From Notebook to Production: Hands-on MLOps Workshop

Learn how to build scalable, reproducible ML workflows using industry-standard open source tools. Whether you're working with traditional models or large language models - this hands-on workshop covers real code, real results, real deployments. (PowerPoint slides kept to an absolute minimum!)

• Platform Independent: Use open source tools only, avoid vendor lock-in
• Production Ready: Learn proven workflows that scale from your machine to enterprise clusters
• Hands-on Learning: Build and deploy scalable and reproducible ML pipelines yourself, not just theory
• Expert Guidance: Learn from prokube's MLOps engineers with years of experience

By the end of this workshop, you'll be able to:

• Build scalable ML training pipelines with Kubeflow Pipelines for reproducible data preprocessing and model training
• Track experiments systematically with MLflow for ML experiment tracking and model management
• Deploy trained models to production using KServe for high-performance, higly available and scalable model serving
• Set up model registry workflows and configure deployment automation

• Python Developers transitioning into ML engineering
• Data Scientists wanting to productionize their models
• ML Engineers seeking to master industry-standard tools
• Team Leads planning MLOps implementations

• Python experience (intermediate level)
• Basic understanding of containerization (Docker concepts)
• ML fundamentals (model training, evaluation basics)
• Laptop with webbrowser installed

• Kubernetes expertise (we'll cover the basics)
• Prior MLOps experience
• Specific ML framework knowledge
• Local development environment

*Our confidence guarantee - we're sure you'll find value that we'll refund your fee if you didn't feel you got your money's worth - no questions asked (only if you attendet the complete workshop).

Contact us at [email protected]

Info: 1) Introductions and networking; 2) Why MLOps matters: From notebook chaos to production success; 3) Overview of the open source MLOps ecosystem

Info: 1) Container & Kubernetes essentials for ML; 2) Introduction to Kubeflow architecture; 3) Setting up your development environment

Info: 1) Theory (30 min): Pipeline concepts, components, and workflows; 2) Hands-on (90 min): Launch your first (Kubeflow) notebook environment, build lightweight pipeline components, create your first ML pipeline, execute, debug and monitor pipeline runs

Info: Networking lunch with fellow participants and prokube team

Info: 1) Experiment Tracking: Organize and compare model experiments; 2) Model Registry: Manage model versions and deployments; 3) Hands-on: Integration with Kubeflow workflows

Info: 1) Theory (30 min): Production serving requirements and KServe architecture; 2) Hands-on (45 min): Deploy and test your trained models

Info: 1) Containerized components for complex workflows; 2) Hyperparameter tuning with Katib; 3) Multi-GPU multi-node training with Training Operator; 4) Custom transformers and serving logic; 5) Building your MLOps roadmap

Info: 1) Key takeaways and best practices; 2) Resources for continued learning; 3) prokube platform demonstration

Host or Publisher prokube.ai GmbH