Since introducing Snowpark Container Services, we’ve seen overwhelming adoption across industries from customers and partners, including Landing.AI, Relational.AI, H20.AI, SailPoint, AIR MILES, Spark NZ, and Eutelsat OneWeb. These organizations and many more are using Snowpark Container Services capabilities to easily and securely deploy everything from custom front-ends and large-scale ML training and inference to open source and homegrown models, all securely within Snowflake.

Businesses are seeking ways to collect all the data from their digital sources and draw out patterns that can reinterpret human participation with automation. A vanguard in this attempt is the emergence of large language models (LLMs) powered by artificial intelligence (AI).

Low-code gives developers the flexibility to automate and streamline large portions of workflows, applications, and end-to-end processes while still allowing room for high-code development. This dual capability accelerates project timelines and enhances developer productivity. That’s why low-code development is often used as a supplement to high-code approaches; but it can also be the primary driver of development efforts.

The world of Data Integration continues to evolve at a rapid pace, and Qlik has evolved along with it at equal pace, starting with acquisitions of Podium, Attunity, and now Talend, Qlik’s Data Integration and Quality solutions are a formidable force to have in your toolkit when you are looking to modernise data engineering in your organisation.

By December 2027, all SAP customers are required to have migrated to its S/4HANA platform. To streamline this mandatory migration, SAP developed its SAP Activate methodology. The methodology itself is broken down into six key phases as shown below: When we examine these phases through a quality lens, focused on ensuring that the outcome is a highly performant and scalable system – we find that the need for Enterprise Performance Testing is critical at every stage.

Upon close inspection of the growing adoption of artificial intelligence across different industries, it becomes apparent that a lot it rests on the shoulders of API-powered systems. A survey by McKinsey found that 56% of respondents reported AI adoption in at least one function within their organizations, with significant increase in the use of AI APIs for tasks like natural language processing and computer vision.