𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁!!

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.

🧵🧵 In his 𝗣𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻, Matt provided a comprehensive breakdown of the MLOps tool space, categorizing it into SaaS platforms, fully open source, and partly open source tools.

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.

🧵🧵🧵 Do you want to learn more about using real-time metrics in machine learning experiments? Look nowhere else!

In this 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹, we'll show you how to use DVCLive and the Hugging Face Dataset to build a potent Twitter sentiment analyzer.

We are happy to announce something exciting to the ML/AI Community🚨

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.

Here's a comprehensive thread on how to build a seamless batch-scoring experience.

You will learn how to:

🤖 Design ML pipelines with @DVCorg

🐈 Training with @gitlab and CML

🧪 ML experiments with @Iterativeai Studio

🚢 Deployments with @ApacheAirflow

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What is your favourite IDE/Code Editor for Machine Learning?

1. Jupyter Lab/Notebook
2. R Studio
3. Pycharm
4. VS Code
5. Spyder
6. Others(name it) Follow @DVCorg

A lot of people keep mentioning @code

Check out this @code Extension for Machine learning experiment management -

marketplace.visualstudio.com/items?itemName…

Woah! Been here? Is deep learning model training going horribly wrong? 🙋🏽‍♂️

Iterative Studio makes this easy to see so you don't waste time and resources!

🧵 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.

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👨🏻‍💻 Setup CI/CD in your machine learning projects using these simple yet powerful “♾️ CML commands”:

🔄 ci
🏃‍♂️ runner
⤴️ pr
💬 comment
🧑🏼‍🏫 tensorboard

@Iterativeai @DVCorg
#cml #tensorboard #opensource

🧵[1/7] 🔄 ci

The “cml ci” command prepares the whole repository for CML operations after creating the cml.yaml file

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🎁 Ding Dong! Here’s a flash from Iterative Community this month👇

🦮 MLOps Guide
🧪 DVC Extension
🌌 A Fable about MLOps
📝 Cheatsheet for DVC
🧑‍💻 Data Query Language

@Iterativeai @DVCorg
#mlOps #data #community

🧵[1/7] 🦮 MLOps Guide

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.

mlops-guide.github.io

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👨🏻‍💻 Ever tried deploying a model? and ended up entangled in scripts?

Here’s how MLEM lets us do “single command deployments” ⚡️:

- Deployment Options
- Setting up the Environment
- Run the command
- Getting Predictions

@Iterativeai @DVCorg
#mlem #aws #deployment

🧵[1/6] 🚀 MLEM gives us a simple and powerful API to deploy to platforms such as:

✅ @awscloud Sagemaker
✅ Docker Container
✅ Heroku
✅ Kubernetes

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𝗗𝗩𝗖𝗟𝗶𝘃𝗲 + 𝘆𝗼𝘂𝗿 𝗰𝗼𝗱𝗲 = 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴!

Produce an experiment tracker in CLI or @Code with DVCLive and a few lines added to your code or notebook

Version your ML experiments fast with no database or SaaS needed colab.research.google.com/drive/1VKEBdSg…
1/4 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸?

1 . Install DVC and DVCLive 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 --𝚞𝚙𝚐𝚛𝚊𝚍𝚎 𝚍𝚟𝚌

2. Initialize your repo with 𝗚𝗶𝘁 and 𝗗𝗩𝗖

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✨ Visualize your pipelines as beautiful graphs of connected stages using “dvc dag” command.

Here's different types of graphs you can explore 🗺️

✅ Full DAG
➡️ DAG of outputs
🧜‍♀️ Mermaid flowchart
⚪️ DOT format
📣 Verbose

@Iterativeai @DVCorg
#data #pipeline #dag

🧵[1/7] ✅ Full DAG

“dvc dag --full” shows the full DAG that the target stage belongs to, instead of showing only its ancestors.

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🦉 DVC is designed to improve upon past solutions to make the life of ML teams easier. Here’s how it differentiates from other related technologies:

🐱 Git
📦 Git-LFS (Large File Storage)
🗄 Git-annex

@Iterativeai @DVCorg
#mlOps #data #git #lfs

🧵[1/5] 🐱 Git vs DVC

DVC builds upon Git by the concept of data files – large files that should not be stored in a Git repository, but need to be tracked and versioned.

It leverages Git's features to enable managing different versions of data, data pipelines, and experiments.

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🦉 Here’s a quick brief from Iterative Community👇

🪐 GitHub Universe
🤝 Hacktoberfest Contributions
📹 Closure Session 2 event
♾️ CML at NeurIPS
🇬🇧 MLOps Summit London
🇺🇸 ODSC West

@Iterativeai @DVCorg
#mlOps #data #pipeline #dag

🧵[1/8] 🪐 Dmitry at GitHub Universe

Dmitry Petrov (@FullStackML) spoke at #GitHubUniverse on “ML with Git: experiment tracking in Codespaces” on 8th November.

He presented how to use the DVC extension for VS Code and Codespaces to streamline ML experimentation process.

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🦉Wanna get started with DVC on your own machine? Here's how you can get started👇

🍎 MacOS
🪟 Windows
🖥️ Linux
🐍 Python Package
👨🏻‍💻 VS Code Extension

@Iterativeai @DVCorg
#mac #windows #linux #code

🧵 [1/7] 🍎 MacOS

The recommended way to install dvc on Mac is using @MacHomebrew with command “brew install dvc” ☕️

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🦉 Here’s some exciting news brief this month from Iterative Community 👇

🤝 October Meetup
🎉 Hacktoberfest
🐲 DVC-Hydra Integration
♾️ CML in Research
📄 DVC Wikipedia Page

@Iterativeai @DVCorg
#mlOps #data #pipeline #dag

🧵 [1/7] 🤝 October Meetup

@NadiaIIT presented her team's research on Collaboration Challenges in Building ML-Enabled Systems in the October Meetup. If you missed it, visit .

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For all the devs out there willing to contribute to DVC, here is a quick guide to contributing to iterative/dvc repo
🐞 Open a new issue
💻 Set up a dev environment
🍴 Fork iterative/dvc
🧪 Add tests and run them locally
⬆️ Submit a pull request
@iterativeai

🧵 [1/7] 🐞 Open a new issue

Open a new issue in the issue tracker, whether it be a bug report or a feature request. 👇🏽
github.com/iterative/dvc/…

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🦉 Here are some of the cool commands you can try out right now in the DVC command line interface!

💻 dvc dag
🧊 dvc freeze
📦 dvc move
📊 dvc metrics show
🧹 dvc gc

🧵 [1/7] 💻 dvc dag

𝚍𝚟𝚌 𝚍𝚊𝚐 is very helpful in quickly checking out the stages of a pipeline up to the target stage in a simple visual representation. If the target is omitted, it will show the full project DAG.

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🦉 Did you hear? By popular demand, CML.dev now supports @Bitbucket Pipelines, rounding out our coverage of the leading version control platforms! 🎉

🧵 1/6 You can get started with CML in @Bitbucket by forking this repo and cloning it to your local workstation.

👉🏽 bitbucket.org/iterative-ai/e…

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🦉📣Today we introduce the DVC extension for VS Code! Now the power of DVC is magnified with @code !

🧵1/ 📒 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗕𝗼𝗼𝗸𝗸𝗲𝗲𝗽𝗶𝗻𝗴

Quickly run new experiments and compare their resulting metrics in the experiments table. Use the command palette or buttons to run new experiments, or add them to the queue for later.

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