Building Blox.ai: The xBlox Story4 min readReading Time: 3 minutes
Why we built xBlox
At Mad Street Den, we have been developing Machine Learning based software solutions for more than half a decade at this point. And in that time, we found that for all its novelty, developing with ML is no different from developing software of any kind. The unique problems we faced actually arose from the lack of standard practices and developer skills in the field. How do you recruit experienced developers in an industry that’s effectively a handful of years old? And how do you possibly establish a rapid development cycle when you have to define the standards yourself?
A shared language
When trying to answer those questions, we came up against another oddity. Outside of AI, it’s virtually superfluous to note that any commercially ambitious product needs to be able to scale seamlessly. Having any link between the number of users for your product and the number of engineers needed for your product to function correctly is an unforgiving cap imposed on your company’s growth. In an ideal world, we would build a product and deliver it to an arbitrary number of domain experts, each of whom would be able to use it to generate value. But in practice, we saw that any ML-based product needed at least one ML engineer to be assigned to work alongside the domain expert. This generated many challenges, not the least of which was the necessity of creating a shared language to ensure clear communication between the domain expert and engineer. Which, considering the fundamental unconnectedness of ML with most domains, was a tough undertaking, not to mention having to repeat the process for each new domain.
What we needed was a solution that would let us decouple this link. A system that would not only automate the ML lifecycle to make the lives of ML engineers easier but would go a few steps further and place all that power squarely in the hands of a domain expert – no strings attached. We now knew exactly what we needed. The problem? Nothing like that existed…
So we built it ourselves!
Created as a tool for which our own Retail AI product was the first customer, xBlox is a no-code and AI-powered solution, letting you journey all the way from raw data to model deployment in a single interface. If you have ten thousand points to label, your domain expert will label a few hundred points in an interactive UI, while our AI system takes care of labeling the rest. We have an integrated taxonomy management system, a robust user and task management system, annotation tooling, and much more – xBlox is built for teams, out-of-the-box.
xBlox decisively puts you in charge, so it’s not finally about automation. It’s finally about the user.
Under the hood, uninhibited by the need for human intervention, we have a library of hundreds of ML models whose combined knowledge is used to learn from every one of your interactions on screen. Additionally, we periodically train new models with your data so that you have tailor-made models that excel at your specific use case and get better with each set of data you import to the tool.
The scale needed for seamless automation of this magnitude is enabled by our internal MLOps pipeline that can serve both humans and programmatic users. In a single API call, training jobs can be launched on any public cloud, in any region, and with built-in support for distributed training across a cluster of machines so you can train hundreds of models on a single set of data and auto-tune your hyperparameters. We keep track of the generated models using our own model tracking system and enable immediate deployment to a production-ready, highly scalable inference system with a click. We can also use your taxonomy to generate a custom logical path for inference, allowing you to invoke any number of models in a single API call.
With infinitely scalable infrastructure, a large library of high-quality ML models, and an interface that creates a beautiful synergy between AI and humans, xBlox has quite a lot to offer and promises to unlock the next level of productivity for your ML workloads.
We can honestly say that building and using the tool ourselves was an immensely rewarding process, a culmination of our previous struggles, and a fitting bookend to the incomplete story of the ML lifecycle.
We can’t wait to bring xBlox to all of you, we’re sure you’re going to love it as much as we do!