We talked about:
00:00 DataTalks.Club intro
02:34 Career journey and transition into MLOps
08:41 Dutch agriculture and its challenges
10:36 The concept of "technical debt" in MLOps
13:37 Trade-offs in MLOps: moving fast vs. doing things right
14:05 Building teams and the role of coordination in MLOps
16:58 Key roles in an MLOps team: evangelists and tech translators
23:01 Role of the MLOps team in an organization
25:19 How MLOps teams assist product teams
27 :56 Standardizing practices in MLOps
32:46 Getting feedback and creating buy-in from data scientists
36:55 The importance of addressing pain points in MLOps
39:06 Best practices and tools for standardizing MLOps processes
42:31 Value of data versioning and reproducibility
44:22 When to start thinking about data versioning
45:10 Importance of data science experience for MLOps
46:06 Skill mix needed in MLOps teams
47:33 Building a diverse MLOps team
48:18 Best practices for implementing MLOps in new teams
49:52 Starting with CI/CD in MLOps
51:21 Key components for a complete MLOps setup
53:08 Role of package registries in MLOps
54:12 Using Docker vs. packages in MLOps
57:56 Examples of MLOps success and failure stories
1:00:54 What MLOps is in simple terms
1:01:58 The complexity of achieving easy deployment, monitoring, and maintenance
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