Systems | Development | Analytics | API | Testing

AI is only as good as the data it learns from. Without a strong data integrity foundation, AI models risk reinforcing biases, misinterpreting trends, and making flawed predictions — ultimately leading businesses down costly and damaging paths.

New AI and Machine Learning (ML) solutions have become one of the most powerful tools in today’s technology stacks. These support the ability to process and analyze vast amounts of data to identify patterns and to make forecasting more reliable. AI has been driving innovation in healthcare, finance, and retail. Now, it's beginning to revolutionize the field of software engineering. This article shares some of our innovations in applying AI to software engineering processes.

Sales reps dedicate just two hours each day to active selling, according to HubSpot research. At Snowflake, our sales team found they were wasting 10 to 15 minutes searching for the right content every time they needed to answer a single question, like “Can you explain how Snowflake handles data integration from various sources?” Valuable content was scattered across different platforms, forcing employees to hop between various tools to assemble the right information.

Visit booth for live demos and expert insights on building a scalable, secure data foundation for AI with Microsoft Fabric and OneLake.

You can now run any open-source LLM from Vertex AI Model Garden or Hugging Face with BigQuery Machine Learning.

Being able to leverage unstructured data is a critical part of an effective data strategy for 2025 and beyond. To keep up with the competition and AI-accelerated pace of innovation, businesses must be able to mine the treasure trove of value buried in the mountains of unstructured data that comprise approximately 80% of all enterprise data — from call center logs, customer reviews, emails and claims reports to news, filings and transcripts.

AI success demands more than data access; it requires governance, strategy and collaboration across the enterprise.

The e-commerce landscape is passionately competitive. Today is a prime example as consumers rush to secure the perfect Valentine’s Day gift before it's slim pickings on the chocolates and the sad looking roses! How do you meet the demand and keep those last minute bouquet hunters happy?

Open source LLMs continue to compete with proprietary models on performance benchmarks for natural language tasks like text generation, code completion, and reasoning. Despite having fewer resources than closed models, these open LLMs offer cutting-edge AI without the high costs and restrictions of proprietary models. However, running these open-source models in production and at scale remains a challenge.

As real-time data processing becomes a cornerstone of modern applications, the ability to integrate machine learning model inference with Apache Flink offers developers a powerful tool for on-demand predictions in areas like fraud detection, customer personalization, predictive maintenance, and customer support. Flink enables developers to connect real-time data streams to external machine learning models through remote inference, where models are hosted on dedicated model servers and accessed via APIs.