This week I spoke with Lak Lakhshmanan, who worked for years at Google on ML and AI projects and products at a senior level and he also brings years of experience working on meteorology and other scientific projects previously.

Lak brings a ton of experience to the table and it was interesting to hear his suggestions around when it is and isn't appropriate to bring the full set of MLOps tools to the table, for example. We also discussed the fundamentals of doing ML-backed projects as well as the teams needed to make those projects succeed.

Special Guest: Lak Lakshmanan.

Links:

• Lak on LinkedIn
• lak lakshmanan (@lak_luster) / Twitter
• Valliappa Lakshmanan (Lak) - Home
• Lak Lakshmanan – Medium
• Amazon.com: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps: 9781098115784: Lakshmanan, Valliappa, Robinson, Sara, Munn, Michael: Books
• Amazon.com: Practical Machine Learning for Computer Vision eBook : Lakshmanan, Valliappa, Görner, Martin, Gillard, Ryan: Kindle Store
• Amazon.com: Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale eBook : Lakshmanan, Valliappa, Tigani, Jordan: Kindle Store
• Amazon.com: Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning: 9781098118952: Lakshmanan, Valliappa: Books