Enterprise-Grade Platform with Seph

Weights & Biases · Beginner ·📰 AI News & Updates ·3y ago

Key Takeaways

Weights & Biases announces support for Dedicated Cloud on Microsoft Azure, providing a secure, single-tenant managed environment for deploying Weights & Biases, and highlights its Enterprise-grade security controls and compliance certifications.

Full Transcript

foreign next up I'm thrilled to dive into a powerful new Enterprise capabilities for wmb and first up I want to highlight that we've recently refactored our approach to tracking distributed training jobs within our python SDK users can now easily log and then evaluate distributed training runs with wmb with multi-processing now generally available and enabled by default the user experience is significantly improved with distributed settings simply call wmb init from your training script and automatically track all of your distributed training runs from single or many processes and do this without sacrificing performance or the robustness of your jobs easy grouping via our runs table allows you to seamlessly explore data from your multi-node training jobs use our industry-leading IMO visualizations to find exactly what you need from your distributed jobs really cementing wmb as the leading Enterprise experiment tracking solution our approach to multi-processing significantly improves the user experience and it ensures the reliability and robustness to errors or hanging jobs of your distributed training runs to learn more about our approach to multi-processing I encourage everyone to check out the guide that's linked at the bottom of this slide and you can go through in detail examples and understand exactly how our refactoring has benefited you our core users I'm also really excited to announce wnb dedicated Cloud for azure the wmb dedicated Cloud provides a secure and flexible managed private Cloud environment enabling Enterprise ml Ops at scale and with this release we've added support from Microsoft azure so now our dedicated Cloud supports all three major Cloud providers Azure gcp and AWS just as a reminder we offer several deployment options for the wmb platform you can deploy on bare metal servers in your on-premise data centers configure a customer managed production deployment on your own private cloud we offer secure access via the multi-tenant public Cloud as software as a service and then finally with the wmb dedicated Cloud as a managed single tenant Cloud infrastructure providing you with your choice of cloud provider and Cloud region with the wmb dedicated Cloud we offer the infrastructure management and we're responsible for the platform performance while giving you the user's full control over your data with our Secure Storage connector and this Secure Storage connector enables you to connect and manage your own secure object storage it's with this configuration that we really unlock Enterprise grade security controls providing isolation guarantees that are aligned with even the most stringent security protocols while we provide you the guarantees of ensuring uptime of your mission critical ml infrastructure collectively the wmb cloud is trusted by some of the largest and even the most regulated companies in the world to deliver ml Ops at scale and we're really proud to now make it available um on Microsoft azure and throughout today's webinar you may have noticed a common theme that's at weights and biases we're really dedicated to meeting customers where they are and with more and more Enterprises relying on Microsoft Enterprise applications and services we added a single sign-on authentication with Microsoft so in addition to supporting standard identity management tools like open ID or active directory customers can use third-party SSO providers like Google and GitHub to authenticate users for wmb access logging into wmb with SSO authentication provides a really seamless experience for users um with Enterprise authentication providers and we're thrilled to now offer authentication with Microsoft with Azure single sign-on we we are providing a trouble-free and trusted access to wmb with a single Microsoft login and that's without sacrificing any security so it's with this release users can manage authentication with Microsoft which is really a trusted standard with Enterprise proven Authentication this means that when you as users invite others to collaborate on the wmb application as long as they have a Microsoft account they can automatically sign in and create a wmb account without further it configuration and speaking about security wmb is trusted by some of the largest uh companies in the world to deliver in my opposite scale customers from around the world they entrust us with their sensitive Mission critical ml data and really nothing is more important to us than this I've been honoring this custodial commitment to protect all of their mission critical information So today we're really proud to highlight our security position with the wmb security and compliance Center um we encourage you to visit security.wmb.ai to review details of several of our security Frameworks our regulation alignment and certifications that apply not only to our company but also to our full Suite of products and our deployment options the wmb security Center is uh it's a centralized self-service portable and it's it's made with Enterprise customers in mind for fast and efficient evaluation of our Enterprise security readiness within the security Center we ensure that our customers have all the information they need for fast compliance to Common industry security standards so again we encourage everyone to visit this portal linked here on screen by going to security.wmb.ai all right all right [Music] foreign [Music]

Original Description

W&B Product Manager Seph Mard announces support for Dedicated Cloud on Microsoft Azure and explains why customers around the world entrust sensitive data to W&B. W&B Dedicated Cloud provides a secure, single-tenant managed environment for deploying Weights & Biases, and the W&B Security Portal (security.wandb.ai) provides a centralized hub to access all W&B security frameworks and certifications. --- This video is Part 3 of a three-part mini series 🎥 Part 1 is an introduction to W&B Models with Carey Phelps, Product Lead and W&B Launch with Igor Veksler, Product Manager https://youtu.be/NCKPxVX5aWY Part 2 is a highlight of recent product updates with Stacey Svetlichnaya, Deep Learning Engineer https://youtu.be/sZd0H9Do74s Watch the full recording below 👇 https://youtu.be/kblzEBtIiuc --- Let's stay connected! 📍 Twitter: http://twitter.com/weights_biases 📍 Linkedin: https://www.linkedin.com/company/weights-biases 📍Get started with W&B today for free: http://wandb.me/intro
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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
Weights & Biases
9 5. Sentiment Analysis
5. Sentiment Analysis
Weights & Biases
10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
Weights & Biases
11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
Weights & Biases
12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
Weights & Biases
13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
Weights & Biases
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
Weights & Biases
17 10. Seq2Seq Models
10. Seq2Seq Models
Weights & Biases
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
Weights & Biases
21 14. Data Augmentation | Keras
14. Data Augmentation | Keras
Weights & Biases
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)
Weights & Biases
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
Weights & Biases
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
Weights & Biases
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
Weights & Biases
45 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
Weights & Biases
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
Weights & Biases
48 Rachael Tatman — Conversational AI and Linguistics
Rachael Tatman — Conversational AI and Linguistics
Weights & Biases
49 Reformer by Han Lee
Reformer by Han Lee
Weights & Biases
50 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
Weights & Biases
51 GitHub Actions & Machine Learning Workflows with Hamel Husain
GitHub Actions & Machine Learning Workflows with Hamel Husain
Weights & Biases
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
Weights & Biases
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
Weights & Biases
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
Weights & Biases
58 Made with ML - Goku Mohandas
Made with ML - Goku Mohandas
Weights & Biases
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

Weights & Biases announces support for Dedicated Cloud on Microsoft Azure, providing a secure environment for deploying AI solutions, and highlights its Enterprise-grade security controls and compliance certifications.

Key Takeaways
  1. Deploy Weights & Biases on Microsoft Azure using Dedicated Cloud
  2. Configure single sign-on authentication with Microsoft
  3. Implement Enterprise-grade security controls
  4. Review compliance certifications and security frameworks
💡 Weights & Biases provides a secure and flexible managed private Cloud environment for deploying AI solutions, with Enterprise-grade security controls and compliance certifications.

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