Solutions Review Names 3 Data Science and Machine Learning Vendors to Watch, 2022

Solutions Review’s Data Science and Machine Learning Vendors to Watch is an annual listing of solution providers we believe are worth monitoring. Companies are commonly included if they demonstrate a product roadmap aligning with our meta-analysis of the marketplace. Other criteria include recent and significant funding, talent acquisition, a disruptive or innovative new technology or product, or inclusion in a major analyst publication.

Data science and predictive analytics is one of the fastest-growing industries in the world. The field touts a burgeoning citizen data and enterprise software market mature with product options for an array of personas and use cases. AI and machine learning are major enablers here, both in terms of complexity and quality of output.  Complexity of analysis and automation are key buying drivers based on our meta-analysis. The amount of innovation happening in the development community will continue to vastly outpace mainstream adoption for at least several more years.

These data science and machine learning Vendors to Watch have met at least two of our five points of inclusion and represent to some degree the evolution of the marketplace. It’s in that spirit we turn our attention to the immediate future. Providers are listed in alphabetical order. Provider names and logos are linked so you can learn more.

ChaosSearch

ChaosSearch is a massively scalable ELK-compatible log analysis platform delivered as a fully managed service. The product enables search and analysis of a customer’s cloud data via a proprietary UltraHot universal data format and associated architecture that allows for direct and accelerated analytics. ChaosSearch is stateless and decouples storage from compute. It also streamlines and automates the data management process within your own S3 account. No data movement, transformation, or schema definition is required.

Datatron

Datatron offers an enterprise AI platform that streamlines machine learning operations (MLOps) and governance workflows. The solution simplifies the process of putting models into production, including cataloging, provisioning, and management. Datatron also helps customers catalog, provision, and manage models without ad hoc scripting or manual processes. Enterprise capabilities enable the management of more models in production faster, easier, and for more teams.

Molecula

Molecula offers a commercial version of the open-source data format Pilosa, which touts a cloud-agnostic and compute-ready data layer for advanced analytics and AI. Data engineers and data scientists can use this system continuously to extract and update features into a centralized feature store. The feature store, according to Molecula can reduce the data footprint “by 60-90 percent” and provides a secure data format for sharing.