November 9, 2017

Bonsai + TensorFlow: The Value of an Abstraction Layer for Building Industrial AI

TensorFlow is unquestionably a powerful tool for training AI models and a crucial part of the emerging AI ecosystem. While there’s a steep learning curve for developers without extensive math backgrounds, TensorFlow is a great resource for data scientists who are capable of programming at the low-level to set up the neural network for AI models.

But setting up a neural network is only the beginning.

How you structure your network will impact its ability to learn certain tasks.

The network’s layers and hyperparameters will also impact its ability to learn. If you make a mistake at this point, you might not even realize it, but you will definitely feel the pain later on.

There’s a lot to manage when you’re programming AI models – a veritable spaghetti bowl of messy code. TensorFlow will spit out plenty of code, but it won’t really help you organize, manage and maintain it.

If you’re dealing with complex problem spaces like robotic assembly lines, commercial HVAC systems or wind turbine farms, you’re going to need a solution that’s built to handle Industrial AI.

Bonsai + TensorFlow

Bonsai tackles this problem. The Bonsai Platform is an abstraction layer that sits on top of TensorFlow and automatically takes care of the low-level chores that will come back to haunt you if they’re not handled properly from the beginning.

Let’s say you’re operating a 100-turbine wind farm and your goal is optimizing its energy output across a wide variety of continuously changing weather conditions. Each of those 100 turbines has six variable settings, such as blade pitch and head yaw, which gives you 600 “knobs” to adjust for producing optimal power at any given moment.

If you were using TensorFlow alone, the problem would become unmanageable very quickly. With Bonsai, however, you are able to input your control actions, weather conditions and objectives using Inkling, our specialized programming language for Machine Teaching. Bonsai automatically creates a teaching plan based on your inputs and then our AI engine generates a tuned neural network wrapped in a GPU-optimized Docker container that runs in the cloud and connects via API or Python SDK.

TensorFlow is at the bottom of that stack, largely invisible to users. That means you don’t need teams of highly specialized data scientists manually training your AI models. By streamlining and automating the process, it radically reduces the time and energy required to get the most value from your AI investments.

Bonsai makes it simple for analysts and operators to generate optimal neural networks and create usable Industrial AI, ready to tackle real-world problems in complex physical environments.

To learn more about best use cases for Bonsai + TensorFlow’s Machine Teaching approach, download our whitepaper, “AI for  Industrial Applications”. You can also get started building deep reinforcement learning models for your own control systems with the Bonsai Platform.

Always. Be. Learning.

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