53 min

HockeyStick #11 - MLOps essentials HockeyStick Show

    • Entrepreneurship

The Essentials of MLOps: With Eric Riddoch
Join Miko Pawlikowski as he dives into the world of MLOps with Eric Riddoch, a machine learning platform engineer and MLOps practitioner. In this episode, they discuss the differences between MLOps, DevOps, and platform engineering, tools and practices in MLOps, as well as Eric's journey into the field from studying applied math to becoming an MLOps expert. They explore automated workflows, experiment tracking, model serving, and monitoring, while considering the evolving landscape of MLOps and the challenges of integrating various tools. Tune in for an in-depth look at the technical and non-technical aspects of MLOps, and learn why this field is critical and exciting.
00:00 Introduction to MLOps
01:20 Eric Riddoch's Journey into MLOps
08:12 The Emergence of MLOps
10:23 Comparing MLOps and DevOps
10:53 Challenges in MLOps
21:15 Tools and MLOps Maturity
25:57 Building an ML Platform with Orchestrators
26:35 Experiment Tracking and Model Performance
27:08 ML Flow and Alternatives
29:18 Serving Models with BentoML
31:49 Challenges with SageMaker and GPU Quotas
32:54 Monitoring Tools and Their Limitations
36:48 The PyTorch vs TensorFlow Debate
42:41 Challenges in MLOps Roles and Leadership
50:42 Advice for Aspiring MLOps Engineers

The Essentials of MLOps: With Eric Riddoch
Join Miko Pawlikowski as he dives into the world of MLOps with Eric Riddoch, a machine learning platform engineer and MLOps practitioner. In this episode, they discuss the differences between MLOps, DevOps, and platform engineering, tools and practices in MLOps, as well as Eric's journey into the field from studying applied math to becoming an MLOps expert. They explore automated workflows, experiment tracking, model serving, and monitoring, while considering the evolving landscape of MLOps and the challenges of integrating various tools. Tune in for an in-depth look at the technical and non-technical aspects of MLOps, and learn why this field is critical and exciting.
00:00 Introduction to MLOps
01:20 Eric Riddoch's Journey into MLOps
08:12 The Emergence of MLOps
10:23 Comparing MLOps and DevOps
10:53 Challenges in MLOps
21:15 Tools and MLOps Maturity
25:57 Building an ML Platform with Orchestrators
26:35 Experiment Tracking and Model Performance
27:08 ML Flow and Alternatives
29:18 Serving Models with BentoML
31:49 Challenges with SageMaker and GPU Quotas
32:54 Monitoring Tools and Their Limitations
36:48 The PyTorch vs TensorFlow Debate
42:41 Challenges in MLOps Roles and Leadership
50:42 Advice for Aspiring MLOps Engineers

53 min