Talking Delivery Roundup #1: Machine Learning and AI Delivery

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There is a boom in machine learning (ML) and artificial intelligence (AI) initiatives right now. Even the federal government and military are taking note. With the “new normal” of remote work, this boom is here to stay. If you’ve made the decision to explore ML and AI capabilities for an initiative or about to embark on a ML and AI product journey, this article roundup is for you. 

The articles and links below are a summary of ML/AI delivery trends and best practices for executives, implementation, product and project managers. Topic headers include: 

  • What is Machine Learning? 
  • ML/AI Strategy
  • Managing Machine Learning and AI Projects
  • Use Case Studies

What is Machine Learning? 

There is a lot of frightening jargon for ML/AI: supervised versus unsupervised learning, classification models, Random Forrest versus Conditional Inference algorithms etc. This article is a good starting point to describe ML/AI concepts. It’s a little crass (i.e. curse words) but the points are solid.

Machine Learning and Artificial Intelligence Strategy

Managing Machine Learning and AI Projects

  • The Dumb Reason Why Your AI Project Will Fail: Every technology project should start with the question, “Why will this effort fail?” in order to mitigate risks up front versus after the million dollar check has been written for the effort. I’ve seen first hand how planning for operations integration is rarely taken into account for a new digital innovation initiative. This article explains the pitfalls of that: https://hbr.org/2020/06/the-dumb-reason-your-ai-project-will-fail
  • Machine Learning Best Practices in Financial Services by AWS: Want to get real nerdy? This whitepaper from AWS details best security practices for Financial Services ML initiatives in Amazon’s SageMaker. 

Use Case Studies

Published on August 14, 2020

Starr Corbin is the Founding Partner of Corbin Solutions Group. Follow her on LinkedIn and Twitter @StarrCorbin.

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