Rule 0: We believe every app will be an AI-infused app in the future
This is a Seaplane core belief. A Rule Zero, if you will. AI is here and even if you think AI can’t beat you, deep down you know someone using AI probably can. We use the term infused since we know AI (this includes Machine Learning [ML] and Data Science [DS], etc.) will become part of every system you interact with. Co-pilots, chatbots, and computer vision are already rapidly becoming part of every solution you use and without doing something similar, you will fall behind.
It’s important to also understand that it’s not just all about the models. Whether you’re using foundational LLMs; open-source models optimized on a particular domain; or even your own trained model on your proprietary data, you still need an app that wires all these things together, connects to your data sources and delivers the actual value to your users. This is a key challenge moving from demoware or an experiment to something actually delivering ROI at scale.
The clouds as you know them today, the ones running most of the legacy apps, are not equipped to help the vast number of people who need to innovate in this emerging AI space. These Cloud Service Providers (CSPs) provide a host of components and powerful low-level functionality, but understanding and efficiently configuring them all requires expensive, specialist cloud expertise. Finally, when it is all set up and working, you get to run it, maintain it, and operate it 365 days a year. (Or 366 in 2024!) In short, making AI ‘as easy as the cloud’ isn’t a positive for the broader developer and data science communities at all. We know there is a simpler way. We built it.
How will Apps be AI infused?
Today, there is already a long list of use cases where traditional data science and machine learning models are being enhanced with AI or as we like to say, infused. We’ll explain a few to get your creative juices flowing but the options are endless and the future is bright.
Traditional Image recognition of faces and objects has now been enhanced to find cancer in an MRI, do full facial swaps on photographs, or even create new amazing images from text (think MidJourney and the GenAI movement).
Legacy Speech Recognition technologies that are utilized by virtual assistants of the past are expanding with AI to include better answers from detailed documents via Retrieval-Augmented Generation (RAG) style chatbots.
For years, Predictive Analytics have been used for sales forecasts but when they become AI-infused, these super sales apps will spawn off pre-written emails that sales professionals can use to grow their funnel and retain customers.
Natural Language Processing [NLP] has been around for some time but with AI, we can add the enhancement of a more human-like interface you can interact with; one that has memory and context from each earlier conversation.
Another example is a Recommendation System. These machine learning systems of the past used to know what others like you also bought or wanted to buy (think: “people like you also bought…” or basket-calculation); but with AI this has turned into a full detailed explanation of why Product A was chosen for you over Product B and what you’d like about it in great detail. Imagine a snowboard website where you get more than “you might like this board too” but instead you get a full AI-infused shopping experience, even magically updating its future suggestions, as you improve your skill levels over time.
What is needed to support this movement?
As CEOs and Boards continued to ask “What is our AI Strategy?”, there is a dire need to increase the velocity of this transformation toward AI-infused. Unfortunately for most, the talents they have and the technical chops they need do not match. AI Developers (including data science and machine learning professionals too) are typically not full-stack developers or cloud engineering experts, they are PhDs in mathematics or physics. It is not that they can’t learn these cloud technologies, these are extremely smart and talented professionals. It is that they should not have to when they barely have the time to create, train, and code the model into their AI-application. Now, they are being asked to deploy it, operate it, and be on-call for it 24/7? Additionally, there has been a burst of open-source projects that sound good on the surface, but under the hood they are object-oriented - it works on my laptop but not in production - imposters that have set up the production world for failure. Anyone who has tried to do anything that is beyond chaining together a cute demo on their laptop has quickly figured out that this movement is in a bind in 2024. Waiting for other teams (if you even have them) to deploy for you isn’t practical, given the pace of research and innovation. What is missing is a platform that allows the AI Developer to do more than host a model as an API, or throw together a cute demo that will never scale.
So what’s the solution?
How about a platform that doesn’t require cloud expertise, but instead meets data scientists and AI/ML app developers where they are (python APIs to describe an app, then just typing “deploy”)? A service that combines all the necessary cloud-to-edge compute, GPUs, network, storage and data/database components into a single, managed deployment zone with global reach and regional awareness? And handles them all without you having to configure or maintain them? A solution that lets data professionals and app developers move as fast as the pace of innovation and business flux happening in the AI-world today, keeping up with the latest models and methods while truly self-serving - from quick experiment, to (CDN-style) local-everywhere, anywhere on the planet, autoscaled deployment in production, using the same workflow?
That’s Seaplane.
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