Learn Azure Gen AI Training and Deployment

Azure Gen AI training is a powerful tool for building and deploying AI models. It offers a wide range of pre-built models and tools that can be used to train and deploy AI models.

To get started with Azure Gen AI training, you'll need to create an Azure account and enable the Azure Gen AI resource. This will give you access to a variety of tools and resources for building and deploying AI models.

You can use the Azure Gen AI training service to train models on a wide range of data types, including text, images, and audio. This allows you to build models that can understand and respond to different types of input.

One of the key benefits of Azure Gen AI training is its ability to scale with your needs. You can start small and scale up as your needs grow, making it a flexible and cost-effective option for building and deploying AI models.

By the end of the Azure Gen AI Training, participants will be able to develop intelligent applications using Generative AI to address real-world challenges.

You'll gain a comprehensive understanding of the fundamentals and operations of Azure OpenAI Service, as well as the ability to effectively leverage Azure resources to enhance AI capabilities and efficiency.

To achieve this, you'll learn Azure Machine Learning Service, Python Programming, and Azure Machine Learning Workspace, among other skills.

Here are the key skills you'll gain:

You'll also learn to create, train, test, and deploy your AI model in the cloud, and work with Azure APIs, including those for vision, language, and search.

The course covers a range of topics, including AI and ML Definitions, Deep Learning, and the Microsoft Team Data Science Process, with a total of 6 minutes, 3 minutes, and 30 minutes of content, respectively.

Here's a breakdown of the course content:

By the end of the course, you'll be able to troubleshoot common issues related to Azure OpenAI Service and efficiently handle data to make AI-driven predictions and improve decision-making.

The AI-050 certification training is a comprehensive course designed to provide knowledge on developing and implementing AI models using Azure's powerful OpenAI services. This course is the gateway to Azure's extensive AI capabilities, offering boundless opportunities in the world of Generative AI solutions.

The course is tailored to meet the needs of both beginners and experienced professionals, regardless of their expertise level. Participants will gain a comprehensive understanding of the fundamentals and operations of Azure OpenAI Service.

By the end of the AI-050 course, participants will be able to develop intelligent applications using Generative AI to address real-world challenges. They will also effectively leverage Azure resources to enhance AI capabilities and efficiency.

The course covers a wide range of topics, including:

The course duration is 16 weeks, and it's an online program that offers a certificate from Microsoft. Participants will learn generative AI with code and no-code on Azure and OpenAI, and they will get details on syllabus, projects, tools, and more.

Model training and management are crucial steps in developing and deploying AI models on Azure. You can create an ML workspace, which is the central hub for all your machine learning activities, using the Azure ML Service.

To train a model, you'll need to set up experiments, which are the foundation for training, testing, and deploying models. Experiments allow you to define and manage the workflow for training and testing your models.

Here's a quick overview of the steps involved in training a model using Azure ML:

Once you've trained your model, you can register it in the model registry, which is a central location for storing and managing all your models. This makes it easy to track and manage your models throughout their lifecycle.

The Azure Machine Learning Service provides a range of resources to help you train and deploy your models, including the ability to define scoring and dependencies, configure a deployment target, and build a container image.

You'll need to connect to your Azure Machine Learning workspace to manage your models. This can be done in just 4 minutes.

To register a trained model, you'll need to understand how the model registry works. This involves registering a model locally and from a workspace training run.

Preparing a model for deployment involves identifying dependencies, configuring a deployment target, and building a container image. This process can take around 9 minutes.

Once you've prepared your model, you can deploy it as a web service and test it by sending JSON objects to the API. This process can take around 13 minutes.

Here's a summary of the deployment process:

By following these steps, you can successfully deploy your model as a web service and start using it in your applications.

I attended a knowledge session at Microsoft where I gained a solid understanding of the basics of AI/ML.

The Azure OpenAI Service is a cloud-based service that offers OpenAI's language models, such as GPT-3.5, as an API.

Developers can integrate powerful natural language generation capabilities into their applications and services using this service.

The Azure OpenAI Service can be used to build a wide range of applications, including chatbots, content generation tools, language translation, text summarization, and more.

It's particularly useful for tasks that involve natural language understanding and generation.

You can earn a certificate in Generative AI for Business with Microsoft Azure OpenAI from Great Learning and Microsoft Azure. This certificate is a valuable addition to your portfolio or resume.

The certificate is a recognition of your dedication and commitment to advancing your knowledge in the field of Generative AI and Azure OpenAI Service. It can be a great way to demonstrate your commitment to learning and your newly acquired skills in this exciting domain.

To earn this certificate, you'll need to complete the AI-050: Develop Generative AI Solutions with Azure OpenAI Service course. Upon successful completion, you'll receive a Participation Certificate.

Here are the details of the certification: