Deployment and integration of machine learning models
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Machine Learning Engineer
1. MLOps - what it is and why you can't do without it
- When things get serious with machine learning projects
- Domain knowledge and challenges
- The MLOps cycle at a glance
- MLOps is more than DevOps
- The MLOps maturity levels
2. data versioning and experiment tracking
- Basics and advantages of code and data versioning
- Introduction to DVC
- Exercise: Data versioning with DVC
- Exercise: Experiment Tracking with DVC
3. data pipeline orchestration
- Basics and advantages of data pipelines
- Introduction to Dagster
- Exercise: Asset jobs with Dagster
- Exercise: Op jobs with Dagster
4. experiment tracking
- Parameters, metrics and artifacts
- Basics and advantages of experiment tracking
- Experiment tracking with MLflow
- Exercise: Experiment tracking with MLflow
- Exercise: Model management with MLflow
5. CI/CD for machine learning
- Introduction to CI/CD, differentiation of CI/CD for code
- What can we test?
- Variants of CI/CD for ML products
- Showcase: Github Actions and CML
6. deployment and serving
- Basics of machine learning deployment
- Differentiation between batch inference and live inference
- Data preprocessing in deployment
- Introduction to Open Neural Network Exchange (ONNX)
- Exercise: FastAPI and ONNX
7. monitoring
- Monitoring of ML models
- Data, metrics, KPIs
- Application metrics
- Showcase: Monitoring with evidently.ai
8 MLOps in the cloud
- When are cloud solutions recommended?
- Classification Amazon Sagemaker, Azure ML Studio and Google Vertex AI
- Showcase: Model training with Azure ML Studio
9. machine learning platforms
- How and when do I scale the development of my ML teams?
- What is a feature store?
10. excursus: LLMOps
- What distinguishes LLMOps from MLOps?
- Showcase: companyGPT
Technical requirements
In this training , you will learn how to use the methods and tools in many practical exercises. You will carry these out yourself locally on your own computer. To make this possible, we ask you to install and test the Docker Desktop and Git applications before the course starts - please note that you will need the appropriate permissions on your computer. After installation, you can download and set up the packages provided by the trainers. You will receive detailed instructions about two weeks before the course date.
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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