The practical guide to accelerating the data science lifecycle

The data science lifecycle (DSLC) is a series of iterative steps to create, deploy and monitor an analytical model. While the need to accelerate and optimize the DSLC to achieve high model velocity has always been important, it became absolutely critical for businesses in the wake of the pandemic. As one energy company executive put it, “We have our long-term forecasts around the amount of energy that is going to be in demand. COVID came and ate that for lunch."

In this paper you will:

• Take a deep dive into the four phases of the Data Science Lifecycle
• Discover the best-practices in each phase of the DSLC to help you accelerate data science in your organization.
• Learn how adopting MLOps practices and technology unlocks productivity and removes friction from the DSLC.