Summary
Machine learning is a class of technologies that promise to revolutionize business. Unfortunately, it can be difficult to identify and execute on ways that it can be used in large companies. Kevin Dewalt founded Prolego to help Fortune 500 companies build, launch, and maintain their first machine learning projects so that they can remain competitive in our landscape of constant change. In this episode he discusses why machine learning projects require a new set of capabilities, how to build a team from internal and external candidates, and how an example project progressed through each phase of maturity. This was a great conversation for anyone who wants to understand the benefits and tradeoffs of machine learning for their own projects and how to put it into practice.
Introduction
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Kevin Dewalt about his experiences at Prolego, building machine learning projects for Fortune 500 companies
Interview
- Introduction
- How did you get involved in the area of data management?
- For the benefit of software engineers and team leaders who are new to machine learning, can you briefly describe what machine learning is and why is it relevant to them?
- What is your primary mission at Prolego and how did you identify, execute on, and establish a presence in your particular market?
- How much of your sales process is spent on educating your clients about what AI or ML are and the benefits that these technologies can provide?
- What have you found to be the technical skills and capacity necessary for being successful in building and deploying a machine learning project?
- When engaging with a client, what have you found to be the most common areas of technical capacity or knowledge that are needed?
- Everyone talks about a talent shortage in machine learning. Can you suggest a recruiting or skills development process for companies which need to build out their data engineering practice?
- What challenges will teams typically encounter when creating an efficient working relationship between data scientists and data engineers?
- Can you briefly describe a successful project of developing a first ML model and putting it into production?
- What is the breakdown of how much time was spent on different activities such as data wrangling, model development, and data engineering pipeline development?
- When releasing to production, can you share the types of metrics that you track to ensure the health and proper functioning of the models?
- What does a deployable artifact for a machine learning/deep learning application look like?
- What basic technology stack is necessary for putting the first ML models into production?
- How does the build vs. buy debate break down in this space and what products do you typically recommend to your clients?
- What are the major risks associated with deploying ML models and how can a team mitigate them?
- Suppose a software engineer wants to break into ML. What data engineering skills would you suggest they learn? How should they position themselves for the right opportunity?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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