Democratizing AI: Each Redefines AI Infrastructure

Hey! Welcome to our new blog post. In this post, we are going to talk about our story, raison d’être, and what magicpoint is.

This is where the story begins…

After the rise of Big Data, most companies started understanding and steering their companies or startups with data. They began using ML models or creating their models with custom data. In conjunction with these developments, companies began realizing the value of data and analyzing it for meaningful information. On the end-user side, users discovered personalized products and services.

The public release of ChatGPT sent shockwaves through the industry, creating a nuclear impact on November 30, 2023.

Every company, every startup, and every developer tried to find a use case to integrate ChatGPT into their products. At the same time, Midjourney published a new version of the Image Generation model.

Everyone started talking about AI, the future of AI, and the capabilities of AI due to its democratized nature. No location, computer, or industry restriction… You can create an image of an astronaut on the street with Midjourney or talk with ChatGPT about recognizing quality wine.

A few months later, AI users realized they needed to feed their data to generation models because they had unique data and wanted more relevant results from AIs.

But, wait. They were paying tons of money to these APIs because they were using fine-tuned or created models with more than 13 billion data sets. But to build a how-to-cook app or a chatbot for their website users don’t need huge models.

Companies realized that they needed to find a way to solve pricing, deployment, and fine-tuning problems. 

What are the steps for fine-tuning and serving an AI model properly?

• Find a public AI model that handles your request.
• Create a fine-tuning pipeline and script.
• Containerize it and deploy it to the cloud.
• Fine-tune with your data and generate a new model/file.
• Deploy the new model to the cloud.
• Build a CI/CD pipeline.
• Secure servers and model inference endpoints.
• Fix the scaling issue in high traffic.
• Handle high GPU prices and availability on public & private cloud providers.

These steps led to our initiation of Each.

What is Each?

Each is a new-generation platform solution, especially for AI and ML models. You can create fine-tuning pipelines and inference endpoints with just a few clicks or SDK methods. Each collects valuable public AI and ML models and stores them in Model Yard. Everyone can use or fine-tune these qualified and verified models without complexity. Conditional statement responses take around 5ms, and database-related response times are approximately 100ms, but AI model latencies can be more than 5 seconds depending on the input. For scaling, we created a solution with latency-based scaling, which enables you to downscale or upscale your GPU-powered machines with models' response time. It improves user experience and also the usability of AI.

What is Magicpoint?

Magicpoint simplifies creating custom fine-tuning a model, deployment, and inference endpoints. With Magicpoint SDK, you can turn your model into an API. It is natively integrated with Each. You can deploy your pipelines or models in seconds.

Who are we?

We are a small team of engineers and platformers who want to build next-generation AI infrastructure technologies and democratize GPU-powered machines.

Let's keep in touch! but

We are not only a platform solution, we are also a community of passionate people who would like to make an impact. Join us and keep in touch! Also, if you have any issues with your deployment, development, or scaling processes, contact us, and let’s create solutions together!