DVC AI
Was ist DVC AI?
Ein Sortiment von Tools für das ML-Datenmanagement, das Verfolgen von Experimenten und die Automatisierung von Pipelines.
Wie benutzt man DVC AI?
1. Versionieren Sie Ihre Daten und ML-Experimente. 2. Automatisieren Sie Compute-Ressourcen in jeder Cloud. 3. Verfolgen und verwalten Sie Ihre ML-Experimente.
DVC AI's Hauptmerkmale
Milliarden von Dateien verarbeiten
Eigene Einbettungen
Automatische Beschriftungen
Verringerung von Bias
Entfernen von nahezu Duplikaten
Versionierung und Teilen
Verwenden Sie Git als einzige verlässliche Quelle
DVC AI's Anwendungsfälle
Vorverarbeitung und Verwaltung von Daten
Verfolgen von Experimenten
Versionierung von ML-Modellen
Automatisierung von Pipelines
FAQ von DVC AI
Was ist DVC AI?
Was sind die Kernfunktionen von DVC AI?
Was sind die Anwendungsfälle von DVC AI?
Wie kann ich DVC AI verwenden?
DVC AI Discord
Hier ist der DVC AI Discord: https://discord.com/invite/dvwXA2N. Für weitere Discord-Nachrichten klicken Sie bitte hier(/de/discord/dvwxa2n).
DVC AI Support-E-Mail & Kundendienstkontakt & Rückerstattungskontakt usw.
Weitere Informationen zu Kontakt finden Sie auf der Kontaktseite (https://dvc.ai/#learn_more)
DVC AI Preise
DVC AI Preislink: https://dvc.ai/pricing
DVC AI Youtube
DVC AI Youtube link: https://www.youtube.com/channel/UC37rp97Go-xIX3aNFVHhXfQ
DVC AI Linkedin
DVC AI Linkedin link: https://www.linkedin.com/company/iterative-ai/
DVC AI Twitter
DVC AI Twitter link: https://twitter.com/DVCorg
DVC AI Github
DVC AI Github link: https://github.com/iterative
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Data Versioning in Generative AI: A Pathway to Cost Effective ML
This talk was presented at the 2023 MLOps World and Generative AI World Summit. Dmity Petrov shares on his learnings over 5 years of creating and maintaining DVC and how we can now tackle the management of unstructured Generative AI data in a cost-effective way. Talk Abstract: For 5 years we have been building DVC and we know how data versioning helps teams. The evolving Generative AI workflows are different and require an evolution of versioning workflows to accomplish Generative AI goals. This new era thrives on vast amounts of unstructured data, which include everything from images, videos, and audio, to MRI scans, document scans, and plain text dialogues. This data, often scaling into billions of objects, together with the resource-consuming task of scoring models on expensive GPU hardware or using model APIs like ChatGPT, brings forth unique challenges in the field of data management and versioning. In this talk, we will delve into data versioning in the context of generative AI. Our focus will be on strategies that assist businesses in minimizing their processing time and the volume of API calls to external models like ChatGPT, resulting in substantial cost savings. Furthermore, we will discuss effective methodologies for sharing datasets amongst ML researchers to promote seamless collaboration. Lastly, we will examine the pivotal transformations generative AI has introduced to data versioning in the past year including annotations and embeddings versioning. Together, these insights will provide attendees with an in-depth understanding of the rapidly evolving data management landscape in the era of generative AI. What You’ll Learn 1. How data management is different in the Generative AI environment compared to traditional ML 2. How to save cost on compute and API calls using data versioning 3. Dataset sharing in the team as a way to improve collaboration 4. How to efficiently version annotation, embeddings, and auto-labels together with data To learn more about our approach see the recent research paper that we presented as a workshop at ICCV in Paris: https://arxiv.org/abs/2309.11608 DVC delivers the best practices of software engineering to your machine learning workflows; setting you up for success! Learn more about our ecosystem of tools at https://dvc.ai *Try out the DVC Extension for VS Code here:* https://marketplace.visualstudio.com/items?itemName=Iterative.dvc To learn more about Iterative's open-source and SaaS tools please visit: 🧑🏽💻 *Our free online course:* https://learn.iterative.ai ✍🏼 *Our docs:* https://dvc.org/doc (Data Version Control, Pipelines, Experiments) https://cml.dev/doc (CI/CD for Machine Learning) https://studio.iterative.ai (Team Collaboration, Experiments, Model Registry) *Join the Community on our Discord server:* https://discord.gg/W49xzNmycw #dvc #machinelearning #datascience #generativeai
DVC Extension for VS Code Update: Plots Wizard
In this video demo, Julie Galvan showcases the latest update to the DVC VS Code extension: a new Plots Wizard. Now, you can easily add plots to your dvc.yaml file. Julie guides you through the process, from selecting data files, and the plot template, to picking metrics for axes, which produces the code in your dvc.yml file and ultimately your plot. You can find more info in this release in the DVC for VS Code v. 1.0.54. Release notes: https://github.com/iterative/vscode-dvc/releases/tag/1.0.54 DVC delivers the best practices of software engineering to your machine learning workflows; setting you up for success! Learn more about our ecosystem of tools at https://dvc.ai *Try out the DVC Extension for VS Code here:* https://marketplace.visualstudio.com/items?itemName=Iterative.dvc To learn more about Iterative's open-source and SaaS tools please visit: 🧑🏽💻 *Our free online course:* https://learn.iterative.ai ✍🏼 *Our docs:* https://dvc.org/doc (Data Version Control, Pipelines, Experiments) https://cml.dev/doc (CI/CD for Machine Learning) https://studio.iterative.ai (Team Collaboration, Experiments, Model Registry) *Join the Community on our Discord server:* https://discord.gg/W49xzNmycw #dvc #machinelearning #datascience #generativeai
DataChain Open-Source Release - A new way to manage your Unstructured Data
DataChain Open-Source Release - A new way to manage your Unstructured Data ⭐️ Star the repo and get started: https://github.com/iterative/datachain See Dmitry's blog post on the thought process behind the tool: https://dvc.ai/blog/datachain-release Find out more at https://dvc.ai
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