I have been managing R&D teams for the past 14 years or so and have learned many lessons along the way. Some of the best lessons have come about in the moment when your software meets the real world and you find that you need to debug remotely. Part of them were learned from my own battle scars and some were taught to me by my peers and employees.

From rice genomes to historical hurricane data, Google Cloud Public Datasets offer a world of exploration and insight. The more than 20 PB across 200+ datasets in our Public Dataset Program helps you explore big data and data analytics without a lot of cost, setup, or overhead. You can explore up to 1 TB per month at no cost, and you don’t even need a billing account to start using BigQuery sandbox.

At AppianGovernment, a record number of government customers and partners participated in 49 sessions with government executives, Appian technology experts, and partners. Leaders from the U.S. Food and Drug Administration, the U.S. Marine Corps, Health Canada, the U.S. Navy, the U.S. Department of Education, and other innovative organizations described how low-code automation is accelerating and simplifying the modernization of their mission systems.

With the speed of change in artificial intelligence (AI) and big data, podcasts are an excellent way to stay up-to-date on recent developments, new innovations, and gain exposure to experts’ personal opinions, regardless if they can be proven scientifically. Great examples of the thought-provoking topics that are perfect for a podcast’s longer-form, conversational format include the road to AGI, AI ethics and safety, and the technology’s overall impact on society.

According to The Economist, “the world’s most valuable resource is no longer oil, but data.” Despite the value of enterprise data, much has been written about the so-called “data science shortage”: the supposed lack of professionals with knowledge of how to use and manipulate big data. A 2018 study by LinkedIn estimated that there were more than 151,000 unfilled jobs in the U.S. requiring data science skills.

Businesses are increasingly demanding new software solutions that are quick, efficient, and user friendly. Low code is a way to automate several steps of the application process while still providing rapid delivery. In simplest terms, low code is a way of building processes and applications with very little coding. There are several aspects of this type of software development you need to understand when fully answering the question, what is low code?

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.