Get started with MLOps

MLOps for Data Scientist

Intermediate

1 day

On-site training

Unlock the full potential of your machine learning models by embracing MLOps. This training is designed specifically for data scientists looking to streamline the deployment, monitoring, and scaling of machine learning (ML) models. You will benefit from our expertise in data science and our many years of experience in internal and customer projects. This course is an ideal introduction to MLOps, offering insights into current concepts, methods, and tools. The technical content is enriched with an ongoing showcase to demonstrate real-world applications.

How participants can apply their learning

Automate ML model deployment pipelines in production environments.
Ensure efficient collaboration between data scientists, engineers, and operations teams.
Monitor, update, and manage ML models over time.
Implement industry-standard tools for scalable and reliable ML operations.

What you will learn

Fundamentals of MLOps

Understand the basics and why MLOps is essential for scaling machine learning in production.

Getting Started with MLOps

Guided step-by-step in setting up MLOps

MLOps Values and Principles

Core principles for successful MLOps implementations.

Tools in MLOps

Common tools for MLOps (e.g., MLflow, Kubeflow, Docker, Kubernetes) and how to integrate them into your workflows.

Your benefits

Our training is customised to your specific context and held in-house at your premises to guarantee that you get the best possible outcome.

Streamline your machine learning workflows with automation and scalability.

Enhance collaboration between data science, engineering, and operations teams.

Deploy and manage models more efficiently, reducing time to market.

Gain hands-on experience with industry-standard MLOps tools and practices.

Who should join?

Data Scientist: Who wants to transition from ML development to deploying and managing models in production environments.
ML Engineer: Interested in automating model lifecycle management with best MLOps practices.
AI Consultant: Who wants to add value by streamlining AI workflows for clients.
DevOps Engineer: Looking to expand their knowledge by integrating ML models into existing CI/CD pipelines.

We have successfully run the program for Finance, Accounting, Marketing, Communications, Sales, Research & Development and Supply Chain departments. Further, we were able to apply this format to the Pharmaceutical, Healthcare, Finance, Chemical, Logistics, Retail and Manufacturing industry.

How we do it

The course combines theory with practice, including lectures, discussions, and interactive elements:

Interactive Workshops: Participants will work through real-world case studies.
Live Coding Sessions: Practical implementations of MLOps workflows using industry-leading tools.
Q&A and Mentorship: Ongoing support during the training to answer your specific MLOps questions.

Detailed Content

Fundamentals of MLOps
Design and development
Deploying Machine Learning in Production
Managing ML in Production Environments
Using the MLOps Framework for Regulation and Compliance
Testing ML pipelines