ClearML

Tel Aviv, Israel
2016
May 3, 2021   |  By ClearML
May 3rd 2021 – With over 11 man-years of working, and tinkering, long into the night, I am pleased to announce we have hit version 1.0. Following quickly after the release of ClearML 0.17.5, we added the last remaining features we felt 1.0 needed. Namely multi-model support, as well as improved batch operations. With these in place, the choice was clear. The next version released should be the baseline moving forward.
May 3, 2021   |  By ClearML
Few things in life are certain, least of all roadmaps. There is a saying I love, apart from the one above, which says “if you want to hear God laugh, tell him/her your plans”. Nowhere is that more true than in software development in a startup. There are grand ideas put forward, people often vie with one another, in short, life happens.
Apr 21, 2021   |  By Ivan Ralašić
Although the title might sound like a collaboration of two music bands with really bad names, this blog is all about understanding how computer vision and machine learning can be used to improve safety and security in a harsh and dangerous environment of a construction site. The construction industry is one of the most dangerous industries according to the common stats from OSHA.
Jan 18, 2021   |  By Raviv Pavel
Building machine learning (ML) and deep learning (DL) models obviously require plenty of data as a training-set and a test-set on which the model is tested against and evaluated. Best practices related to the setup of train-sets and test-sets have evolved in academic circles, however, within the context of applied data science, organizations need to take into consideration a very different set of requirements and goals. Ultimately, any model that a company builds aims to address a business problem.
Jan 5, 2021   |  By ClearML
We have three big announcements to our community today, and I wanted to talk to you about them: One, Allegro Trains is changing its name, two, we’re adding a completely new way to use Trains, and three, we’re announcing a bunch of features that make Trains an even better product for you! Read all about it on our blog at Clear.ml, our new website for our open source suite of tools.
Nov 18, 2020   |  By ClearML
Deep learning has evolved in the past five years from an academic research domain, to being adopted, integrated and leveraged for new dimensions of productivity across multiple industries and use cases, such as medical imaging, surveillance, IoT, chatbots, robotic,s and many more. From NLP to computer vision, deep learning has been breaking the barriers of SOTA algorithms and providing results that were, otherwise, impossible to achieve.
Oct 27, 2020   |  By Henok Yemam
The infamous data science workflow with interconnected circles of data acquisition, wrangling, analysis, and reporting understates the multi-connectivity and non-linearity of these components. The same is true for machine learning and deep learning workflows. I understand the need for oversimplification is expedient in presentations and executive summaries. However, it may paint unrealistic pictures, hide the intricacies of ML development and conceal the realities of the mess.
Oct 18, 2020   |  By Dan Malowany
Audio signals are all around us. As such, there is an increasing interest in audio classification for various scenarios, from fire alarm detection for hearing impaired people, through engine sound analysis for maintenance purposes, to baby monitoring. Though audio signals are temporal in nature, in many cases it is possible to leverage recent advancements in the field of image classification and use popular high performing convolutional neural networks for audio classification.
Oct 15, 2020   |  By ClearML
The design and training of neural networks are still challenging and unpredictable procedures. The difficulty of tuning these models makes training and reproducing more of an art than a science, based on the researcher’s knowledge and experience. One of the reasons for this difficulty is that the training procedure of machine learning models includes multiple hyperparameters that affect how the training process fits the model to the data.
Oct 5, 2020   |  By ClearML
So, if you’re a nose-to-the-keyboard developer, there’s ample probability that this analogy is outside your comfort zone … bear with me. Imagine two Olympics-level figure skaters working together on the ice, day in and day out, to develop and perfect a medal-winning performance. Each has his or her role, and they work in sync to merge their actions and fine-tune the results.
May 11, 2021   |  By ClearML
Can you build your own feature store in two minutes? (sort of) Yes!!! DataOps is all you need. Join Ariel and T.Guerre to find out how! First time hearing about us? Go to - clear.ml! ClearML: One open-source suite of tools that automates preparing, executing, and analyzing machine learning experiments. Bring enterprise-grade data science tools to any ML project.
May 4, 2021   |  By ClearML
T. Guerre gives a UI-centric overture of the recent changes in v1.0 Depeche Mode MegaMix - nullsleep (CC)
Apr 27, 2021   |  By ClearML
Ariel and T.Guerre discussing the reasoning behind features stores. Should you get one for your production pipeline? First time hearing about us? Go to - clear.ml! ClearML: One open-source suite of tools that automates preparing, executing, and analyzing machine learning experiments. Bring enterprise-grade data science tools to any ML project.
Apr 20, 2021   |  By ClearML
Ariel and T.Guerre discussing how to get the dataset onto your machine :) First time hearing about us? Go to - clear.ml! ClearML: One open-source suite of tools that automates preparing, executing, and analyzing machine learning experiments. Bring enterprise-grade data science tools to any ML project.
Apr 13, 2021   |  By ClearML
A git-for-data that isn't a git-for-data ;) ClearML: One open-source suite of tools that automates preparing, executing, and analyzing machine learning experiments. Bring enterprise-grade data science tools to any ML project.
Dec 6, 2020   |  By ClearML
Join Dr Ariel Biller, AllegroAI's evangelist in a webinar discussing Hyperparamter optimizations, what are they, what options for optimizations are available and how to easily hyperparameter search.
Nov 24, 2020   |  By ClearML
Join Dr Ariel Biller, AllegroAI's evangelist in a webinar discussing pipelines for machine learning research, what are they, how do they make your life easier and why it's becoming a must-have tool in every researcher's arsenal.
Nov 18, 2020   |  By ClearML
Join Dr Ariel Biller, AllegroAI's evangelist in a webinar discussing Deep Learning data challenges and tools to overcome them
Oct 15, 2020   |  By ClearML
Join Dr Ariel Biller, AllegroAI's evangelist in a webinar discussing the concept of MLOps, what it is, what problems it comes to solve and what are the tools which can help in machine and deep learning research.
Sep 2, 2020   |  By ClearML
Allegro Trains is an open source ML / DL experiment manager, versioning and ML-Ops full system solution.

End-to-end enterprise-grade platform for data scientists, data engineers, DevOps and managers to manage the entire machine learning & deep learning product life-cycle.

ClearML helps companies develop, deploy and manage machine & deep learning solutions. With ClearML, organizations bring to market and manage higher quality products, faster and more cost effectively. Our products are based on the Allegro Trains open source ML & DL experiment manager and ML-Ops package.

Why ClearML?

  • Scale Smarter: Abstract away all the building blocks of the ML/DL lifecycle: data management, experiment orchestration, resource management, and feedback loop.
  • Bridge Science & Engineering: Empower your team to leverage models created by data scientists with unprecedented ease and accessibility. Seamless handoff.
  • Effortless ML-Ops: Let us manage & scale the platform to meet your needs, cloud or on-prem. Let us also optionally build a customized, automated data pipeline for you, complete with integration to your current systems.
  • Cut Costs: Empower your researchers and teams to be profoundly more productive. Complete tasks in a fraction of the time and focus on the data that brings the highest ROI.

ClearML’s customers hail from over 55 countries and span almost all industries, such as automotive, media, healthcare, medical devices, robotics, security, silicon & manufacturing.