💡C. Light Technologies is to design and build the AI solution that spots changes in eye motion to detect the earliest stage of Alzheimer’s disease.
https://bit.ly/2VNHySe
https://bit.ly/2VNHySe
VentureBeat
C. Light Technologies uses AI to diagnose Alzheimer’s through retinal eye studies
C. Light Technologies has raised money for its AI tech that detects changes in eye motion to diagnose early signs of Alzheimer's disease.
Hey friends!👋🏻
The latest issue of the digest is already waiting for you on our website! New articles, papers, books, and more! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3AwkJkw
The latest issue of the digest is already waiting for you on our website! New articles, papers, books, and more! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3AwkJkw
Data Phoenix
Data Phoenix Digest - 12.08.2021
Webinar "The A-Z of Data: Introduction to MLOps", new AI system that translates natural language to code, AI for drug delivery and dementia diagnosis, data monetization 101, introducing Triton, sketch your own GAN, StyleGAN-NADA, Droidlet, AutoTinyBERT, courses…
📌Building Architectures that Can Handle the World’s Data
Perceiver is a general-purpose architecture that can process data including images, point clouds, audio, video, and their combinations. Learn more about this universal architecture!
https://bit.ly/2VLxCc2
Perceiver is a general-purpose architecture that can process data including images, point clouds, audio, video, and their combinations. Learn more about this universal architecture!
https://bit.ly/2VLxCc2
Deepmind
Building architectures that can handle the world’s data
Most architectures used by AI systems today are specialists. A 2D residual network may be a good choice for processing images, but at best it’s a loose fit for other kinds of data — such as the Lidar signals used in self-driving cars or the torques used in…
😊 Салют!
🙊 Бывает, что о важной, полезной конференции узнаешь уже по фотографиям с мероприятия, выложенных в сеть докладах и восторженных статусах коллег.
🔥 Есть способ не пропускать актуальные ивенты, загодя планировать время и бюджет на обучение.
🚀 Представляем канал наших друзей @gde_konfa, который поможет вам быть в курсе всех интересных конференций по data science, project/product менеджменту, маркетингу в Украине и не только! А теперь еще и много полезного online-контента: онлайн-курсы, конференциях и обучающие материалы.
⚠️ А еще, в канале часто публикуются уникальные промо-коды на ивенты.
🙊 Бывает, что о важной, полезной конференции узнаешь уже по фотографиям с мероприятия, выложенных в сеть докладах и восторженных статусах коллег.
🔥 Есть способ не пропускать актуальные ивенты, загодя планировать время и бюджет на обучение.
🚀 Представляем канал наших друзей @gde_konfa, который поможет вам быть в курсе всех интересных конференций по data science, project/product менеджменту, маркетингу в Украине и не только! А теперь еще и много полезного online-контента: онлайн-курсы, конференциях и обучающие материалы.
⚠️ А еще, в канале часто публикуются уникальные промо-коды на ивенты.
We are aware that some of you are looking for job opportunities. We've put together a list of 10 positions available this week, enjoy!
1) Machine Learning Optimization Engineer, Data Science UA
https://bit.ly/3m4TgCD
2) Deep Learning Engineer, Reface
https://bit.ly/3yJo4MV
3) Data Scientist (Advanced Analytics), SoftServe
https://bit.ly/37Cu7Hd
4) Lead MLOps Engineer, SoftServe
https://bit.ly/3fWst7G
5) AI/ML Computer Vision Engineer, Xenoss
https://bit.ly/3lWiFOV
For other 5 positions click 👉🏻 https://bit.ly/3CKoR2h
Did you find something for yourself? Let us know!
