For their engineering final project at @Insper, Arthur Olga, Gabriel Monteiro, Guilherme Leite, and Vinicius Lima created the MLOps Guide, which provides a Complete MLOps development cycle using DVC, CML, and IBM Watson.
@alexburlacu1996 tells a great story and provides many tips from his experience in MLOps in his blog โA Fable About MLOpsโฆand Broken Dreams ๐ญโ.
If youโd like an online CheatSheet for DVC you can find one created by @igor_chubin here ๐ cheat.sh/dvc
Pick a command from the drop-down menu and bam ๐ฅ, youโve got the info you need!
๐งต[5/7]
๐งโ๐ป Data Query Language
We have a new solution for managing datasets of unstructured data! Extend your DVC environment with the first unstructured data query language for machine learning.
Schedule a meeting with us if that's what you're needing!
We were privileged to have Matt Squire from @FuzzyLabsAI, a renowned expert in the MLOps space, as our guest.
During the session, Matt shared his insights on the benefits of open-source MLOps tools and how they can help businesses.
๐งต๐งต
In his ๐ฃ๐ฟ๐ฒ๐๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป, Matt provided a comprehensive breakdown of the MLOps tool space, categorizing it into SaaS platforms, fully open source, and partly open source tools.
He went on to describe the defining traits of open source and why he believes it to be the best choice for MLOps, including its flexibility, cost-effectiveness, and agility.
Matt's views on open-source MLOps tools generated a lot of engagement from our community members.
Several data practitioners have asked for a tutorial video from us on DVC, and we are glad to make this available.
In this video, you'll discover how to use the DVCLive and @huggingface datasets to create a Tweet Sentiment Analyzer.
๐งต๐งต๐งต
Do you want to learn more about using real-time metrics in machine learning experiments? Look nowhere else!
In this ๐๐๐๐ผ๐ฟ๐ถ๐ฎ๐น, we'll show you how to use DVCLive and the Hugging Face Dataset to build a potent Twitter sentiment analyzer.
๐๐๐๐๐ข๐ฏ๐, a library from ๐๐๐, gives you the ability to easily monitor your ML experiments. You will be able to easily grasp the performance of your model at every stage thanks to its real-time metrics.
We are happy to announce something exciting to the ML/AI Community๐จ
DVC has just released its new integration with Optuna, enabling you to streamline and optimize your hyperparameter search process while keeping track of every step with version control.
Our users have been requesting this integration for a while, and we're thrilled to deliver!
With the DVC's extension for VS Code, you can easily monitor and analyze your results, saving you time and effort in your machine learning workflow.
Try out our new integration today and see the benefits for yourself! ๐
This is the link to the video -
In the video, we explain how to use Optuna with Keras and view each iteration as an experiment in DVC's experiment table and plots. ๐๐
Woah! Been here? Is deep learning model training going horribly wrong? ๐๐ฝโโ๏ธ
Iterative Studio makes this easy to see so you don't waste time and resources!
๐งตย 1/7
With Iterative Studio and DVCLive, you can monitor the progress of your long-running experiments against others that you or your team have performed. All are easily accessed at work, at home, or by the rest of your team on the project.
๐งต2/7
You provide a couple environment variables for your model training job:
You can enter your STUDIO_TOKEN and dvc exp run if running locally