Red Hat & Linux
MLOps Enablement with Red Hat OpenShift AI (AI500)
Take a predictive AI use case from first experiment to dependable production on Red Hat OpenShift AI.
5 daysIntermediate9 modulesAI500
About this course
Take a predictive AI use case from first experiment to dependable production on Red Hat OpenShift AI. Blending team culture with technique across five days, you will cover inner-loop experimentation, training pipelines, continuous training and automated deployment, monitoring models as data shifts, data versioning, feature stores, canary and blue-green rollouts, and automated security guardrails.
What you'll learn
- This course takes you an end to end journey of a Predictive Intelligent Application use case, from ideation to inner loop experimentation to production, while bringing different personas together to seamlessly collaborate on a single platform.
- This course blends cultural and technical practices into a unique, highly-engaging experience, packed with real-world applications. You will learn MLOps practices and how they build upon each other to improve team alignment and delivery efficiency.
- Most AI training focuses on a particular framework or technology, this course combines the best Open Source tools while giving you the experience of how they fit together to reliably and efficiently build, deploy and maintain AI models in production.
Curriculum
9 modules
- Brainstorm and explore what principles, practices, and cultural elements make up a MLOps model for ML model developments and deployments.
- Familiarize ourselves with the necessary tools for experimenting and building our model; we will create a workbench, explore the dataset, start tracking our experiments, and deploy our models.
- Transition to automating the previous steps for productionizing our model training.
- Introduction to MLOps: a set of practices that automate and simplify machine learning workflows and deployments.
- Here we will create our MLOps environment where the continuous training pipeline, automated deployment, and the supporting toolings will be running.
- Machine learning models can be influenced by various factors, including changes in data patterns, shifts in user behavior, and evolving external conditions. By implementing continuous monitoring, we will proactively identify these changes, assess their impact on model accuracy, and make necessary adjustments to maintain optimal performance.
- Enhance traceability by introducing versioning for our datasets as they change over time.
- Properly handle pre- and post-processing for data and predictions, explore autoscaling to handle loads, and introduce advanced deployment patterns like canary and blue-green deployments to ensure safe and seamless model rollouts.
- Robust ways of dealing with data features and their changes, as well as making sure features are homogeneous between training and serving.
- Implement automated security guardrails to stay compliant with the organizations security practices and extend them to the models.
Who it's for
Data scientists, ML engineers and platform teams who want to ship and maintain models together.
Before you start
- Basic understanding of OpenShift/Kubernetes and containers is helpful
- High level understanding of AI is beneficial
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