Activeloop

Founded 2018

Store multimodal data and run continuous learning pipelines for artificial intelligence models

Pricing model

Free Trial

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Activeloop to build continuous learning workflows for artificial intelligence agents and machine learning teams. It combines Deeplake, a database built for vectors and tensors, with organizational memory tools to track agent behavior. You can stream dataset tensors directly to graphics processing units for model fine-tuning without downloading entire datasets locally. The system also records execution traces, benchmarks agent performance across iterations, and helps developers refine machine learning models and code kernels continuously based on actual production feedback.

What Activeloop does

Connect vector and tensor storage to model training pipelinesStream training batches directly to GPU clustersLog execution traces and agent prompt historiesQuery agent memory using SQL and vector searchesRun regression benchmarks on updated agent policiesSync shared skill repositories across development teamsStore vector embeddings and multidimensional tensors in a unified databaseStream training data directly to graphics processing units for model fine-tuningQuery datasets through a serverless PostgreSQL interfaceCapture and save interaction trajectories from autonomous agentsDistribute proven skills and execution traces across engineering teamsGenerate and test custom code kernels based on production feedbackBenchmark model updates against regression tests before deployment