Show HN: All Vision – Find dynamic patterns in data with no code

Show HN: All Vision – Find dynamic patterns in data with no code(all.vision)

12 points by chetan_v 4 years ago | 7 comments

Does it handle sequential data, I.e a csv with columns such as — patient_id, date, event_type

chetan_v 4 years ago |

Hi HN! We are Joe and Chetan, co-founders of All Vision Technologies (https://all.vision/). We met when working at Capital One and noticed tooling around unsupervised learning for building and deploying models left a lot to be desired. As a result unsupervised learning seems to be very underutilized in industry.

We left Capital One to work on this problem full time and have been working with great customers like the US Space Force. We just launched our Unsupervised Model Zoo, which runs and compares 12 clustering and anomaly detection algorithms. Also, we implemented cluster tracking over time to show things like conceptual drift or changing populations in your dataset.

This tool with one click lets data scientists clean data (automatically!), set up cloud and processing around their models, explore differences between clusters, and work faster.

What I'm proudest of: We rebuilt many of these algorithms from scratch, achieving up to 10x speedups in comparison with open-source implementations like Nvidia Rapids, you can read more about how we did this in our technical paper here: (https://www.all.vision/technical-whitepaper).

Welcome to our first milestone in our journey of building powerful and easy to use unsupervised learning tools.

We have a free trial for anyone who signs up from this post. To get started, make an account at (https://all.vision/) and upload any unlabeled dataset to find patterns inside your data set.

slava_kiose 4 years ago |

Hi! Tell me, where do you use your unique tool?

katie-k 4 years ago |

Why is this better than sci-kit learn?

chetan_v 4 years ago | |

Thanks for the question, unlike sci-kit learn we have focused on building better hyper parameter tuning and continuous streaming unsupervised learning rather than one time batch unsupervised learning, this allows us to much better track patterns over time. We also have several models not on sklearn and even for the ones that are there, we have reimplemented them with better GPU support. You can also find some more info on that here : https://www.all.vision/technical-whitepaper

katie-k 4 years ago | | |

Thanks - I'll check it out

Amin699 4 years ago |

best of luck