💡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
Hey friends! Data Phoenix is here and we want to tell you that 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/3mWVxjC
https://bit.ly/3mWVxjC
Data Phoenix
Data Phoenix Digest - 02.09.2021
Open Data Science Odessa Meetup #4, AI with "natural" voices from NVIDIA, deploying NVIDIA Triton at Scale with MIG and Kubernetes, self-supervised CT denoising, creating synthetic data, VIL-100, Neural-GIF, YOLOP, FedScale, AP-10K, jobs, and more...
💡Bootstrap a Modern Data Stack in 5 minutes with Terraform
The guide with all the details to walk you through setting up Airbyte, BigQuery, dbt, Metabase, and everything else you need to run a Modern Data Stack using Terraform.
https://bit.ly/3zLgVfA
The guide with all the details to walk you through setting up Airbyte, BigQuery, dbt, Metabase, and everything else you need to run a Modern Data Stack using Terraform.
https://bit.ly/3zLgVfA
Medium
Bootstrap a Modern Data Stack in 5 minutes with Terraform
Setup Airbyte, BigQuery, dbt, Metabase, and everything else you need to run a Modern Data Stack using Terraform.
Data Phoenix Events together with Autodoc and VITech invites you all on September 15 to the meet-up of Open Data Science community in Odessa. During which we will discuss how NLP has changed throughout the last 10 years. In addition to that, we will talk about the experience of involvement in ML competitions. If you can’t attend in person there’s no problem because we are going to be Live as well.
For more details and registration tap this link 👉🏻
https://bit.ly/2WOT6oE
For more details and registration tap this link 👉🏻
https://bit.ly/2WOT6oE
We are aware that some of you are looking for job opportunities. We got you and here is a list of 10 positions available this week, enjoy!
1) Machine learning Engineer (middle/senior), Depositphotos, Kyiv, Remote
https://bit.ly/3taJmB7
2) ML Developer for Data Science Team (Python), Rakuten, Kyiv, Odesa, Remote
https://bit.ly/3jGcRHK
3) AI/ML Computer Vision Engineer, Xenoss, Kyiv, Kharkiv, Lviv, Odesa, Remote
https://bit.ly/3n1m9jt
4) Data Scientist (Advanced Analytics), SoftServe, Lviv, Kyiv, Poland
https://bit.ly/3n44jwn
5) Data Scientist III, Rackspace, Remote (United States)
https://bit.ly/3BGQ70C
For other 5 positions click 👉🏻https://bit.ly/2WOT3c4
Did you find something for yourself? Let us know!
1) Machine learning Engineer (middle/senior), Depositphotos, Kyiv, Remote
https://bit.ly/3taJmB7
2) ML Developer for Data Science Team (Python), Rakuten, Kyiv, Odesa, Remote
https://bit.ly/3jGcRHK
3) AI/ML Computer Vision Engineer, Xenoss, Kyiv, Kharkiv, Lviv, Odesa, Remote
https://bit.ly/3n1m9jt
4) Data Scientist (Advanced Analytics), SoftServe, Lviv, Kyiv, Poland
https://bit.ly/3n44jwn
5) Data Scientist III, Rackspace, Remote (United States)
https://bit.ly/3BGQ70C
For other 5 positions click 👉🏻https://bit.ly/2WOT3c4
Did you find something for yourself? Let us know!
📌Anomaly Detection with TensorFlow Probability and Vertex AI
In this article, you'll learn how Google's AI team uses an ML solution for anomaly detection on Vertex AI to automate these laborious processes of building time series models.
https://bit.ly/3yGIPYB
In this article, you'll learn how Google's AI team uses an ML solution for anomaly detection on Vertex AI to automate these laborious processes of building time series models.
https://bit.ly/3yGIPYB
Google Cloud Blog
Anomaly detection with TensorFlow Probability and Vertex AI | Google Cloud Blog
Data Phoenix pinned «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.…»
Data Phoenix pinned «Hey friends! Data Phoenix is here and we want to tell you that 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/3mWVxjC»
Data Phoenix pinned «Data Phoenix Events together with Autodoc and VITech invites you all on September 15 to the meet-up of Open Data Science community in Odessa. During which we will discuss how NLP has changed throughout the last 10 years. In addition to that, we will talk about…»
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