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Operationalize machine learning and generative AI solutions (AI-300)
Code:
AI-300
Duration:
4 Day
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$2595
USD
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This course covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices.
This course is available in the following formats:
Duration: 4 Day
Duration: 4 Day
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Delivery Format: Virtual Classroom
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$ 2595 |
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Delivery Format: Virtual Classroom
|
$ 2595 |
|
|
Delivery Format: Virtual Classroom
|
$ 2595 |
|
|
Delivery Format: Virtual Classroom
|
$ 2595 |
By the end of this course, learners will be able to design, deploy, automate, monitor, and optimize machine learning and generative AI solutions on Azure using MLOps and GenAIOps practices to deliver secure, scalable, and production-ready AI systems.
Module 1: Operationalize machine learning models (MLOps)
- Experiment with Azure Machine Learning
- Perform hyperparameter tuning with Azure Machine Learning
- Run pipelines in Azure Machine Learning
- Trigger Azure Machine Learning jobs with GitHub Actions
- Trigger GitHub Actions with feature-based development
- Work with environments in GitHub Actions
- Deploy a model with GitHub Actions
Module 2:Operationalize generative AI applications (GenAIOps)
- Plan and prepare a GenAIOps solution
- Manage prompts for agents in Microsoft Foundry with GitHub
- Evaluate and optimize AI agents through structured experiments
- Automate AI evaluations with Microsoft Foundry and GitHub Actions
- Monitor your generative AI application
- Analyze and debug your generative AI app with tracing
Module 1: Operationalize machine learning models (MLOps)
- Experiment with Azure Machine Learning
- Perform hyperparameter tuning with Azure Machine Learning
- Run pipelines in Azure Machine Learning
- Trigger Azure Machine Learning jobs with GitHub Actions
- Trigger GitHub Actions with feature-based development
- Work with environments in GitHub Actions
- Deploy a model with GitHub Actions
Module 2:Operationalize generative AI applications (GenAIOps)
- Plan and prepare a GenAIOps solution
- Manage prompts for agents in Microsoft Foundry with GitHub
- Evaluate and optimize AI agents through structured experiments
- Automate AI evaluations with Microsoft Foundry and GitHub Actions
- Monitor your generative AI application
- Analyze and debug your generative AI app with tracing
This course is intended for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure. It is suited for learners with experience in Python, a foundational understanding of machine learning concepts, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools, who are preparing to implement MLOps and GenAIOps workflows using Azure-native services.