📌 MLOps and DevOps: Why Data Makes It Different
In this article, the O'Reilly team digs into the fundamentals of machine learning as an engineering discipline to answer key questions about MLOps, DevOps, and their evolution through data.
https://bit.ly/31ZNA51
In this article, the O'Reilly team digs into the fundamentals of machine learning as an engineering discipline to answer key questions about MLOps, DevOps, and their evolution through data.
https://bit.ly/31ZNA51
O’Reilly Media
MLOps and DevOps: Why Data Makes It Different
Machine Learning’s deployment stack is maturing
We hope your Sunday is going great and you are ready for the upcoming week!
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3ccpLbO
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3ccpLbO
📚SSAST: Self-Supervised Audio Spectrogram Transformer
In this paper, the authors aim to alleviate the data requirement issues with the AST by leveraging self-supervised learning using unlabeled data for audio and speech classification.
https://bit.ly/3qCwlky
In this paper, the authors aim to alleviate the data requirement issues with the AST by leveraging self-supervised learning using unlabeled data for audio and speech classification.
https://bit.ly/3qCwlky
⚡️Happy Monday, friends! Today we want to feature Hilary Mason, the Founder of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in residence at Accel.
Previously, she was Chief Scientist at Bitly, co-founded of HackNY, and co-hosted DataGotham. She is a member of NYCResistor. As a Data Scientist in residence at Accel, she provides consulting services to companies large and small about their data strategy.
Hilary spent four years as Chief Scientist at Bitly, where she led the team that studied attention on the internet in real time, doing a mix of research, exploration, and engineering. Also, she co-founded HackNY, a non-profit that helps talented engineering students find their way into the startup community of creative technologists in New York City.
Hilton was a member of Mayor Bloomberg’s Technology and Innovation Advisory Council, which was a great way for her to learn how government and industry can work together.
Did you know about Hilary before? Let us know what you think!😉
https://bit.ly/3ChZWSz
Previously, she was Chief Scientist at Bitly, co-founded of HackNY, and co-hosted DataGotham. She is a member of NYCResistor. As a Data Scientist in residence at Accel, she provides consulting services to companies large and small about their data strategy.
Hilary spent four years as Chief Scientist at Bitly, where she led the team that studied attention on the internet in real time, doing a mix of research, exploration, and engineering. Also, she co-founded HackNY, a non-profit that helps talented engineering students find their way into the startup community of creative technologists in New York City.
Hilton was a member of Mayor Bloomberg’s Technology and Innovation Advisory Council, which was a great way for her to learn how government and industry can work together.
Did you know about Hilary before? Let us know what you think!😉
https://bit.ly/3ChZWSz
📚The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Learn about the solution!
https://bit.ly/3qF6usi
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Learn about the solution!
https://bit.ly/3qF6usi
💡Detect industrial defects at low latency with computer vision at the edge with Amazon SageMaker Edge
Learn how to create the cloud to edge solution with Amazon SageMaker to detect defective parts from a real-time stream of images sent to an edge device. A demo included.
https://go.aws/30ny7ea
Learn how to create the cloud to edge solution with Amazon SageMaker to detect defective parts from a real-time stream of images sent to an edge device. A demo included.
https://go.aws/30ny7ea
Amazon
Detect industrial defects at low latency with computer vision at the edge with Amazon SageMaker Edge | Amazon Web Services
Defect detection in manufacturing can benefit from machine learning (ML) and computer vision (CV) to reduce operational costs, improve time to market, and increase productivity, quality, and safety. According to McKinsey, the “benefits of defect detection…
📚Alias-Free Generative Adversarial Networks
The researchers trace the root cause to careless signal processing that causes aliasing in the generator network and derive architectural changes that guarantee better results.
https://bit.ly/3qHf6ys
The researchers trace the root cause to careless signal processing that causes aliasing in the generator network and derive architectural changes that guarantee better results.
https://bit.ly/3qHf6ys
📌Training a DCGAN in PyTotch
In this tutorial, you'll learn how to train our first DCGAN Model using PyTorch to generate images. Check out Part 2 and Part 3 of the series
on Advanced PyTorch Techniques.
https://bit.ly/3qOyRUU
In this tutorial, you'll learn how to train our first DCGAN Model using PyTorch to generate images. Check out Part 2 and Part 3 of the series
on Advanced PyTorch Techniques.
https://bit.ly/3qOyRUU
PyImageSearch
Training a DCGAN in PyTorch - PyImageSearch
Learn to train a DCGAN using PyTorch and Python. This tutorial is perfect for coders comfortable with PyTorch and Generative Adversarial Networks.
