Enterprise AI

A guide to applying machine learning in the enterprise – part 1

The core steps to solving a business problem with ML

This blog is the first in a series designed to help enterprise leaders capture the promise of machine learning—pragmatically. That means operationalizing ML to drive revenue and business results.

Machine learning (ML) is rapidly changing enterprise technology. While the hype around artificial intelligence can be daunting for those without a PhD in ML (i.e. just about everyone), successful enterprise adoption requires that teams communicate to align technical capabilities with real-world business problems. This business skillset, moreover, overlaps with digital innovation practices that have been evolving over the last 10–15 years. After all, ML has a lot in common with advanced analytics.

 The bottom line?

You don’t need an engineering degree to play a pivotal role in shaping the future of applied ML. What you do need is commitment to the first principles of good problem solving, a slightly evolved playbook based on what is new about ML, and a willingness to learn as you go. While these blog posts don’t promise to solve applied ML, we hope they’ll provide a helpful framework to kickstart your journey.

Core Steps to Solving a Business Problem with ML

Let’s start with a bird’s eye view of what it takes to apply ML in enterprise settings. If you’re working with a vendor, you won’t take all these steps in house, but the overarching principles still hold.

  1. Pick a big-ticket business problem that ML is well suited to solve.

  2. Understand, scope, and define the opportunity. Solving an enterprise problem with ML has two phases: the model and the implementation.

  3. The math (How do we build a model that predicts x)

    • The implementation (How will we integrate the predictions into the business so that the model’s output impacts business operations)

    • Think about your key questions and hypotheses to guide steps 4 and 5.

  4. Acquire and prepare the data (Warning! This may be the most important step in the process and can be incredibly time consuming.).

  5. Do some initial data discovery to ensure that there is signal in the data (i.e., enough variation to identify meaningful differences between things, or trends that might support the hypothesis you are testing) to refine the problem scope and to select the best ML algorithm (simpler is almost always better!).

  6. Build and productionize the model

  7. Integrate the model with the business (i.e., make sure that your model’s insights drive action) in a way that ensures your model is getting automatic feedback in as close to real time as possible

  8. Start with a proof of concept/experiment to test your solution

  9. Scale up implementation from proof of concept to full-scale solution (or shut it down)

  10. Maintain the model and update it as the data changes

In this series we’ll unpack each of these steps for you, starting with step one: How to identify and scope a good machine learning problem.


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