If you think workflow orchestration sounds like tech jargon, check out this simple explanation and consider practical advice for how to apply it to your business processes. Let’s start with the basics: what is workflow orchestration? Put simply, workflow orchestration is the end-to-end management of people, digital workers, systems, and data in a process.

In this post, we demonstrate how a robust and scalable generative artificial intelligence (GenAI) chatbot is built using Amazon Bedrock and Confluent Cloud. We walk through the architecture and implementation of this generative AI chatbot, and see how it uses Confluent's real-time event streaming capabilities along with Amazon's infrastructure to continually stay up to date with the latest advances from the AI landscape.

When Snowflake acquired the TruEra AI Observability platform, we committed to keeping TruLens open source. We’re not only keeping that promise; we’re emphasizing it. Our goal remains to support LLM app developers in creating trustworthy generative AI applications. In the weeks since the acquisition, we have already added ecosystem-friendly enhancements including: We plan to continue making enhancements and improvements that benefit the community at large, whether on Snowflake or not.

In the fast-evolving landscape of data science and machine learning, efficiency is not just desirable—it’s essential. Imagine a world where every data practitioner, from seasoned data scientists to budding developers, has an intelligent assistant at their fingertips. This assistant doesn’t just automate mundane tasks but understands the intricacies of your workflows, anticipates your needs, and dramatically enhances your productivity at every turn.

How to use BigQuery vector search to analyze logs and asset metadata stored in BigQuery tables.

In today's fast-paced software development environment, the integration of AI into DevOps revolutionizes the way teams approach testing. AI, particularly GenAI , proves to be a game-changer, offering unprecedented efficiency and accuracy in software testing processes. By automating repetitive tasks and providing actionable insights, AI is not only enhancing the quality of software but also accelerating deployment cycles.

Integrating artificial intelligence (AI) into business operations is no longer optional—it’s necessary. Yet, too often, businesses fail to reap the full rewards. AI can’t produce the results that impress stakeholders and drive tangible results unless you take a strategic approach to its deployment.

In the age of AI, enterprises are increasingly looking to extract value from their data at scale but often find it difficult to establish a scalable data engineering foundation that can process the large amounts of data required to build or improve models. Designed for processing large data sets, Spark has been a popular solution, yet it is one that can be challenging to manage, especially for users who are new to big data processing or distributed systems.