1) Machine Learning Optimization Engineer, Data Science UA
https://bit.ly/3m4TgCD
2) Deep Learning Engineer, Reface
https://bit.ly/3yJo4MV
3) Data Scientist (Advanced Analytics), SoftServe
https://bit.ly/37Cu7Hd
4) Lead MLOps Engineer, SoftServe
https://bit.ly/3fWst7G
5) AI/ML Computer Vision Engineer, Xenoss
https://bit.ly/3lWiFOV
For other 5 positions click 👉🏻 https://bit.ly/3CKoR2h
Did you find something for yourself? Let us know!
Continuous Integration and Continuous Deployment (CI/CD) Tools for Machine Learning
Continuous integration and continuous deployment are standard software development practices. Naturally, they're used in ML as well. Check out this article to learn how.
https://bit.ly/3jVF508
Continuous integration and continuous deployment are standard software development practices. Naturally, they're used in ML as well. Check out this article to learn how.
https://bit.ly/3jVF508
neptune.ai
Continuous Integration and Continuous Deployment (CI/CD) Tools for Machine Learning - neptune.ai
In modern software development teams, continuous integration (CI) and continuous deployment (CD) are standard practices. CI is about how the project should be built and tested in various runtimes, automatically and continuously. CD is needed so that every…
📌Companies could spend nearly $342 billion on AI software, hardware, and services in 2021. The spending is to rise to $500 billion by 2024.
https://bit.ly/37JFQ6L
https://bit.ly/37JFQ6L
VentureBeat
IDC: AI spending will reach $342B in 2021
In a new report, IDC reports that companies will spend $342 billion on AI software, hardware, and services in 2021.
💡Make a Rock-Solid ML Model Using Sklearn Pipeline
Most of data is useless unless you perform a decent amount of transformation and preprocessing. In this article, you'll learn how to use Sklearn to design and build robust ML models.
https://bit.ly/2W29jq9
#DataPhoenix #DataScience #MachineLearning #ArtificialIntelligence #AI #ML #Data
Most of data is useless unless you perform a decent amount of transformation and preprocessing. In this article, you'll learn how to use Sklearn to design and build robust ML models.
https://bit.ly/2W29jq9
#DataPhoenix #DataScience #MachineLearning #ArtificialIntelligence #AI #ML #Data
Medium
Make a rock-solid ML model using Sklearn Pipeline!
Why, How & When to use Sklearn’s Pipeline!
📌Containerizing Apache Hadoop Infrastructure at Uber
This article provides a summary of problems the Uber team faced when re-architecting the Hadoop deployment stack (Docker containers), and how they solved them along the way.
https://ubr.to/3yTuKrT
This article provides a summary of problems the Uber team faced when re-architecting the Hadoop deployment stack (Docker containers), and how they solved them along the way.
https://ubr.to/3yTuKrT
Uber Blog
Containerizing Apache Hadoop Infrastructure at Uber | Uber Blog
Introduction: As Uber’s business grew, we scaled our Apache Hadoop (referred to as ‘Hadoop’ in this article) deployment to 21000+ hosts in 5 years, to support the various analytical and machine learning use cases. We built a team with varied expertise to…
💡Data Movement in Netflix Studio via Data Mesh
Learn about Netflix's journey to a more efficient data movement using Data Mesh, to improve the pace of production and efficiency of global business operations using the most up-to-date information.
https://bit.ly/3yWUV0R
Learn about Netflix's journey to a more efficient data movement using Data Mesh, to improve the pace of production and efficiency of global business operations using the most up-to-date information.
https://bit.ly/3yWUV0R
Medium
Data Movement in Netflix Studio via Data Mesh
By Andrew Nguonly, Armando Magalhães, Obi-Ike Nwoke, Shervin Afshar, Sreyashi Das, Tongliang Liu, Wei Liu, Yucheng Zeng
Are you onboard and receive our weekly newsletter? 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/37UvSzC
https://bit.ly/37UvSzC
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.
