Substack Week 4: Have an ML strategy… revisited

The post for week 4 is now up and live.

Welcome to the 4th post in this ongoing Substack series. This is the post where I’m going to go back and revisit two very important machine learning questions. First, I’ll take a look back at my answers to the question, “What exactly is an ML strategy?” Second, that will set the foundation to really dig in and answer a question about, “Do you even need an ML strategy?” Obviously, the answer to the question is a hard yes and you know that without question or hesitation. 

1. What exactly is an ML strategy?

As you start to sit down and begin the adventure that is linking budget line items to your machine learning strategy it will become very clear that some decisions have to be made.[1] That is where you will find that your machine learning strategy has to be clearly defined and based on use cases with solid return on investment. Otherwise your key performance indicators that are directly tied back to those budget line items are going to show performance problems. Being planful helps make sure things work out. 

Over the last couple of weeks this Substack series “The Lindahl Letter” has dug into various topics including machine learning talent, machine learning pipelines, machine learning frameworks, and of course return on investment modeling. Now (like right now) it is time to dig into your ML strategy. Stop reading about it and just start figuring out how to do it. Honestly, I held off on this post until we had some foundational groundwork setup to walk around the idea conceptually and kick the tires on what your strategy might actually look like. No matter where you are in an organization from the bottom to the top you can begin to ideate and visualize what could be possible from a machine learning strategy. Maybe start with something simple like a strategy statement written in a bubble located in the middle of a piece of paper and work outward with your strategy. That can help you focus in on the part to a data driven machine learning strategy based on a planful decision-making process.[2]

Part of your machine learning strategy must be about purpose, replication, and reuse. That is going to be at the heart of getting value back for the organization. Definable and repeatable results are the groundwork to predictable machine learning engagements. Machine learning is typically applied in production systems as part of a definable and repeatable process. That is how you get quality and speed. You have to have guardrails in place that keep things within the confines of what is possible for that model. Outside of that you must be clear on the purpose of using machine learning to do something for your organization. That strategy statement could be as simple as locate 5 use cases where at scale machine learning techniques could be applied in a definable and repeatable way.

Maybe your strategy starts out with a budget line item investing in the development of machine learning capabilities. Investment in training happens every year and is a pretty straightforward thing to do. Now you have part of it tagged to machine learning. From that perspective you could be walking down a path where you are doing it purely for employee engagement, because the team just really wants to do something cool and wants to leverage new technology. You may find yourself in a situation where the team really wants to do it and you can make that happen. Sure, they might figure out a novel way to use that energy and engagement to produce something that aligns to the general guiding purpose of the organization. Some of that is where innovation might drive future strategy, but it is better to have your strategy drive the foundations of how innovation is occurring in the organization. A myriad of resources about strategy exist and some of them are highly targeted in the form of online courses.[3]

From a budget line item to actually being operationalized you have to apply your machine learning strategy in a uniform way based on potential return on investment. After you do that you will know you are selecting the right path for the right reasons. Then you can begin to think about replication of both the results and process across as many applications as possible. Transfer learning both in terms of models and deployments really plays into this and you will learn quickly that after you figured out how to do it with quality and speed that applying that to a suite of things can happen much quicker. That is the power of your team coming together and being able to deliver results. That is why going after 

2. Do you even need an ML strategy?

Seeing the strategy beyond trees in the random forest takes a bit of perspective. Sometimes it is easier to lock in and focus on a specific project and forget about how that project fits into a broader strategy. Having a targeted focused ML strategy that is applied from the top down can help ensure the right executive sponsorship and resources are focused on getting results. Instead of running a bunch of separate efforts that are self-incubating it might be better to have a definable and repeatable process to roll out and help ensure the same approach can be replicated in cost effective ways for the organization. That being said… of course you need an ML strategy. 

Maybe an example of a solid ML strategy might be related to a cost containment or cost saving program to help introduce assistive ML products to allow a workforce to do things quicker with fewer errors. Executing that strategy would require operationalizing it and collecting data on the processes in action to track, measure and ensure positive outcomes.


[1] Check out this article from February 2020 about KPIs and budgets 

[2] Interesting blog post from AWS 

[3] Here is an example of a course lecture you can freely watch right now 

What’s next for The Lindahl Letter?

  • Week 5: Let your ROI drive a fact-based decision-making process
  • Week 6: Understand the ongoing cost and success criteria as part of your ML strategy
  • Week 7: Plan to grow based on successful ROI
  • Week 8: Is the ML we need everywhere now? 
  • Week 9: What is ML scale? The where and the when of ML usage
  • Week 10: Valuing ML use cases based on scale
  • Week 11: Model extensibility for few shot GPT-2
  • Week 12: Confounding within multiple ML model deployments
  • Week 13: Building out your ML Ops 
  • Week 14: My Ai4 Healthcare NYC 2019 talk revisited
  • Week 15: What are people really doing with machine learning?

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed reading this content, then please take a moment and share it with a friend. 

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