Activeloop
Iterative.ai
Pachyderm
Scale
DoltHub
Snowflakepowe.red
Open Wearables
Terra - Connect Widget
Validic
Sahha
Activeloop provides an optimized format for unstructured data, so users can stream their machine learning datasets while training ML models in PyTorch and TensorFlow. Activeloop acts as a data lake for deep learning on unstructured data and offers in-browser dataset visualization, querying, and version control. On top of those features, Activeloop integrates with experimentation and labeling tools to allow rapid iteration on computer vision datasets.
Machine Learning teams can apply Activeloop's data infrastructure to ship their models fast in the following use cases:
Open Wearables is a self-hosted platform that provides unified API access to data from major wearable devices and fitness platforms. It handles OAuth authentication, data normalization, and syncing, eliminating the need to integrate each platform separately.
Why Open Wearables? Building health apps with wearable data shouldn't take months. Open Wearables eliminates the integration nightmare by providing:
Activeloop Open WearablesNo features have been listed yet.
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Open Wearables's answer:
Open Wearables is the only open-source, self-hosted API platform for wearable health data. Unlike proprietary SaaS solutions, developers get full source code access, deploy on their own infrastructure, and avoid vendor lock-in. Built by healthcare AI experts with ISO 13485 certification, it provides AI-ready data schemas and built-in compliance features that generic platforms lack.
Open Wearables's answer:
No per-user pricing that eats into your margins. Full data ownership and control. Deploy in weeks instead of months compared to building in-house. Unlike Terra, Junction, or Spike, you're not locked into a vendor's roadmap or pricing changes. Open source means community contributions, transparency for security audits, and the ability to customize for your specific use case.
Open Wearables's answer:
B2B developer teams at HealthTech startups or scaleups, fitness apps, and longevity platforms who need wearable data integration. Primary decision makers are CTOs evaluating build vs buy, product teams needing faster time-to-market, and technical founders without deep health data expertise.
Open Wearables's answer:
Momentum's 120+ healthcare AI developers kept encountering the same problem across client projects - every company needed wearable integrations, but each device had different APIs, data formats, and OAuth flows. Clients were spending weeks on basic integration before they could focus on their core product. We built Open Wearables to solve this recurring challenge and open-sourced it to help the entire developer community.
Open Wearables's answer:
PostgreSQL and TimescaleDB for time-series health data storage, FastAPI for developer-friendly REST endpoints, Docker for containerized deployment. OAuth 2.0 for secure user authorization across all supported platforms.
Open Wearables's answer:
Currently in MVP launch phase (December 2025) with early adopters in private beta. Momentum's existing healthcare AI clients transitioning to unified platform Active discussions with longevity platforms, fitness apps, and clinical trial companies. Growing developer community on GitHub with 200+ early registrations.
Based on our record, Activeloop seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake. The chatbot searches a dataset stored in Deep Lake to find relevant information and generates responses based on the user's input. Source: about 3 years ago
u/Remote_Cancel_7977 we just launched 100+ computer vision datasets via Activeloop Hub yesterday on r/ML (#1 post for the day!). Note: we do not intend to compete with HuggingFace (we're building the database for AI). Accessing computer vision datasets via Hub is much faster than via HuggingFace though, according to some third-party benchmarks. :). Source: about 4 years ago
Hub, our open-source package, lets you stream datasets while training to PyTorch/TensorFlow. Check out how we achieved 95% GPU utilization while training on ImageNet at 50% less cost. We're building the Database for AI, with everything it should contain. If there's an adjacent feature that would make it more useful for your workflow, do let us know! Source: over 4 years ago
I'm Davit from Activeloop (activeloop.ai). Source: over 4 years ago
Iterative.ai - Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.
Terra - Connect Widget - Terraโs widget makes it easy to connect your app to all wearables.
Pachyderm - Pachyderm is an open source analytics engine that uses Docker containers for distributed computations.
Validic - Validic offers a mobile health API connection that enables healthcare companies to access data from mHealth apps and devices.
Scale - Get human tasks done with just one line of code.
Sahha - Connect to 200+ Wearables & Health Data Sources with One API
