Comprehensive list of data science platforms. Sometimes known also as machine learning platforms, ai platforms or DSML platforms.
List of data science platforms
Read reviews about data science platforms when they exist, or visit on their home page.
| Data science platform | Type | Description | |
|---|---|---|---|
| Abacus.ai | MLOps | End-to-end MLOps platform. | |
| Anaconda Cloud | Data Science | End-to-end data science platform. | |
| Azure ML | Full stack | Full stack data science in Azure cloud | |
| AWS Sagemaker | Full stack | Full stack data science in AWS | |
| Big ML | Full stack | Comprehensive Machine Learning Platform. | |
| Bodo | Data processing | Faster alternative for Spark to run massive ETL jobs in Python | |
| ClearML | Full stack | Robust MLOps platform for end-to-end solutions | |
| cnvrg.io | MLOps | MLOps platform with the focus on Kubernetes deployments | |
| Cocalc | Workspace | Collaborative Calculation and Data Science. | |
| Databricks | Full stack | Managed Spark with comprehensive offering | |
| Dataiku | Low-code | Large ecosystem for citizen data scientists | |
| Datalore | Full stack | Collaborative data science platform with great notebook experience | |
| Datrics | Low-code | A no-code platform for analytics and data science. | |
| Deep block | Low-code | Train AI models without coding. | |
| Fosfor Refract | Full stack | ||
| GCP BigQuery | Database | Integrated functionalities for ML | |
| GCP Vertex AI | Full stack | Emphasize on Docker and Tensorflow | |
| Google Colab | Full stack | Free platform for researchers and students | |
| HPE Ezmeral ML Ops | Full stack | ||
| IBM Watson Studio | Full stack | Renamed from Data Science Experience platform to current name on 2018. | |
| Jupyter in Docker | Workspace | Free open source option for developers | |
| Livebook in fly.io | Workspace | Write interactive and collaborative code notebooks | |
| Neptune AI | MLOps | Log experiments and ML models versions from any environment. | |
| NimbleBox.ai | Full stack | ||
| Paperspace | Full stack | ML platform with their own data centers and IPU processors | |
| PyScript | Workspace | Python scripts in browser UI, By Anaconda | |
| Python Anywhere | Workspace | Host, run, and code Python in the cloud | |
| Saturn Cloud | Full stack | Data science workspace with Dask cluster | |
| SmartPredict | Full stack | - |
Pipelines, infrastructure and development
This site is more focused on data exploration and business value than infrastructure. For this reason, these platforms are not top priority.
| Platform | Description | |
|---|---|---|
| ActiveLoop | PyTorch data loader | |
| Anyscale | Managed Ray from the library founders. one of the founders is also Databricks founder. | |
| Baseten | Baseten is the simplest way to put a model behind an API or webapp hosted on fully managed, scalable infrastructure. | |
| Bento ML | Open source library and possibility for their cloud product | |
| Cencius | Model monitoring. | |
| Fiddler | Model monitoring | |
| [Meltano](CLI & version control for ELT) | CLI & version control for ELT | |
| MetaFlow | Makes it quick and easy to build and manage real-life data science and ML projects. Originally from Netflix. | |
| ModelOp | Enterprise ModelOps platform to govern and scale AI initiatives | |
| Navio, craftworks | Manage, deploy and monitor Machine Learning models | |
| Nvidia Triton Inference server | Inference serving software that helps standardize model deployment and execution | |
| OctoML | Automate Model Deployment at Peak Performance | |
| Pachyderm | Combined ETL and MLOps pipelines | |
| Polyaxon | Reproduce, automate, and scale your data science workflows | |
| Qwak | ML + data ETL | |
| Run.ai | GPU Orchestration Platform for AI/ML Teams | |
| Sagify | MLOps for AWS | |
| ScaleTorch | PyTorch at scale | |
| SegMind | Optimization and deployment platform for generative AI | |
| Seldon | Manage and audit machine learning pipelines | |
| Square Factory | End-to-end MLOps platform | |
| Superwise | ML monitoring | |
| Teachable Hub | Fully-managed platform bringing ML teams together to deploy, serve, and share models | |
| Tecton | Design and Manage the Entire ML Feature Lifecycle | |
| Ultralytics | Deploy YOLO models to mobile apps | |
| Union | Managed Flyte | |
| Valohai | A platform that stores all knowledge |
Data science communities
| Community | Description |
|---|---|
| Hugging Face | Model sharing |
| Kaggle | Machine learning competitions |
Use case specialized solutions
This site is focused on data science platforms that solve generic business problems. These platforms will not be reviewed.
| Platform | Type | Description | ||
|---|---|---|---|---|
| Clarifai | Computer vision | Platform for computer vision, natural language processing, and automatic speech recognition. | ||
| Cogniac | Computer vision | Computer vision platform. Partnered with Cisco. | ||
| Hasty | Computer Vision | Deploy AI vision solutions faster from manufacturing industry | ||
| Krista | Process automation | Orchestrates business processes across people and apps | ||
| Patterns | Process automation | App automation including GPT |
Databases
Many massive scale databases have machine learning capabilities. Their main task is to store and retrieve data.
- Snowflake
- AWS Redshift
- Azure Synapse
Legacy and bloated data science platforms
This site reviews modern data science platforms with clear focus and transparent pricing. Thus, these platforms are excluded:
- 1010data
- Altair
- Alteryx
- DataRobot
- Domino Data Lab
- H20
- KNIME
- MatLab
- Rapid Miner
- SAS
- Tibco
Data processing platforms
Data processing platforms are not recognized for data science work and will not be reviewed on this site.
- Alooma
- ETLeap
- Fivetran
- Matillion
- DataMechanics
- Stitch
- Talend
- xplenty
Business Intelligence platforms
Data processing platforms are meant for simpler reporting and will not be reviewed.
