Simplifying feature engineering for building real-time ML pipelines might just be the next holy grail of data science. It’s incredibly difficult and highly complex, but it’s also desperately needed for multiple use cases across dozens of industries. Currently, feature engineering is siloed between data scientists, who search for and create the features, and data engineers, who rewrite the code for a production environment.

When it comes to machine learning (ML) in the enterprise, there are many misconceptions about what it actually takes to effectively employ machine learning models and scale AI use cases. When many businesses start their journey into ML and AI, it’s common to place a lot of energy and focus on the coding and data science algorithms themselves.

With any transformation in industry or marketplace, there are leaders and losers. The winners know the fundamental pillars that are hidden to some and evident to others that drive and enable success.

Apache Spark is a fast and general-purpose engine for large-scale data processing. It’s most widely used to replace MapReduce for fast processing of data stored in Hadoop. Designed specifically for data science, Spark has evolved to support more use cases, including real-time stream event processing. Spark is also widely used in AI and machine learning applications.

Bring life to your data visualizations and dashboards to create compelling, crowd-pleasing presentations that your managers will love.

A cloud-native data stack equips a construction company with better business intelligence to guide planning and decision-making.