The lack of data scientists, and the inevitable rise in the cost of employing them, are therefore generating serious headwinds. As a result, most engineering enterprises are missing out on unprecedented opportunities to realize new efficiencies. Businesses simply cannot afford to wait for more data scientists to emerge and are challenged with closing the skill gap.

On your podcast, Phil can uncover how to bridge the skill gap between data science and engineering. Specifically, why data science and machine learning teams can build applications without backend, frontend, or MLOps knowledge, enabling faster, smarter decision-making in their organizations. He can share how to solve all the logistics needed — instead of going from team to team to implement it, how to go from ZERO to 100 very quickly.

Early stage startups typically have limited budgets and resources, and so machine learning projects are typically de-prioritized or pushed. That’s because today, a working ML team requires several resources from the start. In addition to data scientists, teams also need DevOps or MLOps engineers, data engineers, and backend/frontend engineering resources to implement and realize the value of machine learning.  As a result, many startups avoid doing it, despite the fact that ML could be the differentiator that accelerates their business.

On your podcast, Phil can talk about a new, scalable way to build ML teams, so companies can reap the benefits of ML without taking on too much risk or upfront costs at the start. From his experience working with early-stage startups on this exact dilemma, he can explain how to think about quick wins, balancing scrappiness with scalability, and showing the value of ML on a budget.

Today, data scientists are still often viewed by business stakeholders as “data fetchers” rather than strategic thought partners. Even though data scientists have a uniquely data-driven insight into product thinking, their roadmaps are often filled with answering one-off questions and last-minute data requests. How can ML teams empower their data scientists to act more like product owners?

On your podcast, Phil can share his unique view on why data scientists should have the power and freedom to talk to business users, identify their biggest pain points, and build ML-powered solutions to solve them. Although the industry doesn’t yet broadly see the data scientist this way, Phil can explain why he thinks this is going to change. Specifically, why in the future, the more involved data scientists are in their company, the more influential they’ll become.

The number of companies adopting ML is blowing up. Companies working on software development tools are quickly developing the ability to productize AI-powered solutions for small tasks. However, the process from model to production can take months, and ML engineers aren’t made from the same cloth as other software engineers.

On your podcast, Phil can share how to take a model to a full-stack app in a matter of minutes, making it useful, available, and maintainable. Specifically, why it’s important to establish best practices from creating the model, and training the model, to realizing the value in the business. He’ll share why organizing models and adopting these practices helps to rapidly build and ship production-grade ML-powered apps.