Data Science Platform for Media & Technology Companies

Data scientists and machine learning engineers are hard to find and retain, especially in the technology sector where their average tenure is only two years. Companies spend lots of time and resources recruiting and hiring data scientists, getting them up to speed, and working to retain them over the years.

Internet and technology companies often have distributed workforces throughout the world, which makes it harder to collaborate and transfer knowledge. Working in silos is a drag on productivity and makes it harder to understand who’s doing what. Hindered collaboration also makes it hard to onboard new people, so everyone starts projects from scratch because they can’t find or re-run old work. Lack of information and expertise sharing can cause duplication of work among different teams as well as loss of institutional knowledge when key personnel leave the company.

Data science and machine learning require far more flexibility and scalability with infrastructure than domains like software development and BI. If data scientists/machine learning engineers can’t use the tools they want, they either cobble together what they need on local machines (shadow IT) or get bogged down, slowing growth and leading to frustration and turnover.

Domino enables organizations to:

• Try new tools/packages quickly and easily.
• Get access to centralized resources as easily as working on a local machine.
• Avoid lock-in to proprietary technologies.

Domino provides the flexibility, agility, and scalability that data science and machine learning teams need and supports dynamic tooling environments, diverse skill sets and preferences. Data scientists and machine learning engineers can do exploratory data analysis and model development without configuring and using their own compute resources. They can spin up high-powered workspaces with a single click, without needing help from Engineering.

Domino enables data science and machine learning teams to conduct collaborative, reproducible research. Data scientists and machine learning engineers gain automated reproducibility for code, data and environment configurations. They can discover, reproduce, and iterate on prior work, experiment with new techniques and re-run the model as new data comes in. Different team members can share, comment and collaborate on projects at every stage of the model development lifecycle. Data scientists and machine learning engineers can build off past knowledge and improve instead of reinventing the wheel.