The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) ML Lifecycle Management at Algorithmia with Diego Oppenheimer - #470
Apr 1, 2021
Diego Oppenheimer, Founder and CEO of Algorithmia, shares insights on overcoming challenges in transitioning AI from theory to practice. He discusses the findings from a recent survey on AI market trends and the importance of translating analytics into actionable strategies. Diego contrasts the machine learning approaches of small versus large firms, noting how smaller businesses capitalize on rapid tech adoption. Also covered are the obstacles to deploying machine learning models, including IT and security concerns, especially in a post-pandemic landscape.
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Quick Wins for Enterprise ML
- Larger organizations should find quick wins with machine learning to showcase success.
- These wins justify further investment and demonstrate the technology's value.
From Artisanal to Industrial ML
- Machine learning and data science are currently artisanal, often involving stitching together various tools.
- The field is moving towards industrialization, focusing on repeatability, scalability, and governance.
The Importance of IT in ML Success
- True success in machine learning requires the involvement of IT, security, and DevOps teams.
- These teams ensure scalability, speed, and security, which are crucial for industrializing machine learning.