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AI500 - MLOps Enablement with Red Hat OpenShift AI

Length 5 days
Price NZD 6725 exc GST

Course overview

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Why study this course

This five-day immersive class offers attendees an opportunity to experience and implement a successful MLOps adoption journey. While many AI or data science training programs focus on a particular framework or technology, this course covers how the best Open Source tools fit together in a full MLOps workflow. It blends continuous discovery, continuous training, and continuous delivery in a highly engaging experience simulating real-world machine learning scenarios.

To achieve the learning objectives, participants should include multiple roles from across the organisation. Data scientists, machine learning engineers, platform engineers, architects, and product owners will gain experience working beyond their traditional silos. The daily routine simulates a real-world delivery team, where cross-functional teams learn how collaboration breeds innovation. Armed with shared experiences and best practices, the team can apply what it has learned to help the organisation's culture and mission succeed in the pursuit of new projects and improved processes.

This course is based on Red Hat OpenShift AI, Red Hat OpenShift GitOps and Predictive AI

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What you’ll learn

As a result of attending this course, you will experience MLOps culture, explore MLOps practices, and apply your learning to bring a machine learning model into production.

After completing the course, you will be able to:

Apply MLOps principles to streamline the development and deployment of machine learning models.
Gain hands-on experience with modern tools and processes, covering the entire lifecycle from inner loop development to outer loop operations.
Enhance your skills in collaborative coding styles with pair and mob programming style.

Red Hat at Lumify Work

Red Hat is the leading provider of open source, enterprise IT solutions. Lumify Work is proud to offer clients the ability to attend over 40 official Red Hat training courses through a resell agreement with Red Hat. Maximise the return on your Red Hat technology investment with lab-intensive, instructor-led training run by Red Hat themselves.

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Who is the course for?

This experience demonstrates how individuals across different roles must learn to share, collaborate, and work toward a common goal to achieve positive outcomes and drive innovation.

It is especially valuable for:

MLOps Platform Users: Data scientists, data engineers, and application developers.
MLOps Platform Providers: Machine learning engineers, MLOps engineers, and platform engineers.
MLOps Platform Stakeholders: Architects and IT managers.

The scenario incorporates technical aspects of working with machine learning systems, offering practical insights into how these roles can align their efforts.

You will learn how to continuously deliver value to your customers by accelerating the deployment of new models to market. The instructors will share experiences and best practices learned from engaging directly with customers during Red Hat services engagements.

Course subjects

What is MLOps?
Brainstorm and explore what principles, practices, and cultural elements make up a MLOps model for ML model developments and deployments.

Inner Loop
Familiarise 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.

Training Pipelines
Transition to automating the previous steps for productionising our model training.

Outer Loop
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.

Monitoring
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.

Data Versioning
Enhance traceability by introducing versioning for our datasets as they change over time.

Advanced Deployments
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.

Feature Stores
Robust ways of dealing with data features and their changes, as well as making sure features are homogeneous between training and serving.

Security
Implement automated security guardrails to stay compliant with the organisations security practices and extend them to the models.

Prerequisites

Take the free assessment to gauge whether this offering is the best fit for your skills.
Containers, Kubernetes and Red Hat OpenShift Technical Overview (DO080) or Basic understanding of OpenShift/Kubernetes and containers is helpful
High level understanding of AI or Red Hat AI Foundations is beneficial

THIRD PARTY REGISTRATION

Lumify Work offers certification and training in Enterprise Linux, Ansible, JBoss, OpenShift, OpenStack, and more through our partnership with Red Hat. This arrangement requires Lumify Work to provide your details to Red Hat for course and/or exam registration purposes.

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.