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:
Based on our record, Wavve should be more popular than Activeloop. It has been mentiond 8 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 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
Wavve will also make audiograms for social media (https://wavve.co/). Source: over 1 year ago
Headliner.app and wavve.co do the promotion and nothing more. Source: over 1 year ago
I can't tell you how many "consultants" told us that what we were doing wasn't worth the effort because a podcast host was going to build this feature that would make Wavve obsolete. Well, they all did build that feature but they all built poor versions of it and customers still came to us. Source: over 2 years ago
And paid niche is already relatively saturated with already big products e.g. https://veed.io https://headliner.app https://wavve.co and several others, so competing seemed like an uphill battle... Source: over 2 years ago
In the early days for Wavve& Zubtitle it was direct sales/outreach. We would find people on social media promoting their podcast or video and pitch our tools to them. Source: almost 3 years ago
DoltHub - DoltHub is where people collaboratively build, manage, and distribute structured data.
Headliner - Promote your podcast, radio show or blog with video
Iterative.ai - Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.
VEED - Simple Online Video Editing
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.
EchoWave.io - Online video maker, with Music Visualizer, and editing tools.