AI-300: Implement machine learning and generative AI solutions

Price
Net €
VAT €

Price
Price on Request

Duration
4 days

For companies and job seekers:
this course is 100% fundable!

Location

Course Language
English

Training Solutions
Online Live

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From intelligent assistance systems to automated decision-making processes: generative AI and machine learning are shaping modern digital business models. The key to success lies in the professional implementation of these technologies.

Key Topics

Productive use of ML and GenAI models
Deployment, scaling, and cloud operations
Lifecycle management and model maintenance
Monitoring, drift, and quality control
Security, compliance, and governance
Interfaces, APIs, and system integration
Increasing Efficiency Through Automation

Prerequisites:
Solid technical understanding of data processes, AI fundamentals, and modern IT infrastructures.

Target Audience
: Professionals in AI, data, IT operations, cloud, and software engineering.

Those who successfully integrate intelligent systems into productive processes create direct value for innovation, efficiency, and competitiveness. This establishes a skill set that remains in high demand in modern companies.

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Course Content

Try Azure Machine Learning

Preprocess data and configure feature engineering
Run an automated machine learning experiment
Evaluate and compare models
Configure MLflow for model tracking in notebooks
Train and track models in notebooks
Evaluate models using the Responsible AI dashboard

Perform hyperparameter tuning with Azure Machine Learning

Define search space
Configure sampling method
Configure early termination
Use a sweep job for hyperparameter tuning

Run pipelines in Azure Machine Learning

Create components
Create a pipeline
Run a pipeline job

Trigger Azure Machine Learning jobs with GitHub Actions

Understanding the business problem
Exploring the solution architecture
Using GitHub Actions for model training

Triggering GitHub Actions in feature-based development

Understanding the business problem
Examining the solution architecture
Trigger a workflow

Working with environments in GitHub Actions

Understanding the business problem
Exploring the Solution Architecture
Set up environments

Deploy a model using GitHub Actions

Understanding the business problem
Exploring the Solution Architecture
Model deployment

Planning and Preparing a GenAIOps Solution

Explore Use Cases for GenAIOps
Selecting the Right Generative AI Model
Understanding the development cycle of a language model application
Explore available tools and frameworks for implementing GenAIOps

Manage prompts for agents in Microsoft Foundry using GitHub

Apply version control to Prompts
Understanding Microsoft Foundry agents and prompt version control
Organizing Prompts in GitHub repositories
Develop secure workflows for deploying prompts

Evaluate and optimize AI agents through structured experiments

Designing evaluation experiments
Apply Git-based workflows to optimization experiments
Apply evaluation rubrics for consistent evaluation

Automate AI evaluations with Microsoft Foundry and GitHub Actions

Why automated reviews are important
Align ratings with human criteria
Creating evaluation datasets
Implementing batch evaluations with Python
Integrating evaluations into GitHub Actions

Monitor a generative AI application

Why is monitoring necessary?
Understanding key metrics to monitor
Explore monitoring options with Azure
Integrate monitoring into your app
Interpret monitoring results

Analyzing and Debugging Generative AI Applications Using Tracing

Why is tracing useful?
Determining what should be traced in generative AI applications
Implementing tracing in generative AI applications
Debugging complex workflows with advanced tracing patterns
Making informed decisions based on the analysis of trace data

Your training will be 100% funded

Our funding schemes are available exclusively to participants who live or work in Germany. Funding is not available outside Germany.

For career starters, job seekers, career changers, startups, experienced professionals, those looking to advance their careers, specialists, etc.

The QCG program for everyone in the company

Further information

The education voucher for all job seekers

Further information

Frequently Asked Questions

What exactly do you learn in the AI-300 course?

This course demonstrates how to effectively deploy, monitor, and reliably scale machine learning and generative AI solutions.

Why is the AI-300 course so popular right now?

Companies are investing heavily in AI-powered processes and need expertise to deploy models securely and efficiently.

Who is the AI-300 course particularly relevant for?

Ideal for professionals in data, AI, cloud, and IT operations who want to build practical, productive AI workflows.

What practical skills does the course teach?

The focus is on deployment, monitoring, model optimization, governance, and the secure use of generative AI in real-world scenarios.

Why is the operationalization of AI crucial?

A good model alone isn't enough—only stable operation, scalability, and control create real business value.

What risks does the AI-300 course help you avoid?

The course helps you avoid common pitfalls such as model drift, performance issues, security vulnerabilities, and uncontrolled AI outputs.

What career benefits does the AI-300 course offer?

This expertise is in high demand and significantly improves career prospects in fields such as AI engineering, MLOps, and cloud AI.

Is the AI-300 course on generative AI in business worth it?

Yes, especially for the professional use of large language models (LLMs), automation, and scalable AI applications in a corporate environment.

Do you have any further questions? Please contact us.

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