Model CI/CD Course: Logging Models

Weights & Biases · Intermediate ·☁️ DevOps & Cloud ·2y ago

Key Takeaways

Logs models to the model registry using Weights & Biases

Original Description

This is lesson 3 of 22 in the Model CI/CD course, where we explore how to log a model to the model registry and link it from a run. *Full course with certification* and class materials available free at http://wandb.me/course_emm. *Episode Description* In this chapter of the Model CI/CD course from Weights & Biases, we learn the practical steps to log models into the model registry. The session covers code-based and UI-based methods for linking models, emphasizing the importance of documentation and best practices for managing model artifacts. *Chapter Highlights* - Importance of Documentation: Learn how to navigate and utilize the Weights & Biases documentation effectively to support your model management tasks. - Logging Models: Understand the process of logging models using Weights & Biases APIs, including creating artifacts and setting up projects. - Model Registry Linking: Explore methods to link logged models to the model registry, both through the UI and programmatically. - Using Aliases: Discover how to use aliases to manage model versions and streamline the workflow by providing semantic identifiers for different stages of the model lifecycle. *Enroll in the full course free:* http://wandb.me/course_emm *Next Chapter:* Navigating Model Registry
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Playlist

Uploads from Weights & Biases · Weights & Biases · 0 of 60

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1 0. What is machine learning?
0. What is machine learning?
Weights & Biases
2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
Weights & Biases
3 Intro to ML: Course Overview
Intro to ML: Course Overview
Weights & Biases
4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
Weights & Biases
5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
Weights & Biases
6 Weights & Biases at OpenAI
Weights & Biases at OpenAI
Weights & Biases
7 Why Experiment Tracking is Crucial to OpenAI
Why Experiment Tracking is Crucial to OpenAI
Weights & Biases
8 4. Autoencoders
4. Autoencoders
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9 5. Sentiment Analysis
5. Sentiment Analysis
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10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
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11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
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12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
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13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
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14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
Weights & Biases
15 Weights and Biases - Developer Tools for Deep Learning
Weights and Biases - Developer Tools for Deep Learning
Weights & Biases
16 Introducing Weights & Biases
Introducing Weights & Biases
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17 10. Seq2Seq Models
10. Seq2Seq Models
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18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
Weights & Biases
19 12. One-shot learning for teaching neural networks to classify objects never seen before
12. One-shot learning for teaching neural networks to classify objects never seen before
Weights & Biases
20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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21 14. Data Augmentation | Keras
14. Data Augmentation | Keras
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22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
Weights & Biases
23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Weights & Biases
24 Grading Rubric for AI Applications with Sergey Karayev  (2019)
Grading Rubric for AI Applications with Sergey Karayev (2019)
Weights & Biases
25 16. Video Frame Prediction using CNNs and LSTMs (2019)
16. Video Frame Prediction using CNNs and LSTMs (2019)
Weights & Biases
26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
Image to LaTeX - Applied Deep Learning Fellowship (2019)
Weights & Biases
27 17.  Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
Weights & Biases
28 Applied Deep Learning - Data Management with Josh Tobin (2019)
Applied Deep Learning - Data Management with Josh Tobin (2019)
Weights & Biases
29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Weights & Biases
30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Weights & Biases
31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
Weights & Biases
32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
Weights & Biases
33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
Weights & Biases
34 Building Machine Learning Teams with Josh Tobin (2019)
Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
35 Pieter Abeel on Potential Deep Learning Research Directions  (2019)
Pieter Abeel on Potential Deep Learning Research Directions (2019)
Weights & Biases
36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Weights & Biases
37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
Five Lessons for Team-Oriented Research with Peter Welder (2019)
Weights & Biases
38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
Applied Deep Learning - Rosanne Liu on AI Research (2019)
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39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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40 Organizing ML projects — W&B walkthrough (2020)
Organizing ML projects — W&B walkthrough (2020)
Weights & Biases
41 Brandon Rohrer — Machine Learning in Production for Robots
Brandon Rohrer — Machine Learning in Production for Robots
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42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Weights & Biases
43 My experiments with Reinforcement Learning with Jariullah Safi
My experiments with Reinforcement Learning with Jariullah Safi
Weights & Biases
44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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45 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
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46 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
Weights & Biases
47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
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48 Rachael Tatman — Conversational AI and Linguistics
Rachael Tatman — Conversational AI and Linguistics
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49 Reformer by Han Lee
Reformer by Han Lee
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50 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
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51 GitHub Actions & Machine Learning Workflows with Hamel Husain
GitHub Actions & Machine Learning Workflows with Hamel Husain
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52 Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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53 Jack Clark — Building Trustworthy AI Systems
Jack Clark — Building Trustworthy AI Systems
Weights & Biases
54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Weights & Biases
56 Antipatterns in open source research code with Jariullah Safi
Antipatterns in open source research code with Jariullah Safi
Weights & Biases
57 Attention for time series forecasting & COVID predictions - Isaac Godfried
Attention for time series forecasting & COVID predictions - Isaac Godfried
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58 Made with ML - Goku Mohandas
Made with ML - Goku Mohandas
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59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Weights & Biases
60 Deep Learning Salon by Weights & Biases
Deep Learning Salon by Weights & Biases
Weights & Biases

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