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
The Data Phoenix Events invites you all on September 29 to our «The A-Z of Data» webinars. The topic — Pachyderm in production: lessons learned.
In this talk, we will take a look at yet another MLOps tool — Pachyderm. This tool is gaining in popularity and is unique for some use-cases. The speaker will share the experience of applying Pachyderm to a real-world, BigData NLP project. Most importantly, we will see the hidden limitations of Pachyderm and why it’s not quite the tool it claims to be.
Speaker
Oleh Lokshyn is a Machine Learning Architect at SoftServe. He built ML workflows on GCP, Azure, and on-premises for different supervised and unsupervised models. Oleh holds several certifications: Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Data Engineer, Microsoft Certified Azure Data Scientist Associate.
Participation is free, but pre-registration is required.
In this talk, we will take a look at yet another MLOps tool — Pachyderm. This tool is gaining in popularity and is unique for some use-cases. The speaker will share the experience of applying Pachyderm to a real-world, BigData NLP project. Most importantly, we will see the hidden limitations of Pachyderm and why it’s not quite the tool it claims to be.
Speaker
Oleh Lokshyn is a Machine Learning Architect at SoftServe. He built ML workflows on GCP, Azure, and on-premises for different supervised and unsupervised models. Oleh holds several certifications: Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Data Engineer, Microsoft Certified Azure Data Scientist Associate.
Participation is free, but pre-registration is required.
Friends! Tomorrow we will publish the new issue of our digest, don't miss it! 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/3zdT0Ev
https://bit.ly/3zdT0Ev
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.
📢 Мы в эфире. Начинаем Open Data Science Odessa Meetup #4. Будем говорить про то, как NLP поменялся за последние 10 лет, а также про опыт участия в ML соревнованиях. Присоединяйтесь!
https://bit.ly/3zexfEz
https://bit.ly/3zexfEz
YouTube
ODS.ai Odessa Meetup #4
Команда Data Phoenix Events вместе с Autodoc и VITech приглашает всех, 15 сентября, на митап одесского Open Data Science сообщества. На нем мы поговорим про то, как NLP поменялся за последние 10 лет, а также про опыт участия в ML соревнованиях.
https://…
https://…
📌Understanding ROC Curves with Python
In this article by Lucas Soares, you'll learn how to design and build the basic intuition for receiver operating characteristics (ROC) curve with Python.
https://bit.ly/3Emjes3
In this article by Lucas Soares, you'll learn how to design and build the basic intuition for receiver operating characteristics (ROC) curve with Python.
https://bit.ly/3Emjes3
Medium
Understanding ROC Curves with Python
Building the basic intuition for receiver operating characteristics curve with Python