Operationalize generative AI workflows and manage enterprise AI deployments using Red Hat AI technologies.

This course teaches how to deploy and manage generative AI workloads using Red Hat OpenShift AI, model serving, MLOps and GenAIOps practices, automation workflows and scalable AI infrastructure management for enterprise environments.

  • Why get trained: Learn how to operationalize generative AI solutions using Red Hat OpenShift AI, model serving, automation workflows, MLOps and GenAIOps practices.
  • Why it matters: GenAIOps capabilities help organizations scale AI deployments, improve operational consistency and manage enterprise AI workloads more securely and efficiently.
  • Who should attend: AI engineers, platform engineers, MLOps practitioners, data scientists and IT professionals responsible for deploying and managing enterprise AI environments.

Build practical GenAIOps and enterprise AI operational capabilities using Red Hat AI technologies with Trainocate. HRD Corp Claimable.

Overview

Experience the practices, culture, and tools that enable teams to reliably and efficiently build, deploy, and maintain GenAI applications in production.

GenAIOps Enablement with Red Hat AI Enterprise (AI501) is a five-day immersive enablement, delivered the Red Hat Way, to build the skills that teams need to articulate and deliver on their AI vision. While many AI training programs focus on a particular framework or technology, this course covers how the tools fit together in a full Generative AI Operations workflow, treating the AI-enabled application, not just the model, as the unit of delivery.

To achieve the learning objectives, participants should include multiple roles from across the organization. AI engineers, application developers, platform engineers, architects, and IT managers will gain experience working beyond their traditional silos. The daily routine simulates a real-world delivery team building an AI-powered application, 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 organization’s culture and mission succeed in the pursuit of generative AI initiatives.

This course is based on Red Hat AI Enterprise, including Red Hat OpenShift AI, as well as Red Hat OpenShift GitOps, Red Hat OpenShift Pipelines, and Generative AI models and open source libraries.

Skills Covered

This course takes you on an end-to-end journey of an AI-enabled application, from prompt experimentation to production deployment, while bringing different personas together to collaborate on a single platform seamlessly.

  • Understanding GenAI fundamentals, including tokens, context windows, and model behavior
  • Experimenting with prompts and evaluating your first AI-enabled application
  • Introducing an orchestration layer for standardized GenAI development
  • Implementing Retrieval Augmented Generation (RAG) for knowledge-enhanced applications
  • Building autonomous AI agents with tool-calling capabilities
  • Deploying AI safety guardrails and implementing GenAI security practices
  • Enabling observability with metrics, logging, and distributed tracing for GenAI systems
  • Exploring small language models and multi-modal capabilities
  • Optimizing models through quantization and compression techniques
  • Implementing Models as a Service (MaaS) for scalable AI infrastructure

Prerequisites

Target Audience

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 generative AI innovation.

It is especially valuable for:

  • AI Platform Users: AI engineers, application developers, data scientists, and data engineers building generative AI applications
  • AI Platform Providers: ML/GenAIOps engineers and platform engineers deploying and managing AI infrastructure
  • AII Platform Stakeholders: Architects and IT managers evaluating and overseeing generative AI adoption strategies

The scenario incorporates technical aspects of working with large language models and generative AI systems, offering practical insights into how these roles can align their efforts.

Course Curriculum

Core Foundations

  • GenAI Fundamentals

    Explore what GenAIOps is and how large language models work, including tokenization, context windows, and the factors that affect model behavior and performance.

  • Experimenting with Prompts

    Learn to craft effective prompts using system prompts and user prompts, configure temperature and output parameters, and optimize prompts for specific use cases.

  • Evaluating Your First AI-Enabled Application

    Implement prompt versioning, build evaluation pipelines, automate testing, and measure application quality systematically.

  • Introducing the Orchestration Layer

    Introduce an orchestration layer for building GenAI applications, deploy backend services, and implement GitOps practices for continuous deployment.

Advanced Topics

  • Integration and Orchestration

    Deploy vector databases, build RAG pipelines for knowledge-enhanced applications, implement tool calling, and create autonomous AI agents.

  • Safety and Observability

    Deploy AI safety guardrails, implement GenAI security practices, enable the three pillars of observability, metrics, logs, and traces.

  • Modeling Techniques

    Explore small language models for efficient deployment and multi-modal model capabilities for handling diverse input types.

  • Optimization and Deployment

    Apply quantization and compression techniques for improved performance, explore fine-tuning approaches, implement Models as a Service (MaaS), and bring it all together in a production deployment.

Dates & Locations

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Exam & Certification

No associated certification.

Training & Certification Guide

  • Many organizations face operational complexity and tool sprawl across teams, prompt and config drift leading to inconsistent outputs, quality regressions that slip in with changes, unmanaged grounding causing hallucinations, safety risks from prompt injection and harmful content, and unpredictable latency and costs that block scale. GenAIOps addresses these challenges through standardization, treating prompts and configs as code, continuous automated evaluations, governed RAG, platform-enforced guardrails, and end-to-end observability.
  • This course introduces real-world GenAIOps culture principles and modern practices. You will experience an AI-enabled application lifecycle end-to-end, from prompt and config versioning through deployment, continuous evaluation, and day-two operations. By the end of the course, you will be equipped to apply GenAIOps principles and leverage Red Hat AI Enterprise to drive and lead generative AI transformation initiatives within your organization.

