Scaling Enterprise ML in 2020: Still Hard! with Sushil Thomas - #429
Analysis
This article summarizes a podcast episode featuring Sushil Thomas, VP of Engineering for Machine Learning at Cloudera. The discussion centers on the challenges of scaling machine learning (ML) efforts within enterprises. Key topics include the impact of COVID-19 on business decision-making, emerging trends in scaling ML, best practices, hybridizing the engineering and scientific aspects of ML, and organizational models for ML teams. The conversation also touches upon the competition for ML talent with large tech companies. The article provides a concise overview of the podcast's content, highlighting the practical challenges and considerations for organizations adopting and expanding their ML initiatives.
Key Takeaways
- •COVID-19 has significantly impacted business decision-making.
- •Organizations are exploring best practices and hybrid approaches to scale ML.
- •Competition for ML talent is fierce, especially with large tech companies.
“The article doesn't contain a direct quote, but summarizes the discussion.”