Dive into the dynamic world of MLOps and master the art of bringing Machine Learning models to life with this MLOps training course
With our expert-led MLOPS training program,
designed to equip you with cutting-edge skills in modern application deployment and management.
Benefit from our wealth of experience from countless customer projects:
With this MLOps training, you will experience a balanced mix of theory, live demonstrations and practical exercises.
Learn the essential principles and concepts of MLOps, including integration into the DevOps and Machine Learning domains.
Dive into the use of specialized cloud platforms, data versioning and feature stores, the creation and management of ML pipelines.
get to know advanced MLOps tools and techniques, as well as methods for continuous integration and delivery (CI/CD).
In this MLOps course, you will learn the essential principles and concepts of MLOps, including integration into the DevOps and Machine Learning domains, the use of specialized cloud platforms, data versioning and feature stores, the creation and management of ML pipelines, as well as the deployment and monitoring of models. You will also get to know advanced MLOps tools and techniques, as well as methods for continuous integration and delivery (CI/CD).
Practical Applications That We Will Cover in the MLOps training:
- 1Implementing and managing ML pipelines with Kubeflow and Apache Airflow.
- 2Using TensorFlow, DVC, Feast, and dbt in practical exercises to create and deploy ML models.
- 3Applying monitoring and metrics tools like Hydrosphere, Evidently.ai, and Grafana to track data and concept drift.
- 4Hands-on activities for model deployment with FastAPI, Seldon Core, and TensorFlow Serving.
- 5Executing CI/CD processes with tools such as Jenkins, Prefect, Airflow, Rundeck, Kedro, TFX, and Kubeflow.
After the MLOps training course, You Will Be Able To:
- 1Understand and apply the importance of MLOps.
- 2Effectively use machine learning concepts and environments.
- 3Implement data versioning and feature stores.
- 4Create and orchestrate ML pipelines.
- 5Use machine learning frameworks such as Scikit-Learn, Keras, and TensorFlow.
- 6Deploy and monitor models using advanced techniques and tools.
- 7Integrate CI/CD tools and platforms into ML workflows.
The MLOps training is NOT suitable for you if you …
Agenda
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Machine Learning Concepts
- Introduction
- Recap: Machine Learning Frameworks
- Machine Learning Environments
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Data Versioning
- Introduction to Versioning
- Overivew of Versioning Tools
- DVC
- Hands-on
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Feature Stores
- Use Cases and Options
- Deep-dive Feast
- Hand-on
-
Pipelines & Orchestration
- Data Pipelines Types and Characteristics
- Frameworks for ML Pipelines
- Hands-on
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Model Serving
- Saving and Loading Models
- Serving Overview
- Hands-on
Additional to Basic Training Agenda:
-
Feature Stores
- Deep-dive Hopswork
-
Pipelines & Orchestration
- Focus: Airflow
- Focus: Kubeflow
-
Model Serving
- Deployment Strategies
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Model Monitoring
- Transparancy and Explainability
- Pillars of monitoring
- Monitoring Frameworks
- Hands-on
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Your Topics
- Your ecosystem
- Your best practises
- further frameworks and hands-on according to your requirements
What other participants of the MLOps course say
This MLOps training exceeded my expectations. The course was well-structured and covered a wide range of topics, from data versioning to CI/CD processes. I found the practical exercises particularly useful, as they allowed me to apply what I learned in real-world scenarios. The use of advanced tools like Seldon Core and Grafana provided me with a deeper understanding of model deployment and monitoring. The course is perfect for anyone working in machine learning or DevOps, and it’s a great way to stay current with the latest MLOps practices. I’m now better equipped to handle ML workflows in my projects.
– Felix Ruge
I recently completed the MLOps training and it was an incredible experience! The course covers a broad range of topics, from the foundational principles of MLOps to advanced techniques for model deployment and monitoring. The practical exercises, especially those involving Kubeflow and Apache Airflow, were extremely hands-on and helped me gain confidence in implementing real-world solutions.
The instructors were knowledgeable and supportive, ensuring that complex concepts like CI/CD for machine learning were easily understood. I particularly appreciated the focus on cloud platforms and the integration of machine learning with DevOps, which are crucial skills in today’s tech industry.
By the end of the course, I felt well-equipped to handle various aspects of MLOps in my role as a data engineer. This training is a must for anyone looking to deepen their expertise in machine learning and operationalize their models effectively.
– Matteo Fontana
Your investment
- Combination of theory and practice with live demos and exercises to actively develop skills.
- Understand the application of DevOps principles in automating the machine learning lifecycle, from data preparation to model training.
- Learn to effectively handle the complexities and challenges of managing machine learning models in a production environment.
Get to know your MLOps training professionals
Marvin Taschenberger
Hudhaifa Ahmed
Senior Lead Big Data Developer & Berlin Territory Manager, Ultra Tendency
Matthias Baumann
Required hardware & infrastructure for your MLOps Training
- 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.
- This VM has a running Kubernetes cluster on which you can test and execute the training instructions.
- You can access the machine via a browser or SSH if you wish and the network restrictions allow it.
