BigML vs Deep Lake
Compare data AI Tools
0% Similar based on 0 shared tags
BigML
End to end machine learning platform with GUI and REST API that covers data prep modeling evaluation deployment and governance for cloud or on premises use.
Pricing Free trial, contact sales
Category data
Difficulty Beginner
Type Web App
Status Active
D
Deep Lake
Vector database and data lake for AI that stores text images audio video and embeddings in one place with fast dataloaders and RAG friendly tooling.
Pricing Free / $40 per month
Category data
Difficulty Beginner
Type Web App
Status Active
Feature Tags Comparison
Only in BigML
machine-learningautomlapideploymentsgovernancecloud
Shared
None
Only in Deep Lake
vector-dbdata-lakeragembeddingsmultimodal
Key Features
BigML
- • GUI and REST API for the full ML lifecycle with reproducible resources
- • AutoML and ensembles
- • Time series anomaly detection clustering and topic modeling
- • WhizzML to script and share pipelines
- • Versioned immutable resources
- • Organizations with roles projects and dashboards
Deep Lake
- • Multimodal storage for text images audio video and embeddings in one dataset
- • Vector search with metadata filters for precise retrieval at scale
- • Native dataloaders for PyTorch and TensorFlow to stream training batches
- • Dataset versioning and time travel for reproducibility and audits
- • Namespaces roles and tokens to isolate apps and teams
- • Python SDK and REST that unify ingest index and query
Use Cases
BigML
- → Stand up a governed ML workflow
- → Automate repeatable training and evaluation with WhizzML
- → Detect anomalies for risk monitoring
- → Forecast demand with time series
- → Cluster customers and products
- → Embed predictions through the REST API
Deep Lake
- → Build RAG assistants grounded in governed documents
- → Fine tune vision language models with streamed tensors
- → Centralize product FAQs PDFs and images for support bots
- → Prototype semantic search across tickets and chats
- → Keep training and inference data in one lineage aware store
- → Migrate from brittle pipelines to unified multimodal datasets
Perfect For
BigML
Data scientists, analytics engineers, and ML platform teams who want a standardized GUI plus API approach to build govern and deploy models
Deep Lake
ml engineers data engineers applied researchers platform teams and startups that need one store for raw data plus embeddings with fast training hooks
Capabilities
BigML
AutoML and Models Professional
Pipelines with WhizzML Professional
Cloud or Private Enterprise
Versioning and Roles Professional
Deep Lake
Multimodal Datasets Professional
Vector Search Professional
Zero copy Dataloaders Intermediate
Versioning and Quotas Intermediate
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