Systems | Development | Analytics | API | Testing

We’re on a mission to simplify application deployment for developers and businesses worldwide, whether they're AI-driven models, full stack applications, APIs, or databases. Our next-generation serverless platform significantly accelerates your deployments and improves efficiency, enabling you to build more with less spend. 2024 was a major year for us, packed with crucial serverless milestones.

Generative AI has rapidly transformed industries by enabling advanced automation, personalized experiences and groundbreaking innovations. However, implementing these powerful tools requires a production-first approach. This will maximize business value while mitigating risks. This guide outlines six best practices to ensure your generative AI initiatives are effective: valuable, scalable, compliant and future-proof.

As AI technologies continue to revolutionize industries, organizations must balance innovation with responsibility. Kong understands that embracing AI involves not just adopting new tools but also ensuring they're secure, compliant, and sustainable. During a recent visit to Singapore, Kong's Co-Founder and CTO Marco Palladino highlighted the critical need for a robust governance framework to manage the rapid pace of AI innovation responsibly.

For years, Cloudera’s platform has helped the world’s most innovative organizations turn data into action. As the AI landscape evolves from experiments into strategic, enterprise-wide initiatives, it’s clear that our naming should reflect that shift. That’s why we’re moving from Cloudera Machine Learning to Cloudera AI.

Software development is a dynamic field that needs reliable and effective testing practices. Artificial Intelligence (AI) is poised to revolutionize software testing services in this dynamic environment. As 2025 draws near, AI is set to transform quality assurance (QA) by automating processes, increasing accuracy, and boosting software quality in general. In this blog article, the leading AI developments in software testing for 2025 are examined.

Artificial intelligence (AI) has become an integral part of modern software, driving innovation across industries from healthcare to finance. However, as AI systems increasingly influence critical decisions, concerns around bias, fairness, and ethical implications have come to the forefront. Quality Assurance (QA) professionals are uniquely positioned to address these challenges, ensuring that AI systems are not only functional but also equitable and ethical.

Today, we are excited to announce the general availability of Claude 3.5 Sonnet as the first Anthropic foundation model available in Snowflake Cortex AI. Customers can now access the most intelligent model in the Claude model family from Anthropic using familiar SQL, Python and REST API interfaces, within the Snowflake security perimeter.

This article was originally published on The New Stack on Dec. 19, 2024. Imagine driving a car with a headset that only updates your view every five minutes instead of providing a continuous video stream. How long would it take before you crashed? While this type of batch processing clearly doesn’t work in the real world, it's how many systems operate today. Batch processing, born out of outdated technology constraints, forces applications to rely on static, delayed data.

Airports are an interconnected system where one unforeseen event can tip the scale into chaos. For a smaller airport in Canada, data has grown to be its North Star in an industry full of surprises. In order for data to bring true value to operations–and ultimately customer experiences–those data insights must be grounded in trust.