Dotscience, a DevOps for machine learning (ML) provider, is emerging from stealth with its platform for collaborative, end-to-end ML data and model management.
Dotscience provides a tool that manages the complete AI lifecycle by empowering data scientists and ML engineers to work in ways in which they are familiar. Data science and ML teams can take advantage of a platform that is easy to use and provides a single place to collaborate on, develop, test, monitor and deliver their ML projects.
By giving teams the unique ability to collaboratively track runs—a record of the data, code and parameters used when training an AI model—Dotscience empowers ML and data science teams in industries including fintech, autonomous vehicles, healthcare and consultancies to achieve reproducibility, accountability, collaboration and continuous delivery across the AI model lifecycle.
The Dotscience platform is now available as SaaS or on-prem, and on the Amazon Web Services (AWS) Marketplace in August.
“The current state of AI development is a lot like software development in the 1990s. Before the movement called DevOps, modern best practices such as version control, continuous integration and continuous delivery were far less common and it was normal that software took six months to ship. Now software ships in minutes,” said Luke Marsden, founder and CEO of Dotscience. “At Dotscience, we are applying the same principles of collaboration, control and continuous delivery of DevOps to AI in order to simplify, accelerate and control AI development.”
Dotscience offers data science and ML teams the benefits that include:
- Seamless flexibility and integration all from one platform
- Optimal team productivity
- Flexible access to compute, hybrid cloud portability for ML development environments
- Ability to work with data from any source
- Allows AI and data science teams to use the tools they care about, while removing the obstacles that aren’t central to productivity
- Guarantees compliance with current and future regulation
Dotscience provides end-to-end ML lifecycle management without forcing users to change their working practices and this approach also extends to the installation options.
Customers can choose to deploy the hosted SaaS and bring their own compute, or install a fully private version of Dotscience either manually, or through the Dotscience installer in the AWS Marketplace.
This flexibility means that a broad userbase can access an integrated ML platform that provides unified version control and collaboration for data scientists.
For more information about this news, visit www.dotscience.com.