⚡️Hello everyone, we hope that your day is going great! Data Phoenix team wants to remind you about our weekly newsletter which is coming tomorrow! Fill in your email and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/3nt5QMd
https://bit.ly/3nt5QMd
Data Phoenix
Data Phoenix is your best friend in learning and growing in the data world! We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI.
This media is not supported in your browser
VIEW IN TELEGRAM
Join Dr. Dean and Dr. Bazhirov on November 19th to learn more about Interpretable Machine Learning for materials research and development.
==
The exponential growth of interest in Machine Learning has led to a wealth of data in recent years, which has corresponded with an expansion in the number of techniques available. In this webinar, we benchmark a few recent techniques, including SISSO (Symbolic Regression), TPOT (AutoML), Roost (deep learning algorithms), and XGBoost (Gradient-boosting) to predict the properties of perovskites and 2dmaterials.
Register at https://bit.ly/3nuAp4h
==
The exponential growth of interest in Machine Learning has led to a wealth of data in recent years, which has corresponded with an expansion in the number of techniques available. In this webinar, we benchmark a few recent techniques, including SISSO (Symbolic Regression), TPOT (AutoML), Roost (deep learning algorithms), and XGBoost (Gradient-boosting) to predict the properties of perovskites and 2dmaterials.
Register at https://bit.ly/3nuAp4h
💡Serving ML Models in Production: Common Patterns
This article explores Ray Serve, a service combining pipelines, ensemble, business logic, and online learning for machine learning. Learn how to use the service for serving ML models in production.
https://bit.ly/3criUeG
This article explores Ray Serve, a service combining pipelines, ensemble, business logic, and online learning for machine learning. Learn how to use the service for serving ML models in production.
https://bit.ly/3criUeG
Anyscale
Serving ML Models in Production: Common Patterns | Anyscale
In this post, we explore the 4 common patterns of ML in production and how to implement these patterns using Ray Serve.
⚡️Hello everyone!
Data Phoenix team is ready to present our weekly issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3x40Q47
Data Phoenix team is ready to present our weekly issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3x40Q47
Data Phoenix
Data Phoenix Digest - ISSUE 32
NVIDIA's Omniverse and BMW, industrial data revolution, AI trends for 2022, K-Means clustering explained, AutoML, EditGAN, DScribe, CFPNet, StyleCLIPDraw, jobs, and more ...
⚡️Hello friends! We hope that your weekend is going great!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) JS Engineer (Meteor+React) at Exabyte.io - please write to d.spodarets@dataphoenix.info directly
2) Software Engineer, Machine Learning at Grammarly (San Francisco; Remote).
https://bit.ly/3oOqRQW
3) Machine Learning Engineer at Amazon (Santa Clara, California, USA).
https://bit.ly/3Fzcln6
4) Machine Learning Engineer at Twilio (Madrid, Spain).
https://bit.ly/3CMAmFz
5) Machine Learning Scientist, Core AI at Amazon (Berlin, Germany).
https://bit.ly/3CA0svf
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) JS Engineer (Meteor+React) at Exabyte.io - please write to d.spodarets@dataphoenix.info directly
2) Software Engineer, Machine Learning at Grammarly (San Francisco; Remote).
https://bit.ly/3oOqRQW
3) Machine Learning Engineer at Amazon (Santa Clara, California, USA).
https://bit.ly/3Fzcln6
4) Machine Learning Engineer at Twilio (Madrid, Spain).
https://bit.ly/3CMAmFz
5) Machine Learning Scientist, Core AI at Amazon (Berlin, Germany).
https://bit.ly/3CA0svf
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info
📌Solving Math Word Problems
Learn about a system trained by the OpenAI team that solves grade school math problems with twice the accuracy of a fine-tuned GPT-3 model. It solves ~90% as many problems as real kids.
https://bit.ly/30KA5oX
Learn about a system trained by the OpenAI team that solves grade school math problems with twice the accuracy of a fine-tuned GPT-3 model. It solves ~90% as many problems as real kids.
https://bit.ly/30KA5oX
Openai
Solving math word problems
We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our…
🔥Data Phoenix wishes you lovely Sunday! We hope is going great and you are ready for the upcoming week!
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3HQC4cG
But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3HQC4cG
📚EditGAN: High-Precision Semantic Image Editing
EditGAN is a novel method for high quality, high precision semantic image editing, allowing users to edit images by modifying their highly detailed part segmentation masks.
https://bit.ly/3cvlAIu
EditGAN is a novel method for high quality, high precision semantic image editing, allowing users to edit images by modifying their highly detailed part segmentation masks.
https://bit.ly/3cvlAIu
⚡️Hello everyone! How are you feeling about starting a new week? Today, let us introduce you Bernard Marr — a world-renowned futurist, influencer, and thought leader in business and technology. Passionate about using technology for the good of humanity, he shares his vision with over 2 million social media followers and 1 million newsletter subscribers. He was ranked by LinkedIn as one of the top 5 business influencers in the world and the No.1 influencer in the UK.
