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ML Stepping Stones: Challenges & Opportunities for Companies

Posted Dec 08, 2021 | Views 434
# Machine Learning
# ML Products
# Machine Learning Ecosytem
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John Crousse
Freelance Machine Learning Engineer @ Ahold Delhaize

John always liked CS/ML/AI but wasn't such a hot topic back then. He found opportunities to work on models in the Financial industry as a consultant from 2007 to 2017 then he went freelance to move outside of the financial industry, and focus on AI/ML.

John likes to do things efficiently, and MLOPs is the bottleneck, so he ended up spending more time on MLOPs than models lately.

John finished his CS degree in 2007.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Adam Sroka
Director @ Hypercube Consulting

Dr. Adam Sroka, Director of Hypercube Consulting, is an experienced data and AI leader helping organizations unlock value from data by delivering enterprise-scale solutions and building high-performing data and analytics teams from the ground up. Adam shares his thoughts and ideas through public speaking, tech community events, on his blog, and in his podcast.

Many organizations aren't getting the most out of their data and many data professionals struggle to communicate their results or the complexity and value of their work in a way that business stakeholders can relate to. Being able to understand both the technology and how it translates to real benefits is key.

Simply hiring the most capable people often isn’t enough. The solution is a mix of clear and explicit communication, strong fundamentals and engineering discipline, and an appetite to experiment and iterate to success quickly.

If this is something you’re struggling with - either as an organization finding its feet with data and AI or as a data professional - the approaches and systems Adam has developed over his career will be able to help so please reach out.

Cutting-edge data technologies are redefining every industry and adopting these new ways of working can be difficult and frustrating. One day, there will be best practices and playbooks for how to maximize the value of your data and teams, but until then Adam is eager to share his experiences in both business and data and shed some light on what works.

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SUMMARY

In this coffee session, John shares his observations after working with multiple companies which were in the process of scaling up their ML capabilities. John's observations are mostly around changes in practices, successes, failures, and bottlenecks identified when building ML products and teams from scratch. John shares a few thoughts on building long-term products vs short-term projects, on the important non-ML components, and the most common missing pieces he sees in today's ecosystem. John also elaborates on how those challenges and solutions can differ for different company sizes.

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