In 24.2, we introduced our enterprise copilot. Enterprise copilot allows you to upload business documents and collect them in knowledge sets. From there, you can ask questions about information in these documents and receive answers quickly. For instance, an organization with a heavy regulatory burden could upload legislative and operational documents. Then, these employees could get insights from Appian AI Copilot to ensure they adhere to compliance requirements.

The Testing Pyramid is a framework in software development that helps organize and manage different types of tests. Its purpose is to ensure efficient and effective testing by structuring tests into a hierarchical model. At its core, the Testing Pyramid emphasizes the importance of having more low-level tests that are quick to execute and fewer high-level tests that are more complex and time-consuming.

Imagine launching your software with confidence, knowing that every button click, data transfer, and user interaction works perfectly—no surprises, no last-minute bugs. This level of assurance doesn't happen by chance; it’s the result of accurate End-to-End (E2E) Testing. But what exactly does end-to-end testing mean? How does it ensure that your application not only works but grows under real-world conditions?

Broadly defined, the supply chain management process (SCM) refers to the coordination of all activities amongst participants in the supply chain, such as sourcing and procurement of raw materials, manufacturing, distribution center coordination, and sales.

As compute gets increasingly powerful, the fact of the matter is: most AI workloads do not require the entire capacity of a single GPU. Computing power required across the model development lifecycle looks like a normal bell curve – with some compute required for data processing and ingestion, maximum firepower for model training and fine-tuning, and stepped-down requirements for ongoing inference.

Today, we are excited to announce the public preview of Snowflake Cortex Analyst. Cortex Analyst, built using Meta’s Llama and Mistral models, is a fully managed service that provides a conversational interface to interact with structured data in Snowflake. It streamlines the development of intuitive, self-serve analytics applications for business users, while providing industry-leading accuracy.

In today’s fast-paced business environment, having control over your data can be the difference between success and stagnation. Leaning on Master Data Management (MDM), the creation of a single, reliable source of master data, ensures the uniformity, accuracy, stewardship, and accountability of shared data assets.

See a solution that bridges the gap between gRPC services and RESTful HTTP APIs, and is enhanced with Apigee’s enterprise API management platform.

Data is the driving force of the world’s modern economies, but data teams are struggling to meet demand to support generative AI (GenAI), including rapid data volume growth and the increasing complexity of data pipelines. More than 88% of software engineers, data scientists, and SQL analysts surveyed say they are turning to AI for more effective bug-fixing and troubleshooting. And 84% of engineers who use AI said it frees up their time to focus on high-value activities.

Today’s enterprise networks are complex. Potential attackers have a wide variety of access points, particularly in cloud-based or multi-cloud environments. Modern threat hunters have the challenge of wading through vast amounts of data in an effort to separate the signal from the noise. That’s where a security data lake can come into play.