📌Using Sentiment Score to Assess Customer Service Quality
Net Promoter Score (NPS) is a well-accepted measurement of customer satisfaction in most customer-facing industries. In this article, you'll learn how Airbnb uses ML to calculate it.
https://bit.ly/380T66X
Net Promoter Score (NPS) is a well-accepted measurement of customer satisfaction in most customer-facing industries. In this article, you'll learn how Airbnb uses ML to calculate it.
https://bit.ly/380T66X
Medium
Using Sentiment Score to Assess Customer Service Quality
How AI-based Sentiment Models Complement Net Promoter Score
💡Feature Store: Data Platform for Machine Learning
Feature data is critical to the accurate predictions made by ML models. In this article, you'll learn how to generate, manage, and serve features by using open-source and homegrown feature stores.
https://bit.ly/3mplpEH
Feature data is critical to the accurate predictions made by ML models. In this article, you'll learn how to generate, manage, and serve features by using open-source and homegrown feature stores.
https://bit.ly/3mplpEH
Medium
Feature Store: Data Platform for Machine Learning
State-of-the-art open-source and homegrown feature stores that generate, manage and serve features at scale
Hello friends! The Data Phoenix team wants to remind you about the upcoming event on August 25. It is the second of our series of "The A-Z of Data" webinars.
The topic — Monitoring ML Models in Production
Welcome our speaker! Emeli Dral is a Co-founder and CTO at Evidently AI, a startup developing open-source tools to analyze and monitor the performance of machine learning models. Earlier, she co-founded an industrial AI startup and served as the Chief Data Scientist at Yandex Data Factory. She led over 50 applied ML projects for various industries - from banking to manufacturing. Emeli is a data science lecturer at GSOM SpBU and Harbour. Space University. She is a co-author of the Machine Learning and Data Analysis curriculum at Coursera with over 100,000 students. She also co-founded Data Mining in Action, the largest open data science course in Russia. Emeli will demonstrate how the quality of a model can change over time, and how one can track and analyze the changes using open source tools.
https://bit.ly/2WfhgbC
The topic — Monitoring ML Models in Production
Welcome our speaker! Emeli Dral is a Co-founder and CTO at Evidently AI, a startup developing open-source tools to analyze and monitor the performance of machine learning models. Earlier, she co-founded an industrial AI startup and served as the Chief Data Scientist at Yandex Data Factory. She led over 50 applied ML projects for various industries - from banking to manufacturing. Emeli is a data science lecturer at GSOM SpBU and Harbour. Space University. She is a co-author of the Machine Learning and Data Analysis curriculum at Coursera with over 100,000 students. She also co-founded Data Mining in Action, the largest open data science course in Russia. Emeli will demonstrate how the quality of a model can change over time, and how one can track and analyze the changes using open source tools.
https://bit.ly/2WfhgbC
📌GPU-Powered Data Science (NOT Deep Learning) with RAPIDS
Tired of the deep learning hype? In this article, you'll learn how to utilize GPUs for regular data science and machine learning even if you don't lots of deep learning work.
https://bit.ly/3BbNQu1
Tired of the deep learning hype? In this article, you'll learn how to utilize GPUs for regular data science and machine learning even if you don't lots of deep learning work.
https://bit.ly/3BbNQu1
Medium
GPU-Powered Data Science (NOT Deep Learning) with RAPIDS
How to utilize the power of your GPU for regular data science and machine learning even if you do not do a lot of deep learning work.
Data Phoenix is proud to be a Ukrainian team and today we celebrate 30 years of independence of our motherland. Join us!
Happy Independence Day of Ukraine! 🇺🇦
Happy Independence Day of Ukraine! 🇺🇦
💡Moving SciPy to the Meson Build System
In this guide, you'll learn about moving SciPy to Meson. Because SciPy now builds with Meson on Linux, it's worth exploring: expect faster builds and a more pleasant development experience.
https://bit.ly/38dUUJX
In this guide, you'll learn about moving SciPy to Meson. Because SciPy now builds with Meson on Linux, it's worth exploring: expect faster builds and a more pleasant development experience.
https://bit.ly/38dUUJX
Quansight Labs
Moving SciPy to the Meson build system
Let's start with an announcement: SciPy now builds with
Meson on Linux, and the full test suite passes!
This is a pretty exciting milestone, and good news for SciPy maintainers and
contributors - they
Meson on Linux, and the full test suite passes!
This is a pretty exciting milestone, and good news for SciPy maintainers and
contributors - they
Friendly reminder that tomorrow you will receive our weekly newsletter, if you didn't subscribe don't waste your time! 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 friends!
