Machine Learning Demystified

Image Credits: David Malan / Getty Images

5 machine learning essentials nontechnical leaders need to understand

1.The ML Recruiting Strategy

Recruiting for ML comes with several challenges.

Image Credits: Snehal Kundalkar
  • Data: Look for candidates that can help select models, design features, handle data modeling/vectorization and analyze results.
  • Platform/Infra: Look for people who evaluate/integrate build platforms to significantly accelerate the productivity of Data and Engineering teams, ETLs, warehouse infrastructure, CI/CD framework for ML

2. Organizational Structure

How to best structure the role of the ML team within the larger organization (it’s size and whether it sits vertically or horizontally) are significant decisions that impact the efficiency and predictability of the business and that should be guided by the stage and size of the company .

3. ML Pipeline

Deploying and maintaining ML pipelines is not dramatically different from deploying and maintaining general software. ML knowledge is required around building, tuning, testing, verifying and versioning the model — as well as monitoring it.

  • Refine datasets
  • Know and isolate data issues vs Model drawbacks
  • Test, debug and version your models

4. Metrics and Evaluation

The key challenge around ML is reliability. How can you be sure your model is performing adequately before it’s deployed? How do you monitor production performance and troubleshoot issues? The solution is pretty similar to software engineering: observability.

  • Data security
  • Stability of the model
  • Practicality of the predictions and recommendations
  • Ability to explain why a model made the recommendation it did.

5. Common Pitfalls

On first read, some of these pitfalls may seem like common sense but they are worth both reiterating and reflecting on since they can help guide your team to making the best decision during a critical moment.

  • Expect instant results: impactful ML takes patience and iterations to get solid results
  • Focus on model success metrics — without enough attention to product success metrics
  • Underestimate the tooling and infrastructure costs leading to slow engineering progress

Senior Director of Engineering

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