Software Alternatives & Reviews

Activeloop VS Iterative.ai

Compare Activeloop VS Iterative.ai and see what are their differences

Activeloop logo Activeloop

Data lake for machine and deep learning. The fastest dataset management tool for computer vision.

Iterative.ai logo Iterative.ai

Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.
  • Activeloop Landing page
    Landing page //
    2021-09-20

About

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.

Activeloop supports the following use cases:

Machine Learning teams can apply Activeloop's data infrastructure to ship their models fast in the following use cases:

  1. AgriTech
  2. Audio processing
  3. Autonomous Vehicles & Robotics
  4. Biomedical and Healthcare ML
  5. Multimedia: Image enhancement, video enhancement, face detection, sports analytics, or machine learning for AR/VR
  6. Safety & Security: surveillance machine learning with biometrics, facial recognition, or crowd counting
  • Iterative.ai Landing page
    Landing page //
    2023-08-18

Activeloop videos

Activeloop Product Demo Video

Iterative.ai videos

Reimagining DevOps for ML by Elle O'Brien, Iterative.ai

Category Popularity

0-100% (relative to Activeloop and Iterative.ai)
Machine Learning Tools
25 25%
75% 75
Data Science And Machine Learning
Data Science Notebooks
15 15%
85% 85
Machine Learning
31 31%
69% 69

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Activeloop and Iterative.ai

Activeloop Reviews

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Iterative.ai Reviews

  1. Rare

    The people at iterative.ai are special.

    Its hard to describe quickly, but if you're a potential client or employee--you could easily go your entire career unaware that groups like this exist.

    Their tools (like DVC) are exceptional, but I write this review because one need only interact with the people there to understand why they're execptional.

    The culture there is one that can only exist when the founding talent is top-tier. The experience you'll have, though, is so much more than that.

    Recommend whole-heatedly.

    👍 Pros:    Constantly improving|Quality|Community

Social recommendations and mentions

Based on our record, Iterative.ai should be more popular than Activeloop. It has been mentiond 6 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.

Activeloop mentions (4)

  • [P] I built a Chatbot to talk with any Github Repo. 🪄
    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 1 year ago
  • [D] NLP has HuggingFace, what does Computer Vision have?
    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 2 years ago
  • [P] Database for AI: Visualize, version-control & explore image, video and audio datasets
    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: about 2 years ago
  • [P] Database for AI: Visualize, version-control & explore image, video and audio datasets
    I'm Davit from Activeloop (activeloop.ai). Source: about 2 years ago

Iterative.ai mentions (6)

  • Work with Google Drive files locally
    PyDrive2 is am open-source python package maintained by the awesome people at Iterative. And it is very easy to install:. - Source: dev.to / about 1 year ago
  • Any MLOps platform you use?
    These three are made by Iterative.ai, and seem like very clean implementations of MLOps tooling - especially if you aren't dealing with massive data. https://iterative.ai/. Source: about 1 year ago
  • How does your data science team collaborate?
    For what it's worth. (Full disclosure: I'm the community manager at Iterative (DVC,et.al.) Just wanted to make you aware of our online course (free) that we created specifically for Data Scientists (https://learn.iterative.ai). We know that bridging the gap between prototype to production/ jupyter notebook to reproducible/software engineering compatible, is a challenge. That's why we created the course. To also... Source: over 1 year ago
  • Advice about Infra and IaC
    What do you think of iterative.ai tools like dvc or cml? I have no direct experience, but I am looking at setting up something similar to what you need for a personal project. Source: almost 2 years ago
  • TPI - Terraform provider for ML/AI & self-recovering spot-instances
    Hey all, we (at iterative.ai) are launching TPI - Terraform Provider Iterative https://github.com/iterative/terraform-provider-iterative. Source: about 2 years ago
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What are some alternatives?

When comparing Activeloop and Iterative.ai, you can also consider the following products

Pachyderm - Pachyderm is an open source analytics engine that uses Docker containers for distributed computations.

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

Managed MLflow - Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.

DoltHub - DoltHub is where people collaboratively build, manage, and distribute structured data.

neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

Weights & Biases - Developer tools for deep learning research