Tuesday, January 23, 2024

g-f(2)1883 From Concept to Brilliance: Unleashing the Power of Machine Learning with bizML


genioux Fact post by Fernando Machuca and ChatGPT


In the fast-paced realm of artificial intelligence, machine learning (ML) stands out as a transformative force with the potential to address some of humanity's most pressing challenges. However, despite its immense promise, many ML initiatives outside of tech giants often falter, stumbling at the crucial deployment stage. Eric Siegel's HBR article, "Getting Machine Learning Projects from Idea to Execution," delves into the challenges hindering ML projects and presents a comprehensive six-step practice named "bizML" to guide these projects from conception to deployment.

genioux GK Nugget:

"Unlocking the true value of machine learning requires a shift from fascination with its technology to meticulous planning for its deployment, where organizational change becomes the true measure of success." — Fernando Machuca and ChatGPT

genioux Foundational Fact:

The success of ML lies not solely in its algorithms but in its ability to effect organizational change and actively improve operations. The gap between data scientists and business stakeholders, often due to a misplaced focus on technology rather than deployment, poses a significant obstacle. The remedy is the bizML practice, emphasizing rigorous planning for deployment from the project's inception.

10 Most Relevant genioux Facts:

  1. ML's Popularity Paradox: The widespread hype around ML often leads to the misconception that the technology's intrinsic value lies solely in its algorithms, neglecting the crucial aspect of operational improvement through deployment.
  2. The ML Fallacy: The value of ML is fully realized when it brings about organizational change. Until a model actively reshapes how an organization works, it remains useless.
  3. Bridge the Culture Gap: Failure to bridge the business/tech culture gap results in data scientists delivering viable models that operational teams are unprepared to deploy.
  4. BizML Framework: The six-step bizML framework (Establish Deployment Goal, Prediction Goal, Evaluation Metrics, Prepare Data, Train Model, Deploy Model) provides a strategic path to successful ML deployment.
  5. Business Magic Before Technical Magic: The first three steps of bizML constitute a "preproduction" phase, involving pitching, socializing, and collaborative planning to define how ML will be deployed and evaluated.
  6. Operationalizing ML: Rigorously planning for deployment from project inception is essential for operationalizing ML and realizing its potential benefits.
  7. Deployment Goal Definition: Clearly defining the business value proposition and how ML will affect operations is the initial step in the bizML framework.
  8. Prediction Goal Precision: A detailed definition of what the ML model will predict for each case, emphasizing the business perspective, is crucial.
  9. Evaluation Metrics for Success: Determining benchmarks and performance levels defining project success during training and deployment stages is integral.
  10. Continuous Model Maintenance: Post-deployment, a model requires ongoing monitoring, maintenance, and periodic refreshment to ensure sustained success.


BizML offers a beacon of guidance in navigating the complexities of ML deployment. Organizations can unlock the true potential of machine learning by prioritizing meticulous planning, collaborative efforts, and a focus on operational change. The journey doesn't end with deployment but extends into continuous improvement, marking a new phase in running enhanced operations. As businesses embrace the bizML framework, a universal procedure facilitating collaboration and planning, they stand poised to capture untapped value and usher in a new era of ML success.


The GK Article

Eric Siegel, Getting Machine Learning Projects from Idea to Execution, Harvard Business Review, January 23, 2024.

Bing Chatbot's summary:

The Harvard Business Review article "Getting Machine Learning Projects from Idea to Execution" by Eric Siegel presents a six-step practice for successfully implementing machine learning projects¹. Despite machine learning being a crucial technology, its deployment is often challenging and many initiatives fail to realize value¹. The article addresses this issue by introducing a specialized business practice suitable for wide adoption¹.

The six-step practice aims to guide machine learning projects from conception to deployment, serving both business professionals and data professionals¹. It provides a strategic framework that is currently lacking in the field¹. The disciplined approach empowers business professionals by giving them a clear path to follow, while also providing data professionals with a structured framework to guide their work¹.

The article emphasizes that machine learning has the potential to help us navigate significant risks and boost various sectors, including sales, healthcare, and manufacturing¹. However, the current challenge lies in the deployment of machine learning initiatives, which is where Siegel's six-step practice comes into play¹.

In summary, the article provides a practical guide for successfully executing machine learning projects, emphasizing the importance of a disciplined and structured approach¹.

Source: Conversation with Bing, 1/24/2024

(1) Getting Machine Learning Projects from Idea to Execution. https://hbr.org/2024/01/getting-machine-learning-projects-from-idea-to-execution.

(2) Generative AI in the pharmaceutical industry: Moving from hype to .... https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality.

(3) Getting Machine Learning Projects from Idea to Execution. https://store.hbr.org/product/getting-machine-learning-projects-from-idea-to-execution/H07YL7.

Eric Siegel

Eric Siegel, Ph.D., is a renowned figure in the field of machine learning. He is a leading consultant and a former professor at Columbia University, where he specialized in machine learning and artificial intelligence¹. Siegel has dedicated his career to helping companies deploy machine learning solutions, bridging the gap between technology and business¹.

He is the founder of the long-running Machine Learning Week conference series, which has served more than 17,000 attendees since its inception in 2009³⁴. This conference series is a testament to Siegel's commitment to fostering a community of learning and innovation in the field of machine learning³⁴.

In addition to his work as a consultant and conference organizer, Siegel is also a frequent keynote speaker, sharing his insights and expertise at various events and platforms¹³⁴. He is the executive editor of The Machine Learning Times, further demonstrating his dedication to disseminating knowledge and promoting advancements in machine learning¹³⁴.

Siegel is also an accomplished author, having written the bestselling book "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die", which is used in courses at hundreds of universities¹. His work has been featured in numerous prestigious publications, including The New York Times, The Wall Street Journal, and Harvard Business Review¹.

In summary, Eric Siegel, Ph.D., is a pivotal figure in the machine learning community, contributing through his consulting work, conference series, keynote speeches, editorial role, and written works¹³⁴.

Source: Conversation with Bing, 1/24/2024

(1) Eric Siegel's Home Page: Machine Learning Events, Training, Book. http://www1.cs.columbia.edu/~evs/.

(2) About Eric Siegel, Author of Predictive Analytics. https://www.predictiveanalyticsworld.com/book/about.php.

(3) Founder at Predictive Analytics World - SAS Blogs. https://blogs.sas.com/content/author/ericsiegel/.

(4) Chapter Notes for Predictive Analytics by Eric Siegel. https://www.predictiveanalyticsworld.com/book/pdf/Chapter%20Notes%20for%20Predictive%20Analytics%20by%20Eric%20Siegel.pdf.

(5) undefined. http://bit.ly/1OHGggC%29.

(6) undefined. https://bing.com/search?q=.

(7) undefined. http://bigthink.com/big-think-edge/4-predictions-about-2016-provided-by-digital-data-collection.

(8) undefined. http://www.predictiveanalyticsworld.com/patimes/trick-predictive-analytics-bridge-quantbusiness-culture-gap/8177/.

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Type: Bombshell Knowledge, Free Speech

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genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)1883, Fernando Machuca and ChatGPT, January 23, 2024, Genioux.com Corporation.
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