Today, Bernard Marr is one of the world’s most highly respected experts when it comes to future trends, strategy, business performance, digital transformation, and the intelligent use of data and AI in business. He has worked with and advised many of the world’s best-known organizations, such as Amazon, Google, Microsoft, Astra Zeneca, The Bank of England, BP, NVIDIA, Cisco, DHL, IBM, HPE, Ericsson, Jaguar Land Rover, Mars, The Ministry of Defense, NATO, The Home Office, NHS, Oracle, T-Mobile, Toyota, The Royal Air Force, Shell, The United Nations, Walgreens Alliance Boots, and Walmart.
He is the author of 20 books and hundreds of high profile reports and articles, including the international best-sellers. His books have been translated into over 20 languages and have been repeatedly featured as the Amazon No.1 bestselling book. He has earned the CMI Management Book of the Year award, the Axiom book award, and the WHSmith best business book award.
Today, Bernard also enjoys teaching for Oxford University, Warwick Business School, the Irish Management Institute, and ICAEW. On top of that, Bernard serves as a non-executive director on the board of businesses and has a seat on the dean’s council for Lancaster University Management School.
https://bit.ly/3qXZAOF
Today, Bernard Marr is one of the world’s most highly respected experts when it comes to future trends, strategy, business performance, digital transformation, and the intelligent use of data and AI in business. He has worked with and advised many of the world’s best-known organizations, such as Amazon, Google, Microsoft, Astra Zeneca, The Bank of England, BP, NVIDIA, Cisco, DHL, IBM, HPE, Ericsson, Jaguar Land Rover, Mars, The Ministry of Defense, NATO, The Home Office, NHS, Oracle, T-Mobile, Toyota, The Royal Air Force, Shell, The United Nations, Walgreens Alliance Boots, and Walmart.
He is the author of 20 books and hundreds of high profile reports and articles, including the international best-sellers. His books have been translated into over 20 languages and have been repeatedly featured as the Amazon No.1 bestselling book. He has earned the CMI Management Book of the Year award, the Axiom book award, and the WHSmith best business book award.
Today, Bernard also enjoys teaching for Oxford University, Warwick Business School, the Irish Management Institute, and ICAEW. On top of that, Bernard serves as a non-executive director on the board of businesses and has a seat on the dean’s council for Lancaster University Management School.
https://bit.ly/3qXZAOF
Bernard Marr
Bernard Marr | Future . Business . Success
Influencer
Bernard Marr is one of the world’s most successful social media influencers at the intersection of business and technology. View More
Keynote Speaker
Bernard Marr is a popular keynote choice because of his vast practical experience…
Bernard Marr is one of the world’s most successful social media influencers at the intersection of business and technology. View More
Keynote Speaker
Bernard Marr is a popular keynote choice because of his vast practical experience…
💡Hugging Face Transformer Inference Under 1 Millisecond Latency
Hugging Face has released “Infinity’’, a server product that performs inference at enterprise scale. It can perform Transformer inference at 1 millisecond latency on the GPU.
https://bit.ly/3xb5Qnj
Hugging Face has released “Infinity’’, a server product that performs inference at enterprise scale. It can perform Transformer inference at 1 millisecond latency on the GPU.
https://bit.ly/3xb5Qnj
Medium
Hugging Face Transformer Inference Under 1 Millisecond Latency
Go to production with Microsoft and Nvidia open source tooling
📚 On the Frequency Bias of Generative Models
In this paper, the authors provide insights on measures against high-frequency artifacts and what makes them effective, with focus on a frequency bias.
https://bit.ly/3l1m51k
In this paper, the authors provide insights on measures against high-frequency artifacts and what makes them effective, with focus on a frequency bias.
https://bit.ly/3l1m51k
📌Design patterns for Machine Learning Pipelines
ML pipeline design keeps evolving. In this article, you'll learn how these design patterns changed, what processes they went through, and their future direction.
https://bit.ly/32nIRKp
ML pipeline design keeps evolving. In this article, you'll learn how these design patterns changed, what processes they went through, and their future direction.
https://bit.ly/32nIRKp
KDnuggets
Design Patterns for Machine Learning Pipelines
ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through…
📚DScribe: Library of Denoscriptors for Machine Learning in Materials Science
DScribe is a software package for ML that provides "denoscriptors" for atomistic materials simulations, to accelerate and simplify the application of ML for atomistic property prediction.
https://bit.ly/3cKVMIe
DScribe is a software package for ML that provides "denoscriptors" for atomistic materials simulations, to accelerate and simplify the application of ML for atomistic property prediction.
https://bit.ly/3cKVMIe