Hello friends! We know that some of you didn’t have the opportunity to be present at our first webinar "Introduction to MLOps". We posted the whole footage that you can check out on our website. Listen carefully, take notes, and don’t miss our future events!
https://bit.ly/3BBzDGI
https://bit.ly/3BBzDGI
💡SummerTime: Text Summarization Toolkit for Non-Experts
In this paper, the authors present SummerTime, a toolkit for text summarization, including various models, datasets, and evaluation metrics, for a full spectrum of summarization-related tasks.
https://bit.ly/3DSh7vG
In this paper, the authors present SummerTime, a toolkit for text summarization, including various models, datasets, and evaluation metrics, for a full spectrum of summarization-related tasks.
https://bit.ly/3DSh7vG
📌Use a SageMaker Pipeline Lambda Step for Lightweight Model Deployments
In this article, you'll explore the Lambda step and how you can use it to add custom functionality to your ML pipelines. Also, the specifics of using the Lambda step for lightweight model deployments.
https://amzn.to/3l0wqKa
In this article, you'll explore the Lambda step and how you can use it to add custom functionality to your ML pipelines. Also, the specifics of using the Lambda step for lightweight model deployments.
https://amzn.to/3l0wqKa
Amazon
Use a SageMaker Pipeline Lambda step for lightweight model deployments | Amazon Web Services
With Amazon SageMaker Pipelines, you can create, automate, and manage end-to-end machine learning (ML) workflows at scale. SageMaker Projects build on SageMaker Pipelines by providing several MLOps templates that automate model building and deployment pipelines…
The Data Phoenix Events team invites you all on September 22 to our "The A-Z of Data" webinars. The topic — From Research to Product with Hydrosphere.
Research and experimentation is usually a fun part of the project. Exploring data, learning domains, choosing and tuning models, researching and exploring to come up with better solutions.
Moving to production is where the fun ends. It often becomes a tedious and problematic part of the project. And that’s where Hydrosphere comes to the rescue. The platform that takes on all the monotonous work of deploying, maintaining, and managing your ML models in production.
Come and learn from experts how to turn your research into a robust AI/ML product and how Hydrosphere can help you do it along the way.
Speaker
Andrii Latysh - Technical Product Owner in ML/DS at Provectus; Founder & Coordinator at Odyssey - Odessa Data Science Community; Machine Learning/Data Science Engineer and Consultant; Lecturer; Speaker; PhD student.
Participation is free, but pre-registration is required.
https://bit.ly/2WSzkZ9
Research and experimentation is usually a fun part of the project. Exploring data, learning domains, choosing and tuning models, researching and exploring to come up with better solutions.
Moving to production is where the fun ends. It often becomes a tedious and problematic part of the project. And that’s where Hydrosphere comes to the rescue. The platform that takes on all the monotonous work of deploying, maintaining, and managing your ML models in production.
Come and learn from experts how to turn your research into a robust AI/ML product and how Hydrosphere can help you do it along the way.
Speaker
Andrii Latysh - Technical Product Owner in ML/DS at Provectus; Founder & Coordinator at Odyssey - Odessa Data Science Community; Machine Learning/Data Science Engineer and Consultant; Lecturer; Speaker; PhD student.
Participation is free, but pre-registration is required.
https://bit.ly/2WSzkZ9
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.
💡Materials Fingerprinting Classification
In this paper, the authors propose a machine learning algorithm coupled with topological data analysis that provides an easy way to extract structural information from APT datasets.
https://bit.ly/2X10ldl
https://bit.ly/2X10ldl)
In this paper, the authors propose a machine learning algorithm coupled with topological data analysis that provides an easy way to extract structural information from APT datasets.
https://bit.ly/2X10ldl
https://bit.ly/2X10ldl)
Don't forget that tomorrow - September 8, our speaker Alexey Grigorev will talk about deploying deep learning models with Kubernetes and Kubeflow.
Alexey is 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.
For more info tap 👉🏻 https://bit.ly/3DRR87H
Alexey is 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.
For more info tap 👉🏻 https://bit.ly/3DRR87H
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…
Hello friends!
We know that some of you didn’t have the opportunity to be present at our second webinar "The A-Z of Data: Monitoring ML Models in Production". We posted the whole footage that you can check out on our website. Listen carefully, take notes, and don’t miss our future events!
https://bit.ly/3tk0owu
We know that some of you didn’t have the opportunity to be present at our second webinar "The A-Z of Data: Monitoring ML Models in Production". We posted the whole footage that you can check out on our website. Listen carefully, take notes, and don’t miss our future events!
https://bit.ly/3tk0owu
📌How to Detect, Evaluate, and Visualize Historical Drifts in the Data
Analyzing historical drift in data is a nice way of understanding how your data changes, to choose monitoring thresholds. Check out this tutorial for details.
https://bit.ly/3yOeEPb
Analyzing historical drift in data is a nice way of understanding how your data changes, to choose monitoring thresholds. Check out this tutorial for details.
https://bit.ly/3yOeEPb
Evidentlyai
How to detect, evaluate and visualize historical drifts in the data
You can look at historical drift in data to understand how your data changes and choose the monitoring thresholds. Here is an example with Evidently, Plotly, Mlflow, and some Python code.
