02:04

How to Version Data with DVC on DagsHub

01:52

How to Connect a Storage Bucket to DagsHub

02:18

How to Upload Data to DagsHub

02:44

How to Create or Connect a DagsHub Repository

35:32

📡 Building Scalable ML Models with Natanel Davidovits

50:38

💼 AI in the Enterprise with Jeremie Dreyfuss

03:36

Open Source Auto-labeling with custom MLflow Models and Label Studio

08:40

Curating and Validating Machine Learning Datasets

50:46

🌲 Machine Learning in Agriculture: Scaling AI for Crop Management with Dror Haor

39:35

📊 Data-Driven Decisions: ML in E-Commerce Forecasting with Federico Bacci

39:25

🚗 Driving Innovation: Machine Learning in Auto Claims Processing

50:26

🚑 ML in the Emergency Room with Ljubomir Buturovic

01:02:55

🌊 AI-Native with Idan Gazit – The future of AI products and interfaces + Getting AI to production

32:46

🍪 Machine Learning in the cookie-less era with Uri Goren

01:05:41

🛰️ Modern & Realistic MLOps with Han-chung Lee

24:36

Deploying ML for free on AWS – A DagsHub Community Webinar

58:49

🩻 AI in Medical Devices & Medicine with Mila Orlovsky

53:40

⏪ Making LLMs Backwards Compatible with Jason Liu

01:11:38

🔴 Live MLOps Podcast – Building, Deploying and Monitoring Large Language Models with Jinen Setpal

00:28

Live MLOps Podcast Episode!

01:02:07

⛹️‍♂️ Large Scale Video ML at WSC Sports with Yuval Gabay

02:12

DagsHub Data Engine: Product Overview

12:27

Data Engine Demo: Monocular Depth Estimation

53:50

DagsHub Learning: Automate Your Labeling Process

58:31

DagsHub Learning: Model Registry and Deployment on AWS services with MLflow

01:05:42

DagsHub Learning: Version and Stream Data with DVC and DagsHub

01:05:41

🤖 GPTs & Large Language Models in production with Hamel Husain

01:04:44

DagsHub Learning: Experiment Tracking for Machine Learning with MLflow

56:06

🫣 Is Data Science a dying job? with Almog Baku

01:17:18

DagsHub Learning: Model Registry and Deployment with MLflow

56:57

🏃‍♀️Moving Fast and Breaking Data with Shreya Shankar

58:48

DagsHub Learning: Experiment Tracking for Machine Learning with MLflow

50:12

🚴‍♀️ Quick & Dirty Machine Learning with Noa Weiss

46:24

Generative AI: Using ChatGPT and Stable Diffusion to Create Comic Strips

24:47

Automate the labeling process with Label Studio and DagsHub

23:37

DagsHub integration with Label Studio - Demo

49:26

Automate the Labeling Process with Label Studio and DagsHub

01:18:08

✍️ Building ML Teams and Platforms with Assaf Pinhasi

01:14:17

🎨 Stable Diffusion and generative models with David Marx

01:14:23

Version and Stream data with DVC and DDA

01:01:04

🔴🟢🟣Julia Language in Production with Logan Kilpatrick

07:56

Direct Data Access - Stream DVC versioned Data using "Hooks"

01:10:14

MLflow Crash Course - Model Registry and Model Deployment

01:20:56

🛠 Building tools for MLOps with Guy Smoilovsky

02:27

The DagsHub Integration with MLflow

01:08:43

MLflow Crash Course - What is MLflow & MLflow Tracking

01:11:00

📈 You Have Too Much Data with Dean Langsam

16:17

Reproducibility Challenge: Re-implementation of CheXNet using TensorFlow

03:43

3. Track Experiments – Get Started with DagsHub

05:17

4. Explore a New Hypothesis – Get Started with DagsHub

01:20:48

🏗 Reasonable Scale MLOps with Jacopo Tagliabue

07:23

2. Version Code & Data – Get Started with DagsHub

05:04

1. Overview and Create a Project on DagsHub

01:29:21

🦾 Made With ML - Learning How to Apply MLOps with Goku Mohandas

01:32:41

MLOps Workflows: Intro to GitOps for ML

58:17

🤹‍♀️ Building models that actually perform with Kyle Gallatin – DagsHub

02:13

DagsHub Connect: Connect Repositories from GitHub to DagsHub

01:01:02

💬 MLOps for NLP Systems with Charlene Chambliss – DagsHub

07:19

DagsHub Connect Demo - Connect GitHub Repository to DagsHub

01:03:41

🧩 Simplifying Complex Ideas with Yannic Kilcher