11:55
Understanding the Feature Store: Literal, Physical & Virtual Explained
neptune_ai
12:45
Inside Internal ML Platforms: Mailchimp's ML Team Structure
5:40
The Reality of Building ML Platform: Syncing With Business Objectives
7:04
Code Reviews in the Data Science Job Flow [With Eduardo Bonet From GitLab]
14:14
MLOps vs DevOps [With Eduardo Bonet from GitLab]
12:18
The Role of an Incubation Engineer at GitLab [With Eduardo Bonet]
9:36
LLMs and the Future of the MLOps Infrastructure Stack
5:23
GitLab’s Approach to Building an ML Platform Product
5:13
Understanding ML Model Registry: The 2024 Perspective
7:10
LLMs and Machine Learning Layoffs
4:08
Merging ML and DevOps Platform Teams
4:45
The Rise of Internal ML Platforms in 2023 and Unsolved Debate on End-to-End vs. Single Components
5:21
The Impact of AI Regulations in 2024
6:32
MLOps and LLMOps Predictions for 2024
8:42
MLOps is an Extension of DevOps, Not a Fork (a Year Later)
7:05
ML Pipeline Management Using Open-Source: DAGWorks and Hamilton
6:33
Why Go Open-Source? The Insight Story of Hamilton at Stitch Fix
4:58
The Crucial Role of Program Managers [ML Platform Team at Stitch Fix]
7:32
End-to-end ML Platform Team Structure at Stitch Fix
9:16
The Story Behind Michelangelo | ML Platform at Uber
5:02
The future of MLOps with LLMs | Mike Del Balso
12:28
Vector Databases and Feature Platforms [With Mike Del Balso From Tecton]
15:49
Michelangelo's Feature Platform [With Co-Creator, Mike Del Balso]
18:47
Real-time Machine Learning | Mike Del Balso
HRS Group's Multi-Cloud Strategy
8:47
Using LLMs to Drive Product Vision [With Olalekan Elesin]
7:15
End-to-end Platforms vs. Point Solutions [With Olalekan Elesin]
9:44
Building ML Platform at Scout 24 [With Olalekan Elesin]
5:57
Foundational Models Era | The Role of Data Scientist and ML Engineer
3:56
From Open-Source to Cloud: The Future of ZenML
LIVE
[Private video]
7:54
Integrating Specialized Vertical ML Tools With ZenML
6:57
Jobs-to-be-done in MLOps for LLMs With ZenML
7:26
The Story Behind ZenML: MLOps Framework for ML Pipelines
5:36
Navigating Machine Learning Pipelines With ZenML
3:21
Standardizing and Automating ML Processes With ZenML
4:30
ZenML’s Artifact Store vs. Experiment Tracker
8:51
Real-World Big Data Models
11:09
DoorDash's Approach to Large Language Models
5:53
Building ML Platforms for Enterprise Scale
3:55
Balancing Product Management and Engineering in ML/AI Platform Teams
8:06
Why DoorDash Built Its ML Prediction Platform
3:47
Centralized vs. Decentralized ML Platform Team Structure
3:12
Improving Internal Documentation for ML platform Components
5:01
From DevOps to MLOps to LLMOps
5:12
Big Data Evolution: 14 Years Ago vs. Now
5:03
GPU Acceleration in Vector Databases
4:47
Building a Documentation Chatbot With a Vector Database
5:58
The Problem of Updating Embeddings in Vector Databases
12:08
Vector Databases: Combining Filtering With Vector Search
7:35
Optimizing Vector Databases With Indexing Strategies
6:54
Vector Databases: Segmentation and Maintenance
Vector Databases: Combining Keyword and Vector Search
6:10
When to Implement a Vector Database
8:09
The Basics of Vector Databases With Frank Liu
