Building Well-Architected Machine Learning Solutions on AWS with Phil Basford

1

Introduction

00:00 • 6min

2

What Does It Take to Architect a Good ML Solution?

05:55 • 2min

3

In-N-Wisse Machine Learning - What's Your Thought Process?

07:37 • 2min

4

ML Engineers - What's the Difference?

09:58 • 3min

5

What's the Culture of MLOps?

13:15 • 3min

6

AWS ML Architecture

16:36 • 3min

7

Is the ML Lens on AWS on the Cloud?

19:35 • 3min

8

Sage-Making Everything So Simple, but It's Still an Investment

22:50 • 5min

9

The Cost of SageMaker

27:58 • 4min

10

The Challenges of Building AML Solutions in the Cloud

31:54 • 3min

11

AWS SageMaker Batch API

34:27 • 3min

12

What Are Some of Your Worst Theories Built in AWS, ML Solutions and AWS?

37:01 • 3min

13

The Advantages of Using SageMaker Experiments on AWS

40:04 • 2min

14

When to Productionize a Model Too Late?

42:00 • 3min

15

Egress Cost Out of AWS?

45:22 • 5min

16

AWS ML Services for Small Teams

50:16 • 1min

17

MLOps Live - What's Next?

51:46 • 2min