Evolving Data Science in the Cloud: Five factors for success

Analytics is evolving. Self-service is a top priority, and demand for more advanced analytics, such as machine learning and AI, is increasing.

Today, many organizations are creating multiplatform environments to support machine learning and other advanced analytics. The cloud is an important piece of this strategy. The cloud has numerous benefits for advanced analytics; top benefits include scalability and elasticity.

When you need to perform analytics processing on a large data set and iterate on that analysis, the cloud enables you to procure as much storage and compute services as necessary. This is critical for analytics and especially for compute-intensive data science initiatives.

This TDWI Checklist Report examines five best practices for utilizing the cloud for data science – including evaluating use cases to run in the cloud, cloud computing architectures, and planning considerations.