📌Observation of Time-Crystalline Eigenstate Order on a Quantum Processor
The authors demonstrate the characteristic spatiotemporal response of a DTC for generic initial states. A time-reversal protocol discriminates external decoherence from intrinsic thermalization and uses quantum typicality to circumvent the cost of densely sampling the eigenspectrum.
https://bit.ly/3CTIBRn
The authors demonstrate the characteristic spatiotemporal response of a DTC for generic initial states. A time-reversal protocol discriminates external decoherence from intrinsic thermalization and uses quantum typicality to circumvent the cost of densely sampling the eigenspectrum.
https://bit.ly/3CTIBRn
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/2W9Iss8
https://bit.ly/2W9Iss8
Data Phoenix
Data Phoenix Digest - 19.08.2021
Webinar "The A-Z of Data: Monitoring ML Models in Production"; AI for food security, road safety, and agriculture efficiency; introducing Droidlet; all probability distributions explained in six minutes; make money using NFT + AI; Perceiver IO, SPEAR, SDEdit…
💡Make Money Using NFT + AI | GAN Image Generation
In this article, you'll see how to create new images using GAN, with a focus on generating art using Stylegan2-ADA. The goal is to create contemporary art via NFT and sell it via Opeansea.
https://bit.ly/2We6xhD
In this article, you'll see how to create new images using GAN, with a focus on generating art using Stylegan2-ADA. The goal is to create contemporary art via NFT and sell it via Opeansea.
https://bit.ly/2We6xhD
Medium
Make money using NFT + AI | GAN image generation
Create images using AI and sell it via NFT
The Data Phoenix Events team invites you all on August 25 to the second of our series of "The A-Z of Data" webinars. The topic — Monitoring ML Models in Production.
The performance of machine learning models can decline over time - due to changes in data, business processes, or simply data loss or failures. To avoid the negative impact on business performance, it is crucial to detect such situations and take timely action - for example, by retraining the model. Due to this, monitoring of services based on machine learning needs to include additional metrics related to the model and data quality. In the course of the tutorial, Emeli Dral 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.
Participation is free, but pre-registration is required.
https://bit.ly/3giNlq0
The performance of machine learning models can decline over time - due to changes in data, business processes, or simply data loss or failures. To avoid the negative impact on business performance, it is crucial to detect such situations and take timely action - for example, by retraining the model. Due to this, monitoring of services based on machine learning needs to include additional metrics related to the model and data quality. In the course of the tutorial, Emeli Dral 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.
Participation is free, but pre-registration is required.
https://bit.ly/3giNlq0
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.
We are aware that some of you are looking for job opportunities. We've put together a list of 10 positions available this week, enjoy!
1) Data Engineer, Appian
https://bit.ly/382oi66
2) Applied Scientist II - ML/NLP, Amazon
https://bit.ly/382s5jz
3) Data Scientist - Analytics, Host Quality, Airbnb
https://bit.ly/3sCRRVk
4) Principal Data Scientist, Atlassian
https://bit.ly/3y6XcFn
5) Machine Learning Scientist, Amazon
https://bit.ly/3AZRiry
For other 5 positions click 👉🏻 https://bit.ly/3mrxwkq
Did you find something for yourself? Let us know!
1) Data Engineer, Appian
https://bit.ly/382oi66
2) Applied Scientist II - ML/NLP, Amazon
https://bit.ly/382s5jz
3) Data Scientist - Analytics, Host Quality, Airbnb
https://bit.ly/3sCRRVk
4) Principal Data Scientist, Atlassian
https://bit.ly/3y6XcFn
5) Machine Learning Scientist, Amazon
https://bit.ly/3AZRiry
For other 5 positions click 👉🏻 https://bit.ly/3mrxwkq
Did you find something for yourself? Let us know!
💡Geometric Foundations of Deep Learning
Geometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. Explore the topic in detail!
https://bit.ly/3B5o4rn
Geometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. Explore the topic in detail!
https://bit.ly/3B5o4rn
Medium
Geometric foundations of Deep Learning
Geometric Deep Learning is an attempt to unify a broad class of ML problems from the perspectives of symmetry and invariance.
📌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