Activeloop
DoltHub
Iterative.ai
Looker
Managed MLflow
Domo
Sisense
Pachyderm
Colaboratory
Kaggle
Jupyter
replit
Explorium
Numerai
Infosec Skills
DataSource.ai
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:
Activeloop ColaboratoryBased on our record, Colaboratory seems to be a lot more popular than Activeloop. While we know about 210 links to Colaboratory, we've tracked only 4 mentions of Activeloop. 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 1 year 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 2 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 2 years ago
I'm Davit from Activeloop (activeloop.ai). Source: over 2 years ago
. Kaggle: For competitions and datasets. . GitHub: For open source projects and collaboration. . Colab: Google’s platform for building and sharing machine learning models. - Source: dev.to / 9 days ago
Model execution is about making your AI model available to the end user. Great results only your laptop or Google Colab notebook won't cut it in a business. The AI model has to integrate with your business IT if it's going to deliver value. - Source: dev.to / 26 days ago
Google Colab - Free Jupyter Notebooks development environment. - Source: dev.to / 4 months ago
To play with the dataset, we first must create a Jupyter notebook, a powerful and popular tool among data engineers. I created mine on Google Colab. - Source: dev.to / 5 months ago
Please head over to the Google Colab. - Source: dev.to / 6 months ago
DoltHub - DoltHub is where people collaboratively build, manage, and distribute structured data.
Kaggle - Kaggle offers innovative business results and solutions to companies.
Iterative.ai - Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
replit - Code, create, andlearn together. Use our free, collaborative, in-browser IDE to code in 50+ languages — without spending a second on setup.