Friends! Don't forget to subscribe to our weekly newsletter, a new issue 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/3haZNby
https://bit.ly/3haZNby
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.
💡Accelerating Materials Discovery with Bayesian Optimization and Graph Deep Learning
The authors show that Bayesian optimization with symmetry constraints using a graph deep learning energy model can be used to perform "DFT-free" relaxations of crystal structures.
https://bit.ly/3tnSIti
The authors show that Bayesian optimization with symmetry constraints using a graph deep learning energy model can be used to perform "DFT-free" relaxations of crystal structures.
https://bit.ly/3tnSIti
Hey friends! Today is a great day, because Data Phoenix just published the latest 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/3ld7G1H
https://bit.ly/3ld7G1H
Data Phoenix
Data Phoenix Digest - 09.09.2021
AI preventing security threats, a deep dive on ARIMA models, a gentle introduction to GNN, how to detect, evaluate, and visualize historical drifts in the data, multiplying matrices without multiplying, books, videos, jobs, and more...
📌Complete Guide to A/B Testing Design, Implementation and Pitfalls
In this guide, the author covers a wide range of topics on end-to-end A/B testing for your Data Science experiments, with examples and Python implementation.
https://bit.ly/3E28krb
In this guide, the author covers a wide range of topics on end-to-end A/B testing for your Data Science experiments, with examples and Python implementation.
https://bit.ly/3E28krb
Medium
Simple and Complete Guide to A/B Testing
End-to-end A/B testing for your Data Science experiments for non-technical and technical specialists with examples and Python…
A small reminder that 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
Our speaker is 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. For more info check 👉🏻
https://bit.ly/3C1OD18
The topic — re-usable pipelines for ML projects with DVC
Our speaker is 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. For more info check 👉🏻
https://bit.ly/3C1OD18
Data Phoenix
Webinar "Re-usable pipelines for ML projects with DVC"
Команда Data Phoenix Events приглашает всех, 16 сентября, на вебинар, который будет посвящен переиспользованию ML пайплайнов между проектами
💡Annotate and Improve Computer Vision Datasets with CVAT and FiftyOne
This post covers two example workflows showing how to use the integration between FiftyOne and CVAT, helping you to build efficient annotation workflows and train better models.
https://bit.ly/3tBbEVE
This post covers two example workflows showing how to use the integration between FiftyOne and CVAT, helping you to build efficient annotation workflows and train better models.
https://bit.ly/3tBbEVE
Towards Data Science
How to Annotate and Improve Computer Vision Datasets with CVAT and FiftyOne | Towards Data Science
Tips for using the open-source tools FiftyOne and CVAT to build efficient annotation workflows and train better models
📌Deep Reinforcement Learning at the Edge of the Statistical Precipice
In the paper, the authors propose a new approach to the reliable evaluation of deep RL models. They illustrate their findings using a case study on the Atari 100k benchmark.
https://bit.ly/3nvEiGJ
In the paper, the authors propose a new approach to the reliable evaluation of deep RL models. They illustrate their findings using a case study on the Atari 100k benchmark.
https://bit.ly/3nvEiGJ
💡IKEA ASM Dataset
The IKEA ASM dataset is a multi-modal and multi-view video dataset of 371 samples of assembly tasks to enable rich analysis and understanding of human activities.
https://bit.ly/3npxg6f
The IKEA ASM dataset is a multi-modal and multi-view video dataset of 371 samples of assembly tasks to enable rich analysis and understanding of human activities.
https://bit.ly/3npxg6f
📌Understanding Convolutions on Graphs
In this article, you'll learn about the building blocks and design choices of graph neural networks. Make sure to check out the Supplementary Material section for more goodies.
https://bit.ly/3EeCY0H
In this article, you'll learn about the building blocks and design choices of graph neural networks. Make sure to check out the Supplementary Material section for more goodies.
https://bit.ly/3EeCY0H
Distill
Understanding Convolutions on Graphs
Understanding the building blocks and design choices of graph neural networks.
Friends the webinar is really soon and we hope to see you there. September 16 - "The A-Z of Data" webinars.
The topic — re-usable pipelines for ML projects with DVC.
For more info and registration tap the link 👉🏻
https://bit.ly/3tS1i3T
The topic — re-usable pipelines for ML projects with DVC.
For more info and registration tap the link 👉🏻
https://bit.ly/3tS1i3T
💡The Natural Scenes Dataset
The Natural Scenes Dataset (NSD) is a large-scale fMRI dataset consisting of whole-brain, high-resolution fMRI measurements of 8 healthy adult subjects while they viewed thousands of color natural scenes over the course of 30–40 scan sessions.
https://bit.ly/2XrEXht
The Natural Scenes Dataset (NSD) is a large-scale fMRI dataset consisting of whole-brain, high-resolution fMRI measurements of 8 healthy adult subjects while they viewed thousands of color natural scenes over the course of 30–40 scan sessions.
https://bit.ly/2XrEXht