Question: I need a solution that enables collaborative development of AI models across data, ML, and embedded teams - do you know of any platforms that offer this?

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Hugging Face

If you want a platform that lets data, ML and embedded teams collaborate on AI model development, Hugging Face is a top contender. The open-source platform is a full-fledged ecosystem for model collaboration, dataset exploration and app development. With more than 400,000 models, 150,000 apps and access to more than 100,000 public datasets, Hugging Face has a lot of collaborative horsepower. It's got optimized compute options, single sign-on and private dataset management, too, so it's good for both solo developers and companies.

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Humanloop

Another strong contender is Humanloop. It's geared for managing and optimizing the development of Large Language Models (LLMs) apps. It's got a collaborative prompt management system with version control and history tracking, an evaluation and monitoring suite, and customization and optimization tools. Humanloop can integrate with popular LLM providers and has Python and TypeScript SDKs for easy integration. It's geared for teams building AI features, and notable customers include Athena, Duolingo, Gusto and Oviva Group.

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Domino Data Lab

If you want a governed environment, check out Domino Data Lab. It's designed to work with a broad ecosystem of tools and infrastructure so you can get the best of both worlds in terms of best practices, reproducibility and governance. It's got features like hybrid and multi-cloud deployment, integrated workflows and the ability to give data scientists the autonomy they need while keeping IT secure and compliant. It's geared for life sciences, financial services and manufacturing customers, but can be used in hybrid multi-cloud environments.

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Dataloop

Last, Dataloop is a platform that combines data curation, model management, pipeline orchestration and human feedback to speed up AI app development. It includes data management for exploring and analyzing large amounts of unstructured data, automated preprocessing and model management for deploying and managing AI models. Dataloop supports a variety of unstructured data types and has strong security controls, so it's a good option for those who want to improve collaboration and speed up development.

Additional AI Projects

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TeamAI

Collaborative AI workspaces unite teams with shared prompts, folders, and chat histories, streamlining workflows and amplifying productivity.

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Together

Accelerate AI model development with optimized training and inference, scalable infrastructure, and collaboration tools for enterprise customers.

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MLflow

Manage the full lifecycle of ML projects, from experimentation to production, with a single environment for tracking, visualizing, and deploying models.

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Dataiku

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Abacus.AI

Build and deploy custom AI agents and systems at scale, leveraging generative AI and novel neural network techniques for automation and prediction.

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HoneyHive

Collaborative LLMOps environment for testing, evaluating, and deploying GenAI applications, with features for observability, dataset management, and prompt optimization.

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Klu

Streamline generative AI application development with collaborative prompt engineering, rapid iteration, and built-in analytics for optimized model fine-tuning.

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Clarifai

Rapidly develop, deploy, and operate AI projects at scale with automated workflows, standardized development, and built-in security and access controls.

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LastMile AI

Streamline generative AI application development with automated evaluators, debuggers, and expert support, enabling confident productionization and optimal performance.

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Athina

Experiment, measure, and optimize AI applications with real-time performance tracking, cost monitoring, and customizable alerts for confident deployment.

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MindsDB

Connects data to AI with 200+ integrations, allowing developers to create tailored AI solutions using their own enterprise data and multiple AI engines.

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Google AI

Unlock AI-driven innovation with a suite of models, tools, and resources that enable responsible and inclusive development, creation, and automation.

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UBOS

Build and deploy custom Generative AI and AI applications in a browser with no setup, using low-code tools and templates, and single-click cloud deployment.

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LLMStack

Build sophisticated AI applications by chaining multiple large language models, importing diverse data types, and leveraging no-code development.

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Watsonx

Unlock AI's full potential with Watsonx, a scalable platform for building, operating, and governing AI systems.

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MindStudio

Create custom AI applications and automations without coding, combining models from various sources to boost productivity and efficiency.

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SuperAnnotate

Streamlines dataset creation, curation, and model evaluation, enabling users to build, fine-tune, and deploy high-performing AI models faster and more accurately.

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Cerebras

Accelerate AI training with a platform that combines AI supercomputers, model services, and cloud options to speed up large language model development.

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Appen

Fuel AI innovation with high-quality, diverse datasets and a customizable platform for human-AI collaboration, data annotation, and model testing.

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Encord

Streamline computer vision development with automated labeling, data management, and model testing tools to build more accurate models faster.