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https://bit.ly/3jeZD4w
Data Phoenix
SF Bay Area media and education platform focused on AI and Data. As a voice of AI industry, Data Phoenix delivers news, practical knowledge, and helps companies be heard in the community.
📌Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR
Khoi Nguyen et al. conduct analyses on three different aspects of SimCLR to analyze properties of contrast learning on fine-tuning, research knowledge distillation through teacher-forcing paradigm and study how transfer learning works on various datasets.
https://bit.ly/38cHRIX
Khoi Nguyen et al. conduct analyses on three different aspects of SimCLR to analyze properties of contrast learning on fine-tuning, research knowledge distillation through teacher-forcing paradigm and study how transfer learning works on various datasets.
https://bit.ly/38cHRIX
The latest issue of the digest is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3sTKMj5
https://bit.ly/3sTKMj5
Data Phoenix
Data Phoenix Digest - 26.08.2021
Announcement of next three webinars of "The A-Z of Data" series, AI and social sciences, MLOps best practices for Data Scientists, bootstrapping labels via supervision & Human-In-The-Loop, Machine Learning Zoomcamp, papers, videos, courses, jobs, and more...
💡SSWL-IDN: Self-Supervised CT Denoising
In this article, Ayaan Hague provides an explanation of SSWL-IDN that leverages residual learning and a hybrid loss combining perceptual loss and MSE, all incorporated in a VAE framework.
https://bit.ly/3mzbs7P
In this article, Ayaan Hague provides an explanation of SSWL-IDN that leverages residual learning and a hybrid loss combining perceptual loss and MSE, all incorporated in a VAE framework.
https://bit.ly/3mzbs7P
Medium
SSWL-IDN: Self-Supervised CT Denoising
A review of our recent CT Denoising paper “Window-Level is a Strong Denoising Surrogate”
We know that some of you are looking for job opportunities. Here is a list of 10 positions available this week, enjoy!
1) Machine Learning Engineer, Shelf
https://bit.ly/2WqvaaI
2) Data Scientist, Shelf
https://bit.ly/3zmOhRv
3) Senior/Middle CV/ML Engineer, Apostera
https://bit.ly/2Wuhyeq
4) Senior Data Scientist for Sport Stream, Parimatch Tech
https://bit.ly/38fPEpg
5) Data Scientist (NLP), SoftServe
https://bit.ly/3ks18vv
For other 5 positions click 👉🏻 https://bit.ly/3mKTtuW
Did you find something for yourself? Let us know!
1) Machine Learning Engineer, Shelf
https://bit.ly/2WqvaaI
2) Data Scientist, Shelf
https://bit.ly/3zmOhRv
3) Senior/Middle CV/ML Engineer, Apostera
https://bit.ly/2Wuhyeq
4) Senior Data Scientist for Sport Stream, Parimatch Tech
https://bit.ly/38fPEpg
5) Data Scientist (NLP), SoftServe
https://bit.ly/3ks18vv
For other 5 positions click 👉🏻 https://bit.ly/3mKTtuW
Did you find something for yourself? Let us know!
📌How to Train a BERT Model From Scratch
A BERT 101. In this article, you'll find a step-by-step guide to training a functional model from scratch. All guidelines are clear and will work well for beginners.
https://bit.ly/3zpXWqC
A BERT 101. In this article, you'll find a step-by-step guide to training a functional model from scratch. All guidelines are clear and will work well for beginners.
https://bit.ly/3zpXWqC
Medium
How to Train a BERT Model From Scratch
Meet BERT’s Italian cousin, FiliBERTo
💡DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
In the paper, the authors review the deep traffic models and the widely used datasets, build a standard benchmark to evaluate their performances with the same settings and metrics.
https://bit.ly/3jpEhBt
In the paper, the authors review the deep traffic models and the widely used datasets, build a standard benchmark to evaluate their performances with the same settings and metrics.
https://bit.ly/3jpEhBt
📌Machine Learning Pipeline End-to-End Solution
ML implementations are supported by dozens of different services. Complexity is not necessarily a good thing. In this article, you'll learn how ML system can be split into as few services as possible.
https://bit.ly/3gGgJqs
ML implementations are supported by dozens of different services. Complexity is not necessarily a good thing. In this article, you'll learn how ML system can be split into as few services as possible.
https://bit.ly/3gGgJqs
Medium
Machine Learning Pipeline End-to-End Solution
ML implementations tend to get complicated quickly. This article will explain how ML system can be split into different services. Services…
📌Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing
Neural Generalized Implicit Functions (Neural-GIF) is a method to animate people in clothing as a function of the body pose trained on various raw 3D scans.
https://bit.ly/3yBNUBG
Neural Generalized Implicit Functions (Neural-GIF) is a method to animate people in clothing as a function of the body pose trained on various raw 3D scans.
https://bit.ly/3yBNUBG
The Data Phoenix Events team invites you all on September 8 to our "The A-Z of Data" webinar. The topic — deploying deep learning models with Kubernetes and Kubeflow.
