How Enterprises Can Build Data Science and AI Teams with Beth Partridge
Beth Partridge is the CEO, Founder and Chief Data Scientist of Milk+Honey, a company that creates and supports an environment in which data scientists and business professionals can learn from one another, develop common understandings and goals, and advance both business and the human experience. Beth brings nearly 30 years of executive-level experience in manufacturing, product engineering, quality control, technical support and operations. Her formal training includes a BS in Electrical Engineering, and a Master of Information and Data Science from UC Berkeley.
Beth Partridge’s LinkedIn: https://www.linkedin.com/in/beth-partridge-b382673/
Beth Partridge’s Twitter: https://twitter.com/bretgreenstein?s=20
Beth Partridge’s Website: https://milkandhoney.ai/
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Here’s the timestamps for the episode:
(00:00) – Introduction
(03:16) – Milk+Honey helps bridge the gap between business and data science for the rest of the world. There’s confusion starting with job titles, how to organize teams too, really what data science means in terms of organizational structure requirements and cultural change requirements.
(04:47) – Milk+Honey has created their own internal, very detailed profiling tool. They cross-reference candidates’ toolset and the roles that they say they do on their projects and the whole package in order to really figure out who’s who.
(05:37) – There’s a complete lack of understanding about who’s going to do what. You can have the best data scientists in the whole planet and the most committed C-suite willing to put whatever resources they have into making the transition to adopting enterprise AI. And if you don’t have somebody in the middle, then it’s still not going to work.
(07:10) – Most companies don’t even have data science teams. Many have tried, most are trying at a project level, but data science takes cross-functional teams, commitment from the top and the cultural stuff.
(08:46) – If somebody has enough confidence and understanding of the business and confidence in the models themselves, then as you get more data, the right data, move to a different kind of model and the confidence is constantly growing, but there’s not that bridge in between.
(10:05) – The Data Strategist: somebody that understands the business, but then understands machine learning enough to understand the different types of approaches and what it means in terms of risk and accuracy.
(13:25) – We need people that understand the business and understand machine learning enough to make the connections and to really be that catalyst. And then we need to create coursework in serious applications of machine learning and business.
(15:34) – The emergence of segments such as the term “data engineering” is starting to stick. But the more catalyst role of applied data science is still missing. It hasn’t really been broadly recognized and we need to find a way to describe what it is and label it.
(17:00) – There’s some debate about the certification programs and the bootcamp programs and how effective those are. You really do need to have some understanding of business in order to effectively do the job.
(19:25) – The traditional question of make versus buy: you can’t take advantage of buying software unless you have somebody that’s doing the strategic plan that understands those different levels of expertise.
(19:57) – 80% of building a machine learning model is data wrangling. And there’s such an opportunity to bring in young data scientists to assist with. Stretch machine learning resources further while training younger data scientists with practical experience.
(21:59) – ML productivity tools help make easy, quick and dirty feasibility analysis. You don’t get a finished model, but you figure out how to approach it algorithmically.
(23:13) – Check the for cultural holders, figure how you’re going to implement it and sit down and understand what resources are necessary for a data science team to be successful. There has to be the business domain expertise, the machine learning expertise and the data engineering expertise.
(29:32) – Get the education, get the training, get solid on at least your machine learning basics, and then find a job at a company that’s next to data science.
(33:29) – Python is the machine learning language of choice for sure.