The Realities of ML

So, you want to build some fancy ML software. Or maybe you are planning the next unicorn, built on a foundation of ML.

Before you begin, familiarize yourself with the core realities of building and utilizing ML.

LeanML -- a better way

There is a better, faster and more efficient way to build ML products and systems that navigates the realities above.

These are the 4 central tenets of LeanML that are an extension to the Lean Startup methodology. If you’re not familiar with Lean Startup, we’d recommend starting there first.

Humans First, ML Second

All Lean Startups know that you figure out the user need first, then select the technology required. But with ML, it goes a step further: the human team must be able to conduct the entire ML task by hand, before the ML is built. If you, the human, can’t output what you’re asking an ML system to do, you’re not going to be successful.

Estimate Error & Collect User Response

There’s error in any ML system. In fact, there’s often more error than you would expect. Your choice of ML solution will be driven by the balance between different types of error produced by different technologies, and your user’s tolerance for error given the product use case. How much error will the user tolerate? What type of errors will the user tolerate?

Designing for Error

Let us repeat -- all ML systems have error! But, that’s okay, and that’s because you can plan for it. After determining user tolerance for error, your process or product perspective should include design to handle the error. For example, do you need two different ML systems, one to generate and another to rank? Do you need a human verification step into the process before showing results?

It’s probably already been built

Worldwide communities of thousands of computer scientists spend their entire careers developing machine learning algorithms, studying how they behave on different types of data, and producing open-source libraries for fast and accurate implementation. In addition, commercial platforms are available that wrap these libraries into super-easy user interfaces, complete with a few models to match your data. Your ML task isn’t unique: there is an entire background of previous research that will guide you in how you should and shouldn’t build your solution, and save you enormous amounts of time and resources.

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