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Machine Learning & Data Analytics / Machine Learning
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MLOps in practice: Deployment and integration of machine learning models

Techniques, tools and end-to-end strategies for productive machine learning pipelines
Online
2 days
German
Download PDF
€ 1.290,-
plus VAT.
€ 1.535,10
incl. VAT.
Booking number
36447
Venue
Online
3 dates
€ 1.290,-
plus VAT.
€ 1.535,10
incl. VAT.
Booking number
36447
Venue
Online
3 dates
Become a certified
Machine Learning Engineer
This course is part of the certified Master Class "Machine Learning Engineer". If you book the entire Master Class, you save over 15 percent compared to booking this individual module.
To the Master Class
In-house training
In-house training for your Employees only - exclusive and effective.
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After the proof-of-concept phase at the latest, machine learning projects require the right approach, control and infrastructure in order to be efficient and scalable in the long term. Similar to DevOps in the field of software development, the MLOps (Machine Learning Operations) model offers a helpful guide with best practices, methods and tools aimed at optimally managing and mapping the life cycle of machine learning models. In this two-day training , you will get to know all MLOps stages in detail - from data versioning to monitoring - and learn how to deploy, monitor and keep your pipelines productive with helpful tools such as Github, MLflow, DVC and many more. As additional excursions, the training covers MLOps in the cloud (e.g. Azure ML Studio, Amazon Sagemaker or Google Vertex AI) and gives an outlook on LLMOps, addressing the special requirements of generative AI models. In addition to knowledge about concepts and methods, the training also offers numerous practical exercises to directly apply the technologies and tools learned.
Contents

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
Your benefit

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.

trainer
Tim Sabsch
Nils Uhrberg
Anke Koke
This is how you learn in this course

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.

Final examination
Recommended for

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.

Start dates and details

Form of learning

Learning form

3.6.2025
Online
Few places available
Implementation secured
Online
Few places available
Implementation secured
15.12.2025
Online
Places free
Implementation secured
Online
Places free
Implementation secured
19.3.2026
Online
Places free
Implementation secured
Online
Places free
Implementation secured
Do you have questions about training?
Call us on +49 761 595 33900 or write to us at service@haufe-akademie.de or use the contact form.