MLOps: professionalizing industrial AI workflows
The journey from a functional AI prototype to a reliable, production-ready system is one of the biggest hurdles in an industrial setting. While prototypes are often developed in isolated Jupyter notebooks, real-world deployment requires professional management of complex artifacts—from datasets and hyperparameters to training results. Without a systematic approach to versioning and configuration, “black boxes” emerge that are neither scalable nor maintainable in production. Those who miss the transition to MLOps (Machine Learning Operations) jeopardize the long-term stability of their AI applications.
The key to success lies in methodical engineering and the standardization of your workflows. Learn how to transition your AI projects from a prototyping environment to a professional infrastructure. Through a combination of theory and live coding sessions, we’ll teach you how to use open-source technologies for data versioning, experiment tracking, and automated pipelines. Make your AI development reproducible, traceable, and ready for production.
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- Professional development: you’ll learn the key difference between local prototyping and a standardized project structure suitable for industrial use.
- Maximum traceability: by using versioning methods for data, models, and code, you’ll ensure that your AI results remain reproducible and auditable at all times.
- Mastery of MLOps workflows: you’ll gain hands-on experience with tools such as GitLab Pipelines and Argo Workflows, as well as deployment on Kubernetes clusters.
- Direct practical application: through live coding sessions and discussions of real-world challenges from your company, you’ll be able to immediately apply the techniques you’ve learned to your own projects.
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This event is aimed at individuals who develop and deploy AI systems in production.
- Data scientists
- Machine learning engineers
- AI engineers
- Software engineers with a focus on AI
- DevOps and MLOps engineers
- IT architects in the AI field
- Tech leads on AI projects
- Developers and researchers in the field of machine learning
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- Format: webinar / workshop / training
- Duration: flexible (e.g., 1–2 days, adjustable)
- Delivery: online or in-person
- Practical component: high, with examples and specific application scenarios
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The format can be customized to suit your organization’s specific needs—for example, by:
- Incorporating your specific use cases
- Adapting to your industry or project type
- Delving deeper into specific methods or tools
