The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Scaling Enterprise ML in 2020: Still Hard! with Sushil Thomas - #429
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Nov 19, 2020 
Sushil Thomas, VP of Engineering for Machine Learning at Cloudera, shares his insights on scaling enterprise ML amidst the challenges posed by the COVID-19 pandemic. He discusses how businesses have adapted decision-making processes and the growing reliance on data for effective strategies. Sushil highlights key trends in hybridizing ML engineering and scientific exploration while navigating organizational models for talent retention. His experiences from a recent roundtable reveal practical approaches to overcoming obstacles in machine learning deployment.
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ML/AI Nascency
- ML/AI is nascent in most organizations, unlike mature data warehousing.
- Teams struggle with data quality, use case identification, feedback loops, scaling, and maintenance.
Balancing ML Perspectives
- Balancing the engineering and scientific aspects of machine learning is crucial.
- Treat it as a traditional engineering task and an unstructured exploration for effective scaling.
Upskilling Data Professionals
- Upskill existing data professionals in machine learning and AI.
- Support their training and create a productive environment for practical application.