Really digging into that content roadmap

Never forget that the gift of happiness begets more happiness. Tonight I’m spending my time working on really digging into that content roadmap that was mentioned yesterday. At the end of last year, I was working on version 12 of a talk titled, “Applied ML ROI – Understanding ML ROI from different approaches at scale.” Instead of working on the 13th version of that talk my focus has turned to working on something else titled, “The scale problem: where and when to use ML.” Outside of working on writing the bulk of that main talk for 2021 I’m focused on a few featured topics that will receive a bulk of my attention in the next few months.

1. Is the ML we need everywhere now? 
2. What is ML scale? The where and the when of ML usage
3. Valuing ML use cases based on scale
4. Model extensibility for few shot GPT-2
5. Confounding within multiple ML model deployments

That batch of fresh topics enumerated above will receive some attention this year. Over the next 7 weeks or so I’m going to work on some Substack posts based on that mythic version 12 talk mentioned above.

1. Machine Learning Return On Investment ( ML/ROI)
2. Machine Learning Frameworks & Pipelines
3. Machine learning Teams
4. Have an ML strategy…
5. Let your ROI drive a fact-based decision-making process
6. Understand the ongoing cost and success criteria as part of your ML strategy
7. Plan to grow based on successful ROI

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