Systems | Development | Analytics | API | Testing

Announcing Strategic Distribution Partnerships to Scale AI

As we head into 2025, Qlik is taking a significant step forward in the evolution of our go-to-market approach by placing an even greater emphasis on our partnerships. This move is aimed at capturing the growing market opportunity in data integration, data quality, analytics and AI.

Event-Driven AI: Building a Research Assistant with Kafka and Flink

This post was originally published on Medium on Nov. 20, 2024. The rise of agentic AI has fueled excitement around agents that autonomously perform tasks, make recommendations, and execute complex workflows blending AI with traditional computing. But creating such agents in real-world, product-driven environments presents challenges that go beyond the AI itself.

Deploy AI Infrastructure in 2025: Serverless GPUs, Autoscaling, Scale to Zero, and More!

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.

EP 6: To Prevent the Artificial Charlatan, Data Management Has to be Fun

The AI explosion has led to non-stop hype cycles as the technology continues to develop. But AI is only as good as the data behind it. The threat of lousy data is bad AI. Andrew Brust, Founder and CEO of Blue Badge Insights, joins The AI Forecast to discuss the AI hype–and how to prevent what he calls an “artificial Charlatan” of bad AI. He emphasizes the dependent relationship between data and AI and the former’s role in the success of the latter. Specifically, he addresses the data governance conundrum, and why in order for data technology to be successful, it has to be fun.

Vector Databases: Why QA professionals Needs to Care About them in the Age of AI? | Toni Ramchandani

In the rapidly evolving age of AI, vector databases have become the backbone of modern systems, revolutionizing the way high-dimensional data is managed and queried. In this insightful session, Toni Ramchandani explores why QA professionals must adapt their skills and approaches to meet the unique demands of vector databases. Traditional testing methods fall short in addressing challenges like similarity search, vector indexing, and performance optimization.

6 Best Practices for Implementing Generative AI

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.

Shaping the Future of Digital Transformation in Singapore

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.

Introducing One Million Minds + One Platform

AI and technology are reshaping the world we live and work in every day. It’s imperative that we equip people with the tools and skills they need to succeed in this rapidly changing landscape. In this video, Snowflake CEO Sridhar Ramaswamy announces a new company initiative to tackle the challenge. Called "One Million Minds + One Platform," its goal is to train and certify one million students and professionals in data and AI by 2029 and 100,000 users on the Snowflake AI Data Cloud by 2027.

The Role of QA in Ensuring AI Ethics and Fairness

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.