In this learning path, you’ll learn how to implement key concepts to build an end-to-end MLOps solution.
Over the course of 3+ hours, you’ll learn
- How to take your machine learning model from experimentation to production
- How to automate your machine learning workflows by using GitHub Actions.
- How to protect your main branch and how to trigger tasks in the machine learning workflow
- How to automate code checks whenever you update code
- How to train, test, and deploy a machine learning model by using environments
- How to automate and test model deployment with GitHub Actions and the Azure Machine Learning CLI (v2)
This learning path will explore the DevOps principles that help in scaling a machine learning project from experimentation to production.
Modules covered include
- Introduction to DevOps Principles for Machine Learning
- Source Control for Machine Learning Projects
- Automate Machine Learning Workflows
- Continuous Deployment for Machine Learning
This learning path will help you get started with generative AI and set you on the road to generating new content based on natural language input.
Topics covered include
- Understanding how large language models form the foundation of generative AI
- Describing how Azure OpenAI Service provides access to the latest generative AI technology
- Understanding how generative AI applications, such as copilots, support efficiencies
- Describing how prompts and responses can be fine-tuned
- Describing how Microsoft’s responsible AI principles drive ethical AI advancements
During this 5+ hour course, you’ll cover a number of modules, including
- Get started with Azure Open AI Services
- Build Natural Language Solutions with Azure Open AI Services
- Apply Prompt Engineering with Azure Open AI Services
- Generate Code with Azure Open AI Services
- Generate Images with Azure Open AI Services
- Use Your Own Data with Azure Open AI Services
- Fundamentals of Responsible AI
Additionally, this learning path will help prepare you for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution.
The purpose of this course is to give you hands-on practice with Generative AI models. You’ll explore the use of generative artificial intelligence (AI) models for natural language processing (NLP) in Azure Machine Learning. First you’ll delve into the history of NLP, with a focus on how Transformer architecture contributed to the creation of large language models (LLMs). Then you’ll practice training a pretrained Large Language Model (LLM) on specific tasks.
The first course in this three-part series perfect for those who are new to the cloud, will start teaching you some of the basic cloud concepts. Over about an hour, you’ll explore
- Cloud concepts, deployment models, and understanding shared responsibility in the cloud
- The benefits cloud computing can offer you or your organization
- Different cloud service types and shares some of the use cases and benefits aligned with each service type.
In this second course in the Microsoft Azure Fundamentals series, you’ll get a streamlined overview of many Azure services. Concepts covered include
- Core architectural components
- Compute and networking services
- Storage services
- Identity, access, and security
Additionally, this learning path will help prepare you for Exam AZ-900: Microsoft Azure Fundamentals.
Get started on building the knowledge and skills you need to take Exam AI-900: Microsoft Azure AI Fundamentals. This 3+ hour course comprises 3 modules: Fundamental AI Concepts, Fundamentals of Machine Learning, and Fundamentals of Azure AI Services. Delve into the core concepts underpinning machine learning and AI.
And that’s just the start. There is a lot more to discover on their website. Check out all of their learning paths here and get started on building new skills and knowledge.
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