ChatGPT has fast become a popular example of what generative ai can do, but one of the limitations of a generative ai tool like ChatGPT is that it runs off of public data (websites, documents and content). In order to create similar tools for a business or organization, it is necessary to provide the platform with access to private files, content and data.
MediaManager.net was created as a user interface and set of AWS tools that make it possible to connect the AWS services in your AWS account, where your files, content and data are secure, with end user and content management tools.
Through the combination of MediaManager.net, available on the AWS Marketplace (here) and AWS machine learning services, you can create your own ChatGPT model that can be used for various tasks, such as chatbots, language translation, and content generation.
Step 1: Define the Problem, the Solution and Gather the Data
The first step in creating a generative ai solution is to define the problem you want to solve and gather the data you need to train your model. For example, if you build a chatbot to answer customer queries, you must gather a dataset of customer queries and their corresponding responses. You can gather data from various sources, such as web pages, forums, and social media.
Step 2: Launch MediaManager.net on AWS for User Management and Data / Content Gathering
MediaManager.net is available as a metered service on AWS here. Launch MediaManager.net to provide a ready-made, secure, user and content management system to connect AWS AI and ML tools to an easy to user interface.
Step 2: Choose AWS Machine Learning Service(s) Based on Goals
Once you have gathered your data, the next step is to choose an AWS machine-learning service that can be used to train your generative ai model. Several AWS services, such as Amazon SageMaker, Amazon Comprehend, and Amazon Transcribe, can be used for natural language processing and language modeling.
For this example, we will use Amazon SageMaker, a fully managed machine learning service that can be used to build, train, and deploy machine learning models. Amazon SageMaker provides a range of machine learning algorithms, pre-built models, and tools for training and deploying custom models.
Step 3: Preprocess the Data
Before training your generative ai model, you must preprocess the data to prepare it for training. This involves cleaning the data, tokenizing it into words or subwords, and encoding it in a format the machine learning algorithm can use.
Amazon SageMaker provides tools for data preprocessing, such as Amazon SageMaker Processing, which can be used to run data preprocessing scripts on large datasets. You can also use AWS Glue, a serverless data integration service, to extract, transform, and load your data into the format required for training your model.
Step 4: Train the Model
Once you have preprocessed your data, the next step is to train your generative ai model using Amazon SageMaker. Amazon SageMaker provides a range of machine learning algorithms, pre-built models, and tools for training and deploying custom models.
For a solution similar to ChatGPT, you can use the Hugging Face Transformers library, which is a popular open-source library for natural language processing and language modeling. The Hugging Face Transformers library provides pre-trained models for various tasks, including language modeling. It can be used with Amazon SageMaker to fine-tune the model on your dataset.
Step 5: Deploy the Model
After training your model, the next step is to deploy it for your application or service. Amazon SageMaker provides several ways to deploy your model, such as hosting it on an Amazon SageMaker endpoint or deploying it as a serverless function using AWS Lambda.
MediaManager.net has a growing set of tools and APIs to present generative ai output to end users.