Systems | Development | Analytics | API | Testing

The Snowflake IPO - What does it mean?

Today, Snowflake began life as a publicly traded company on the New York Stock Exchange. What does it mean? It depends on who you are. For employees, this is of course a huge milestone, especially for the longest serving employees who hired on at the company in 2013 when the company first started staffing beyond its core founding team.

How Neural Guard Built its X-Ray & CT Scanning AI Production Pipeline - Customer Story

Neural Guard produces automated threat detection solutions powered by AI for the security screening market. With the expansion of global trends like urbanization, aviation, mass transportation, and global trade, the associated security and commercial challenges have become ever more crucial.

EP. 3 Developing a Long-Term Data Strategy Minna Karha, Head of Data at Finnair

Minna Karha, Head of Data at Finnair, joins us for this episode of Rise of the Data Cloud. She came to Finnair two and a half years ago as the first person in that role. The company considers data expertise to be essential for its ongoing digital transformation. She built a team and developed a long-term data strategy through 2025. A key element of the strategy is moving much of Finnair’s data to the cloud so it can be more easily integrated. Right now, Finnair is beginning to share data via the cloud with business partners, including other carriers.

Correlation Analysis: A Natural Next Step for Anomaly Detection

Over the last decade, data collection has become a commodity. Consequently, there has been a tremendous deluge of data in every area of industry. This trend is captured by recent research, which points to growing volume of raw data and growth of market segments fueled by that data growth.

Building ML Pipelines Over Federated Data & Compute Environments

A Forbes survey shows that data scientists spend 19% of their time collecting data sets and 60% of their time cleaning and organizing data. All told, data scientists spend around 80% of their time on preparing and managing data for analysis. One of the greatest obstacles that make it so difficult to bring data science initiatives to life is the lack of robust data management tools.