How to Train large language models (LLMs) like ChatGPT on Private Data Using Azure AI Hub It has been observed that people become more proactive when they incorporate Generative AI into their day-to-day workflows. Generative AI enhances proactivity when utilized effectively. Its benefits extend not only to individuals but also to entire industries. There have been rapid advancements in Generative AI technology, evolving from simple text generation to analyzing large amounts of data and generating images. ChatGPT can answer a wide range of questions, but if you are not widely known and want to ask specific questions about yourself, ChatGPT might not have the answers. However, with the help of Azure AI, large language models (LLMs) like ChatGPT can be customized to meet individual requirements, allowing it to function as a personalized AI assistant, How to make your own personalized AI assistant or chatbot Building your own AI assistant or chatbot is exciting, but you need to consider various components for it, such as the LLM module, AI services, orchestration, deployments, monitoring, and more. If you are an Azure enthusiast, Azure AI Studio can be used for this purpose. In this article, I will be using it. Azure AI Studio is a collection of various AI modules and services. It also provides a platform for building AI tools. It supports no-code, low-code, and full-code approaches. How to access Azure AI Studio: It is part of portal.azure.com. You need to create an account if you don't already have one. Login to Azure portal and Search for Azure AI Foundry , and click on it. Click on  Hub Provide the required information for your project, then click on Review + Create, and finally, click on Create Click on Go to resource Click on Launch Azure AI Foundry A separate window for https://ai.azure.com will open. Click on + New project, enter the project name, and click Create. Project Name: aidemoproject  created Click on Models + endpoints and then +Deploy model Click on Deploy base model Select model of your choice. I am selecting gpt-4o-mini and clicking on Confirm Check the Token per Minute Rate Limit as per your application requirements. In this example I took minimum as 1k and clicked on Deploy I deployed gpt-4o-mini, Endpoint can be used in external applications. Here I am  going to deployed model ((gpt-4o-mini) in our chat playground. Click on playgrounds then Try the Chat playground Give instruction to model, what you want it to do and click on Apply changes Now Chat playground is ready to use. Type any word and see the result I got below output from model It is working in same way, what was instructed. I created own chat bot with custom system message. Here I can upload our data and model can use that data to answer of questions. Train LLM model on private Data You can retrieve data for LLM module in different way, such as Azure Blob Storage, get data with storage URL, upload files/folders. Here I will be using upload files/folders option. Click on Data + indexes and then + New data, I am going to create a new data source, where I can upload our data and train LLM module to answers on the basis on that data. Click on Upload files/folders as for this demo I am  going to upload data directly to Azure AI studio but other option also available to store private data. I just uploaded my resume, now click Next Now give Data name, in this example I gave as personaldata and click on Create You can see your uploaded data path on next screen Now I have created data store name as personaldata, now time to create index, click on Data + indexes Click on Indexes Click on + New index Click on Data in Azure AI Foundry Select data source what was created before. Click on Create a new Azure AI Search resource It will ask you to login with https://portal.azure.com , create a search service there and provide values as per your requirements, click on Review + create then Create Now go back to https://ai.azure.com prompt and select created AI Search service aisearchservice124 and click on Next Select vector search Click on Create vector index . This step may takes couples of minutes Status will be Completed once index is created. Index creation took me about 10 minutes. Now click on Playgrounds and then Try the Chat playground Select LLM model of your choice and then index what was created Now type the question related that resume and you will get answers of that How to Train large language models (LLMs) like ChatGPT on Private Data Using Azure AI Hub How to Train large language models (LLMs) like ChatGPT on Private Data Using Azure AI Hub It has been observed that people become more proactive when they incorporate Generative AI into their day-to-day workflows. Generative AI enhances proactivity when utilized effectively. Its benefits extend not only to individuals but also to entire industries. There have been rapid advancements in Generative AI technology, evolving from simple text generation to analyzing large amounts of data and generating images. ChatGPT can answer a wide range of questions, but if you are not widely known and want to ask specific questions about yourself, ChatGPT might not have the answers. However, with the help of Azure AI, large language models (LLMs) like ChatGPT can be customized to meet individual requirements, allowing it to function as a personalized AI assistant, How to make your own personalized AI assistant or chatbot How to make your own Building your own AI assistant or chatbot is exciting, but you need to consider various components for it, such as the LLM module, AI services, orchestration, deployments, monitoring, and more. If you are an Azure enthusiast, Azure AI Studio can be used for this purpose. In this article, I will be using it. Azure AI Studio is a collection of various AI modules and services. It also provides a platform for building AI tools. It supports no-code, low-code, and full-code approaches. How to access Azure AI Studio : It is part of portal.azure.com . You need to create an account if you don't already have one. Login to Azure portal and Search for Azure AI Foundry , and click on it. How to access Azure AI Studio portal.azure.com Azure AI Foundry , Click on Hub Hub Provide the required information for your project, then click on Review + Create , and finally, click on Create Review + Create Create Click on Go to resource Go to resource Click on Launch Azure AI Foundry Launch Azure AI Foundry A separate window for https://ai.azure.com will open. Click on + New project , enter the project name, and click Create . https://ai.azure.com + New project Create Project Name: aidemoproject created aidemoproject Click on Models + endpoints and then +Deploy model Models + endpoints +Deploy model Click on Deploy base model Deploy base model Select model of your choice. I am selecting gpt-4o-mini and clicking on Confirm gpt-4o-mini Confirm Check the Token per Minute Rate Limit as per your application requirements. In this example I took minimum as 1k and clicked on Deploy Token per Minute Rate Limit Deploy I deployed gpt-4o-mini , Endpoint can be used in external applications. gpt-4o-mini Endpoint Here I am  going to deployed model (( gpt-4o-mini) in our chat playground. Click on playgrounds then Try the Chat playground gpt-4o-mini) playgrounds Try the Chat playground Give instruction to model, what you want it to do and click on Apply changes Apply changes Now Chat playground is ready to use. Type any word and see the result I got below output from model It is working in same way, what was instructed. I created own chat bot with custom system message. Here I can upload our data and model can use that data to answer of questions. Train LLM model on private Data Train LLM model on private Data You can retrieve data for LLM module in different way, such as Azure Blob Storage, get data with storage URL, upload files/folders. Here I will be using upload files/folders option. Click on Data + indexes and then + New data, I am going to create a new data source, where I can upload our data and train LLM module to answers on the basis on that data. Data + indexes + New data, Click on Upload files/folders as for this demo I am  going to upload data directly to Azure AI studio but other option also available to store private data. Upload files/folders I just uploaded my resume, now click Next Next Now give Data name, in this example I gave as personaldata and click on Create Create You can see your uploaded data path on next screen Now I have created data store name as personaldata, now time to create index, click on Data + indexes Data + indexes Click on Indexes Indexes Click on + New index + New index Click on Data in Azure AI Foundry Data in Azure AI Foundry Select data source what was created before. Click on Create a new Azure AI Search resource Create a new Azure AI Search resource It will ask you to login with https://portal.azure.com , create a search service there and provide values as per your requirements, click on Review + create then Create https://portal.azure.com search service Review + create Create Now go back to https://ai.azure.com prompt and select created AI Search service aisearchservice124 and click on Next https://ai.azure.com aisearchservice124 Next Select vector search vector search Click on Create vector index . This step may takes couples of minutes Create vector index Status will be Completed once index is created. Index creation took me about 10 minutes. Completed Now click on Playgrounds and then Try the Chat playground Playgrounds Try the Chat playground Select LLM model of your choice and then index what was created Now type the question related that resume and you will get answers of that

Want to Train ChatGPT on Your OWN Data? Here's How to Do It

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