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.
In Kong, plugins can be thought of as policy enforcers. In the case of rate limiting, Kong offers two plugins: An open source one and Enterprise. Both plugins can limit requests per consumer, route, service or globally. Configuring the same plugin is also possible on a more than level. When this occurs, an order of precedence is used to determine which configuration to run. With this capability, it is possible to apply fine-grained policy control. In this article, we cover an advanced use case.
For healthcare and life sciences organizations, 2020 began like many other years. They looked to the year ahead and focused on how to best serve their patients, members, and other constituents through their latest innovation. As we now know, nothing about 2020 was ordinary, especially for leaders within these industries.
Continuous Integration (CI), Continuous Delivery (CD) and Continuous Deployment (CD) are three processes that automate integrating software development into a product and deploying the modified product to test and production environments.
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain futured. However, the sudden surge in demand comes with its own set of challenges.
