Collaborating Authors

The MLOps Stack


MLOps is a set of best practices that revolve around making machine learning in production more seamless. The purpose is to bridge the gap between experimentation and production with key principles to make machine learning reproducible, collaborative, and continuous. MLOps is not dependent on a single technology or platform. However, technologies play a significant role in practical implementations, similarly to how adopting Scrum often culminates in setting up and onboarding the whole team to e.g. To make it easier to consider what tools your organization could use to adopt MLOps, we've made a simple template that breaks down a machine learning workflow into components.

Machine Learning experiments and engineering with DVC


Online video course to teach basics for Machine Learning experiment management, pipelines automation and CI/CD to deliver ML solution into production. During these lessons you'll discover base features of Data Version Control (DVC), how it works and how it may benefit your Machine Learning and Data Science projects. During this course listeners learn engineering approaches in ML around a few practical examples. Screencast videos, repositories with examples and templates to put your hands dirty and make it easier apply best features in your own projects.

GitLab awards researcher $20,000, patches remote code execution bug


Simple steps can make the difference between losing your online accounts or maintaining what is now a precious commodity: Your privacy. GitLab has awarded a cybersecurity researcher $20,000 for reporting a serious remote code execution vulnerability on the platform. Discovered by William "vakzz" Bowling, a programmer and bug bounty hunter, the vulnerability was privately disclosed through the HackerOne bug bounty platform on March 23. Bowling said that GitLab's UploadsRewriter function, used to copy files, was the source of the critical security issue. The function should check file names and paths when issues were copied across projects.

GitLab to create tool for data teams - SD Times


GitLab has revealed it is working on a new tool for the data science lifecycle. Meltano is an open-source solution designed to fill the gaps between data and understanding business operations. "Meltano was created to help fill the gaps by expanding the common data store to support Customer Success, Customer Support, Product teams, and Sales and Marketing," the team wrote in a post. "Meltano aims to be a complete solution for data teams -- the name stands for model, extract, load, transform, analyze, notebook, orchestrate -- in other words, the data science lifecycle. While this might sound familiar if you're already a fan of GitLab, Meltano is a separate product.

The Launch of GitLab 13.1: Automated DevOps Management & QC Filters


The world's most powerful web-based DevOps lifecycle tool GitLab has released GitLab 13.1 to track coding quality and to stay compliant with the dynamic needs of the DevOps world. GitLab 13.1 is now officially available with extended Alert Management and Automated Coding Reporting features. Those who follow GitLab closely would agree that its acquisition of Gemnasium in 2018 has helped further fortify the security and compliance in open source. The smartest enhancement in GitLab 13.1 is Alert Management; to maintain a record of all application maintenance and to address critical issues in real-time. Simplified Alert Management, Alert Assignments and Slack integration enhance DevOps productivity with faster collaboration and just-in-time principles.