  • As a result of attending this course, you will become familiar with the GenAI platform, understand where Red Hat AI Enterprise fits within the GenAIOps ecosystem, and experience an AI-enabled application lifecycle from end to end. You will gain practical patterns to build, ship, and run AI-enabled applications at scale, learning how to take them from prototype to production and keep them reliable.

Why train with Trainocate

AI501 teaches you how to operationalize and manage generative AI solutions in enterprise environments using Red Hat AI technologies.

This course focuses on deploying, monitoring, scaling, and governing generative AI systems using hybrid cloud and containerized AI infrastructure. It emphasizes enterprise-grade operational practices for AI workloads.

Key learning areas:

  • GenAIOps and AI lifecycle management
  • AI platform operations
  • Containerized AI workloads
  • Governance and monitoring of generative AI systems
  • Hybrid cloud AI infrastructure

Pro Tip: Focus on understanding operational workflows, not just AI models. Enterprises increasingly need professionals who can manage AI reliably at scale.

GenAIOps refers to the operational management of generative AI systems throughout their lifecycle.

GenAIOps extends traditional MLOps practices to generative AI applications, including deployment, orchestration, monitoring, governance, and optimization of large language models (LLMs) and AI agents.

Core GenAIOps areas:

  • AI deployment and orchestration
  • Monitoring and observability
  • AI governance and compliance
  • Lifecycle automation and optimization

Pro Tip: Understanding GenAIOps early can position you ahead of many AI professionals as enterprises move AI systems into production.

This course is designed for professionals responsible for deploying and managing enterprise AI platforms.

It targets technical professionals who work with AI infrastructure, DevOps, cloud-native platforms, or enterprise AI operations.

Best suited for:

  • MLOps engineers
  • AI platform engineers
  • DevOps engineers
  • OpenShift/Kubernetes administrators
  • Enterprise AI architects

Pro Tip: Familiarity with Linux, containers, Kubernetes, or OpenShift will help you maximize the value of this course.

You will learn how to deploy, operate, monitor, and govern enterprise generative AI environments.

The course emphasizes practical operational skills required to support AI systems in production environments.

Skills gained:

  • Managing AI infrastructure on hybrid cloud
  • Deploying and scaling AI workloads
  • Monitoring and troubleshooting AI systems
  • Implementing AI governance and operational controls
  • Working with OpenShift-based AI environments

Pro Tip: AI operational skills are becoming just as important as AI development skills in enterprise environments.

Enterprises need GenAIOps to ensure AI systems are scalable, secure, reliable, and compliant.

Many organizations can build AI prototypes, but operationalizing AI at scale requires governance, observability, automation, and lifecycle management. GenAIOps helps organizations move from experimentation to production-ready AI systems.

Enterprise benefits:

  • Improved AI reliability and uptime
  • Better governance and compliance
  • Scalable AI deployment
  • Faster operational troubleshooting

Pro Tip: Many organizations struggle more with operationalizing AI than building models. GenAIOps expertise is increasingly valuable.

AI501 focuses specifically on operationalizing generative AI systems rather than only traditional machine learning models.

Traditional MLOps courses typically focus on training and deploying machine learning models. AI501 expands this to include LLMs, AI agents, prompt workflows, hybrid cloud AI infrastructure, and enterprise governance.

Key differences:

  • Traditional MLOps:
    • Focus: Machine learning model lifecycle
    • Scope: Data science and model deployment
  • AI501 GenAIOps:
    • Focus: Generative AI operations and governance
    • Scope: LLMs, AI agents, hybrid AI infrastructure

Pro Tip: Learning GenAIOps prepares you for the next phase of enterprise AI adoption beyond traditional machine learning.

AI501 covers enterprise AI infrastructure and operational technologies within the Red Hat ecosystem.

The course focuses on technologies used to deploy and manage scalable AI systems in enterprise environments.

Technologies include:

  • Red Hat OpenShift
  • Kubernetes-based AI infrastructure
  • Containerized AI workloads
  • Hybrid cloud AI environments
  • Enterprise AI operational tooling

Pro Tip: Understanding Kubernetes and OpenShift concepts significantly improves your ability to manage AI systems at scale.

It prepares you for emerging roles focused on enterprise AI operations and platform engineering.

As organizations deploy generative AI solutions at scale, demand is increasing for professionals who can manage AI infrastructure, governance, and operational reliability.

Relevant roles:

  • GenAIOps Engineer
  • AI Platform Engineer
  • MLOps Engineer
  • AI Infrastructure Engineer
  • Enterprise AI Architect

The rise of enterprise AI adoption is creating strong demand for operational AI expertise across hybrid cloud and containerized environments.

Pro Tip: Combining AI operations knowledge with Kubernetes/OpenShift expertise can significantly improve your long-term career opportunities.

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