Saturday, October 2, 2021

g-f(2)533 THE BIG PICTURE OF THE DIGITAL AGE (10/2/2021), MIT Sloan, 7 lessons to ensure successful machine learning projects

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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence, Successful machine learning projects (10/2/2021)  g-f(2)426 


Every organization has machine learning opportunities, but finding the right team and the right uses can be a challenge, MIT Sloan

  • When Michelle K. Lee, ’88, SM ’89, was sworn in as the director of the U.S. Patent and Trademark Agency in 2015, she saw an opportunity. The agency was a bit behind on digital transformation and adopting things like cloud computing and artificial intelligence, but the organization had mountains of data — like more than 10 million patents the office has issued since opening in 1802, and 600,000 patent applications received each year.
  • Lee led a project to use data and analytics to modernize the agency, such as implementing AI solutions to improve patent searches and the speed and quality of patents issued. By gathering data about how patent examiners make decisions, and determining outlying behavior, the office could also pinpoint areas in which examiners would benefit from targeted training.

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              Her insights on how to ensure successful machine learning projects, MIT Sloan

                1. Make sure you have easy access to necessary data — and a comprehensive data strategy
                2. Carefully select machine learning use cases, and set success metrics
                3. Make sure technical experts and domain experts work side by side
                4. Ensure executive sponsorship and a culture of experimentation
                5. Assess and address any skills gaps
                6. Free your team from unnecessary heavy lifting and invest in the right infrastructure
                7. Plan for the long term

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                Opportunity, She’s seen businesses in a wide range of industries successfully using machine learning, MIT Sloan

                • Lee, who is now the vice president of machine learning at Amazon Web Services and a full-term member of the MIT Corporation, said she’s seen businesses in a wide range of industries successfully using machine learning. She’s also seen some common stumbling blocks, like businesses struggling to find the best use cases for machine learning, businesses failing to have easy access to their data, and businesses lacking necessary technical talent and expertise.
                • Adopting machine learning is not a “one and done” project, Lee said. Machine learning models need to be updated, retrained, and maintained as data changes. Companies shouldn’t think about implementing everything at once — instead start with a small project, show results, get buy-in, and work toward broader goals.
                • Leaders should also set the right expectations — and get started right away. “It's a long-term investment, and be tolerant of initial under-performance because it can produce better results for you in the end,” Lee said. “Given that machine learning requires data gathering, data cleansing, and prep, given that machine learning requires experimentation and a longer-term horizon … the time to get started is now.”

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                    • Category 2: The Big Picture of the Digital Age
                    • [genioux fact deduced or extracted from geniouxfacts]
                    • This is a “genioux fact fast solution.”
                    • Tag Opportunities those travelling at high speed on GKPath
                    • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
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                      • Inherited from sources + Supported by the knowledge of one or more experts.


                    “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)533, Fernando Machuca, October 2, 2021, Corporation.

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