CNA worked with Google Cloud and several third-party data vendors to develop a solution to address challenges with underwriting flood risk assessment.

With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high. But in highly regulated industries where these technologies may be prohibited, the focus is less on off the shelf generative AI, and more on the relationship between their data and how AI can transform their business.

In today’s high-velocity digital arena, businesses are thrust into the whirlwind of global events, rapid technological advancements, and the incessant push for innovation. Yet, amidst the tempest of mergers, digital acceleration, and shifting tech paradigms, charting a confident path towards cloud migration can be daunting.

Snowpark is the set of libraries and runtimes that enables data engineers, data scientists and developers to build data engineering pipelines, ML workflows, and data applications in Python, Java, and Scala. Functions or procedures written by users in these languages are executed inside of Snowpark’s secure sandbox environment, which runs on the warehouse.

At Cloudera, we’re known for making innovative technological solutions that drive change and impact the world. Our mission is to make data and analytics easy and accessible to everyone. And that doesn’t end with our customer base. We also aim to provide equitable access to career opportunities within data and analytics to the workforce of tomorrow.

Companies want to train and use large language models (LLMs) with their own proprietary data. Open source generative models such as Meta’s Llama 2 are pivotal in making that possible. The next hurdle is finding a platform to harness the power of LLMs. Snowflake lets you apply near-magical generative AI transformations to your data all in Python, with the protection of its out-of-the-box governance and security features.

Embedded analytics solutions have become increasingly popular in recent years, as more organizations across multiple sectors recognize the value of integrating advanced business intelligence (BI) and analytical capabilities into their existing software applications. Such solutions allow for deeper insights from data to make more informed decisions - this is well established.

Enterprises are leveraging cloud investments to supercharge marketing efforts by building a Composable CDP with BigQuery and Hightouch.

In this modern, turbulent market, predictive analytics has become a key feature for analytics software customers. Predictive analytics refers to the use of historical data, machine learning, and artificial intelligence to predict what will happen in the future. This ability to analyze and predict future scenarios sets certain applications apart from the pack, offering application teams significant advantage in a competitive market.

BigQuery Studio lets analytics practitioners use SQL, Python, Spark or natural language directly within BigQuery, to streamline analytics workflows.