MLOps + Machine Learning // James Sutton // MLOps Coffee Sessions #15

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠James Sutton is an ML Engineer focused on helping enterprises bridge the gap between what they have now and where they need to be to enable production-scale ML deployments.----------- Connect With Us ✌️-------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/Connect with James on LinkedIn: https://www.linkedin.com/in/jamessutton2/Timestamps:0:00 - Intro to Speaker 2:20 - Scope of the coffee session 3:10 - Background of James Sutton 8:28 - One-Shot Classifier Algorithm   12:46 - Why is it a challenge from the engineering perspective with deployment? 19:20 - How to overcome bottlenecks? 30:07 - Vision of your landscape?  34:45 - Maturity playout 38:48 - Maturity perspective of ML 41:49 - Risk of overgeneralizing system design patterns 46:10 - Reliability, Speed, Cost 46:46 - Consistency, Availability, Partition Tolerance (CAP Theorem) 47:36 - How do you go about discussing these tradeoffs with your clients? 51: 23 - How would you deal with the PII? 58:50 - Collaborative process with clients 1:00:55 - Wrap up