The 10 Hottest Machine Learning And Data Science Startups In 2021

Businesses looking to bring the rewards of big data analysis to everyday users need ways to prepare and organize data and develop machine learning models for analyzing it. 

Comet’s platform allows data scientists and machine learning teams to manage and optimize the entire machine learning life cycle with a single system, including model management, model production monitoring, as well as tracking data sets, code changes and experimentation history. 

Comet.ml

Databand’s unified data observability and machine learning development platform helps data engineers and data scientists identify, troubleshoot and fix data quality issues for data pipelines running on cloud-native systems such as Snowflake, Apache Spark and Apache Airflow. Databand.ai, founded in 2018 and based in Tel Aviv, Israel, raised $14.5 million in December 2020 in a Series A round of funding.

Databand.ai 

Datafold offers a data reliability platform that data engineers and data scientists use to manage data workflows and monitor and improve analytical data quality. The technology can reduce the number of data quality incidents that make it into production by a factor of 10, according to the company. In November Datafold, founded in 2020 and based in San Francisco, raised $20 million in Series A funding.

Datafold 

DotData develops what it calls AutoML 2.0 solutions for automating data science workflows. The dotData Enterprise machine learning and data science automation platform handles data ingestion and wrangling, automated feature engineering, AutoML and model operationalization tasks  

DotData 

Explorium develops an automated data acquisition platform for integrating a company’s internal data with thousands of external data sources for use by data scientists and business analysts. The Explorium technology portfolio also includes a powerful Auto ML engine for automated data discovery and feature generation, and the Signal Studio for finding and integrating the most relevant external data signals.

Explorium 

Iterative.ai builds open-source tools used to extend traditional development technologies for machine learning projects—especially ML projects involving unstructured data.  Iterative.ai’s portfolio includes the DVC version control system, Continuous Machine Learning (CML) for continuous integration/continuous delivery and deployment, and the just-released DVC Studio for project collaboration.  

Iterative.ai 

Prophecy.io provides a low-code data engineering platform for developing and deploying data pipelines used to manage streams of data for business analytics and machine learning tasks. The system combines visual drag-and-drop development with Agile software engineering practices.  In February Prophecy.io debuted a SaaS-based version of the platform built on Apache Spark, the open-source analytics engine.

Prophecy.io 

Tecton.ai exited stealth in April 2020 with its data platform for machine learning that’s designed to enable data scientists to turn raw data into the predictive signals that power machine learning models. The company’s goal is to solve the data challenges that constitute the biggest impediment to deploying machine learning in the enterprise. The company’s founders—CEO Mike Del Balso, CTO Kevin Stumpf and Engineering Vice President Jeremy Hermann—worked together at Uber when the ride sharing giant was building and deploy new machine learning models. 

Tecton.ai 

Spell.ml develops a machine learning platform for deep learning operations (DLOps) that the company says goes beyond traditional machine learning with its capabilities for preparing, training, deploying and managing the full life cycle of machine learning and deep learning models. Deep learning is a segment of the machine learning world that incorporates complex learning models that rely on AI-based neural networks.

Spell.ml 

The OctoML platform, built on the open-source Apache TVM framework, is used to deploy machine learning models on varied hardware configurations and provide automation and performance when bringing trained models to production. The platform supports cloud services and edge computing hardware endpoints and helps businesses and organizations optimize ML models to match edge resources.