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