NOTE: This course is only for people interested in learning "Microsoft Azure OpenAI service". If you are looking for open source version of OpenAI, then this course should not be on your wish list.
NOTE: This course is only for people interested in learning "Microsoft Azure OpenAI service". If you are looking for open source version of OpenAI, then this course should not be on your wish list.
This course covers all the key concepts related to Azure OpenAI. Be it function calling or something as small as knowing how your engine processes tokens, the course has it all covered. In this course you will learn about concepts such as temperature parameter, token parameter, adding external API's to Azure Open AI function calling, integrating other Azure services such as the Azure Speech Service with Azure Open AI to make your engine/ model more efficient and powerful. This course is tailored in a very concise and short manner, providing you with only the important stuff so that your time is well-spent. This course will act as a bridge to your journey in being a master at using Azure Open AI and its offerings. Although this course is short, the course assures that you get your money's worth
Course Level: The course goes all the way up from level 0 to level 100; Don't know what's the basic difference between Azure OpenAI and OpenAI, don't worry, the course's got your back.
Hand-On Labs: The hands-on labs in the course are very enriching. You will be provided with a github repository which will contain all the codes for the hands-on labs covered in this course. The hands-on labs offered in this course cover a variety of topics including:
1) Chat Completions API.
2) Making use of text embedding engine for enhanced machine learning processes.
3) integrating speech-to-text token query retrieval in your chat engine.
4) making use of function calling functionality exclusive to Azure Open Ai to call an external API to retrieve real-time information/data.
5) Exploring concept of RAG (Retrieval Augmented Generation) by integrating Azure Ai Search with your chat engine.
6) Using Vector search and information retrieval using Azure Machine Learning Workspace.
7) Using GPT-4 using Computer Vision.
Bonus Section: A bonus section that includes GitHub Copilot has been made available with this course as well. Concepts like multi language support, @VScode agent, @workspace agent and code debugging have been explained in depth.
Prerequisites: knowledge about Python programming language and basic command line interface commands makes up for the prerequisites for the course.
Buy this course and get ready to embark on a journey full of brilliant learning.
a brief introduction about what the course goals are and what the course is going to cover
basic software download and other stuff walkthrough
Cloud Technology is a costly affair and we all know it. So following are the bullet points to keep in mind to minimize your chances of making unwanted losses.
1)Always delete the resource group and the resource immediately after your work gets over. It always pains me to remember the fact that I once incurred a loss of nearly $1000 after keeping my AI search resource undeleted for a week (fortunately I had to pay only 10 percent of the total bill according to Microsoft's one-time refund policy).
2)Always have a look at the price calculator before using a resource.
3)Fine-Tuning your azure OpenAI chat engine is costly as hell. I incurred a loss of $1000 because of this ,which again, fortunately, was waived off as a result of Microsoft's one-time refund policy (I had to pay only 10% of the total billing amount). I would suggest watching the Fine-Tuning video in this course only for educational intent unless and until you want to fine-tune your model for actual project work.
4)Take periodic looks and keep a track of your cost management in your azure subscription by going to the "cost management + billing" tab.
Creating an Azure OpenAI resource in your Azure portal account
learn about how to deploy a model of your choice
master the art of prompt engineering
tips to refine your prompt engineering skills
learn how you can control your engine responses with help of the "temperature" parameter
learn about "ChatCompletions" API and its significance
learn how to use the Chat Completions API to get a response to your query from your engine
learn what makes Azure OpenAI have an upper edge
Continuation of the fundamental differences between Azure OpenAi and OpenAi. Understanding why Azure OpenAi stands out from the crowd
learn about the basics of Open AI
understanding what is function calling using Azure OpenAI.
master the art of function calling with Azure OpenAI
combining azure cognitive search solution with Azure Open Ai to create a very powerful document analyzer
Fine-Tuning your custom model using your own training dataset for refined responses
A Demo of How You Can Fine-Tune your own custom model
Evaluating the performance of your custom model on the basis of your custom training dataset
a lab where you learn to integrate Azure Speech service with Azure OpenAI
learning to use Azure OpenAI's text embedding engine
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