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AI-300: Implement machine learning and generative AI solutions
Production-ready AI systems don't emerge from training alone. AI-300 shows which processes are needed to bring models into enterprise use – stably, securely, and at scale.
Key topics
- A structured approach to transferring ML and GenAI models into live operations
- Planning cloud operations, scaling strategies, and controlled deployments
- Actively managing model lifecycles and keeping them current
- Developing monitoring concepts and ensuring real-time drift detection
- Embedding compliance, security, and governance from the start
- Enabling system integration via APIs and seamless connectivity
- Applying automation to build more efficient AI workflows
Prerequisite
Basic knowledge of data processes, AI fundamentals, and IT infrastructure makes entry easier.
Target group
Suited for professionals in AI engineering, data, cloud, and software engineering who want to take their AI projects beyond training and into stable production environments.
Operationalization is the key – it turns models into measurable business value.
Course Content
Getting Started with Azure Machine Learning – Fundamentals and First Models
- Setting up data preparation and feature engineering systematically
- Configuring and running automated ML experiments
- Analyzing and comparing model results methodically
- Using MLflow to track training runs in notebooks
- Setting up model training in notebooks and managing it in a controlled way
- Checking model quality and fairness with the Responsible AI Dashboard
Optimizing Hyperparameters and Running Sweep Jobs in Azure ML
- Defining and configuring hyperparameter search spaces precisely
- Selecting and adjusting appropriate sampling strategies
- Setting up early stopping for faster tuning
- Launching sweep jobs and evaluating results systematically
Developing Pipeline Structures in Azure Machine Learning
- Defining pipeline components and connecting them to each other
- Assembling a complete ML pipeline from individual building blocks
- Launching, monitoring, and evaluating pipeline jobs
Automated Model Training with GitHub Actions
- Capturing requirements and constraints of the business problem
- Understanding the right solution architecture for the ML use case
- Configuring and using GitHub Actions for automated training processes
Controlling and Automating Feature Workflows with GitHub Actions
- Clarifying the starting point and goals of the development initiative
- Thinking through the solution architecture for feature-based development
- Triggering and controlling GitHub Actions workflows with precision
Setting Up and Managing Deployment Environments in GitHub Actions
- Understanding project context and deployment requirements
- Exploring and classifying architecture for environment-based deployments
- Configuring and operating deployment environments in GitHub Actions
Deploying Models Step by Step with GitHub Actions
- Capturing deployment goals and technical context
- Analyzing and classifying the deployment architecture
- Realizing and activating model deployment with GitHub Actions step by step
GenAIOps: Planning and Foundations of Generative AI Solutions
- Identifying practical use cases for GenAIOps
- Selecting the right generative AI model for the given context
- Understanding the development and operation lifecycle of an LLM application
- Getting to know frameworks and tools for GenAIOps at a glance
Versioning and Deploying Prompts for Foundry Agents
- Managing prompts systematically using version control
- Understanding Foundry agents and their connection to prompt versioning
- Storing and maintaining prompts in GitHub repositories in a structured way
- Establishing secure and reproducible deployment workflows for prompts
Evaluating and Improving AI Agents Systematically
- Planning and implementing evaluation approaches for AI agents in a structured way
- Conducting Git-supported optimization experiments
- Developing and applying rubric-based criteria for consistent quality assurance
Automating AI Quality Assurance with Foundry and GitHub Actions
- Understanding the value and benefits of automated quality checks
- Aligning evaluations with human standards and calibrating them
- Building and systematically maintaining evaluation datasets
- Scripting and scaling batch evaluations with Python
- Embedding automated evaluation runs into GitHub Actions
Building Monitoring for Generative AI Applications
- Defining monitoring goals and requirements for AI applications
- Selecting relevant metrics for stable AI systems
- Getting to know Azure monitoring options in the context of generative AI
- Integrating monitoring functionality into the application
- Reading, interpreting, and using monitoring results to guide action
Implementing Tracing Strategies for Generative AI Applications
- Classifying the value and use cases of tracing in AI systems
- Identifying relevant trace points in generative applications
- Integrating tracing systematically into generative AI applications
- Debugging complex AI workflows using advanced tracing patterns
- Reading, evaluating, and using trace data for optimization decisions
Up to 100% funding for your continuing education
Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.
Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.
What do participants learn in the AI-300 training?
- AI-300 covers how ML and GenAI solutions are reliably transferred into production, monitored, and scaled.
Why is operationalizing AI so important today?
- Qualified professionals who can transfer AI models securely into production systems are in high demand – the know-how from AI-300 is correspondingly valuable.
Who benefits from AI-300 training?
- AI-300 is aimed at data scientists, AI engineers, cloud specialists, and IT operations professionals who want to build AI systems ready for production.
What concrete skills does AI-300 develop?
- Key areas include deployment processes, monitoring strategies, model optimization, governance, and hands-on experience with generative AI in production environments.
Why isn't a trained model enough on its own?
- Only stable operations with scaling, monitoring, and controlled lifecycle management turn a model into real business value.
Which AI operational risks does AI-300 address specifically?
- Model drift, quality fluctuations, security gaps, and uncontrolled GenAI outputs are identified, analyzed, and mitigated through appropriate measures.
What career opportunities does AI-300 open up?
- The knowledge from AI-300 is particularly sought after in areas like MLOps, AI engineering, and cloud AI – and strengthens your position on the job market.
Does AI-300 also cover professional GenAI use?
- Yes – with a focus on LLMs, GenAIOps, and scalable AI applications, AI-300 delivers practical know-how for enterprise use of generative AI.
