VP of Enterprise, Data, and Analytics at Red Hat
Retention of experienced data scientists and machine learning engineer. Data scientists and machine learning engineers are hard to find and retain, especially in the technology sector where the average tenure of data scientists/machine learning engineers is only two years. Companies need to spend lots of time and resources to hire data scientists, oftentimes fresh graduates, get them up to speed, and retain them over the years.
Collaboration and knowledge sharing. Internet and technology companies often have distributed workforce 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.
Access to flexible tools and scalable infrastructure. 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 need, they either cobble together what they need on local machines (shadow IT) or get bogged down, slowing growth and leading to frustration and turnover.
Easy access to open and flexible tooling options. Domino enables organizations to:
Domino provides the flexibility, agility, and scalability data science and machine learning team needs and supports dynamic tooling environment, 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.
Reproducibility and collaboration. 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.
Domino provides data scientists and machine learning engineers flexible access to various tools and packages, easy options to spin up the compute power needed and automated reproducibility and collaboration capabilities, all of which keep data scientists and machine learning engineers happy and productive at their job.