In this talk, we'll learn about deploying Keras models. First, we'll see how to do it with TF-Serving and Kubernetes, and in the second part of the talk, we'll do it with KFServing and Kubeflow.
Speaker
Alexey Grigorev - Principal Data Scientist at OLX Group, Founder at DataTalks.Club. Alexey wrote a few books about machine learning. One of them is Machine Learning Bookcamp — a book for software engineers who want to get into machine learning.
Participation is free, but pre-registration is required
https://bit.ly/3BrxTjj
In this talk, we'll learn about deploying Keras models. First, we'll see how to do it with TF-Serving and Kubernetes, and in the second part of the talk, we'll do it with KFServing and Kubeflow.
Speaker
Alexey Grigorev - Principal Data Scientist at OLX Group, Founder at DataTalks.Club. Alexey wrote a few books about machine learning. One of them is Machine Learning Bookcamp — a book for software engineers who want to get into machine learning.
Participation is free, but pre-registration is required
https://bit.ly/3BrxTjj
Data Phoenix
Webinar "Deploying deep learning models with Kubernetes and Kubeflow" (RU)
In this talk, we'll learn about deploying Keras models. First, we'll see how to do it with TF-Serving and Kubernetes, and in the second part of the talk, we'll do it with KFServing and Kubeflow.
Speaker
Alexey Grigorev - Principal Data Scientist at OLX…
Speaker
Alexey Grigorev - Principal Data Scientist at OLX…
💡Creating Synthetic Data for Machine Learning
This tutorial will guide you through the steps needed to create the synthetic data and show how you can then train it with YOLOv5 in order to work on real images.
https://bit.ly/3t1dQ8y
This tutorial will guide you through the steps needed to create the synthetic data and show how you can then train it with YOLOv5 in order to work on real images.
https://bit.ly/3t1dQ8y
Medium
Creating Synthetic Data for Machine Learning
This tutorial will guide you through the steps needed to create the synthetic data and show how you can then train it with YOLOv5 in order to work on real images. If you would like to access the full…
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https://bit.ly/3yyEt5K
https://bit.ly/3yyEt5K
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.
Data Phoenix pinned «The Data Phoenix Events team invites you all on September 8 to our "The A-Z of Data" webinar. The topic — deploying deep learning models with Kubernetes and Kubeflow. In this talk, we'll learn about deploying Keras models. First, we'll see how to do it with…»
The Data Phoenix Events team invites you all on September 16 to our "The A-Z of Data" webinars. The topic — re-usable pipelines for ML projects with DVC.
Good ML pipelines ensure reproducibility of ML experiments and controllability of the development process. In practice, there are often situations when you want to reuse the code of one project into a new one. Sometimes, a new project (model) differs only in the target variable. In such cases, you can reuse up to 95% of the developments from the previous project. This talk discusses the approaches to organize and configure ML pipelines using DVC, ways to reuse ML pipelines, and typical scenarios where this can come in handy.
Speaker
Rozhkov Mikhail - Solution Engineer at Iterative.ai. ML Engineer and enthusiast with over six years of experience in Machine Learning and Data Science. Co-creator ML REPA, author of courses on automating ML experiments with DVC and MLOps. As a member of the Iterative.ai team, he helps teams improve ML development and automate MLOps processes.
Participation is free, but pre-registration is required
https://bit.ly/3yuXs0Z
Good ML pipelines ensure reproducibility of ML experiments and controllability of the development process. In practice, there are often situations when you want to reuse the code of one project into a new one. Sometimes, a new project (model) differs only in the target variable. In such cases, you can reuse up to 95% of the developments from the previous project. This talk discusses the approaches to organize and configure ML pipelines using DVC, ways to reuse ML pipelines, and typical scenarios where this can come in handy.
Speaker
Rozhkov Mikhail - Solution Engineer at Iterative.ai. ML Engineer and enthusiast with over six years of experience in Machine Learning and Data Science. Co-creator ML REPA, author of courses on automating ML experiments with DVC and MLOps. As a member of the Iterative.ai team, he helps teams improve ML development and automate MLOps processes.
Participation is free, but pre-registration is required
https://bit.ly/3yuXs0Z
📌VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
In the paper, the authors propose a new baseline model, named multi-level memory aggregation network (MMA-Net), for video instance lane detection.
https://bit.ly/3gTgrfM
In the paper, the authors propose a new baseline model, named multi-level memory aggregation network (MMA-Net), for video instance lane detection.
https://bit.ly/3gTgrfM