BigQuery ML is now compatible with open-source gen AI models
You can now run any open-source LLM from Vertex AI Model Garden or Hugging Face with BigQuery Machine Learning.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
More AI, More Problems?
AI was supposed to be the game-changer for developer productivity, but reality isn’t living up to the hype. GPT-4 took 50x the resources of GPT-3.5, yet the improvement? Barely noticeable. AI-generated code isn’t saving time—it’s creating more debugging, security headaches, and compliance risks. The real issue? It’s not the AI—it’s how we’re using it. AI isn’t freeing up developers for innovation—it’s adding more noise. So, what’s the fix? Catch the full conversation on the latest Test Case Scenario.
Rethinking AI's Role in Leadership, Governance, and Productivity
AI is reshaping development, but is it meeting expectations? In this episode of Test Case Scenario, Jason Baum and Marcus Merrell explore the evolving role of AI in software development, drawing insights from recent industry reports. They discuss whether AI tools are living up to their promise of reducing burnout and boosting productivity while examining the complexities of debugging, security risks, and governance gaps.
How Financial Services Institutions Should Think About Unstructured Data - and Why It Matters for a Sound Enterprise AI Strategy
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.
Building an AI-ready data strategy: Key takeaways from industry leaders
AI success demands more than data access; it requires governance, strategy and collaboration across the enterprise.
AI Won't Fix Testing-But It Might Break It
AI is being treated as a shortcut for quality. Is that a dangerous gamble? There are a few industry-wide experiments happening right now: Developers are being pushed to own quality, but without dedicated testers, gaps are forming. AI is being used as a crutch for testing, but can it actually replace critical thinking? The real risk? We won’t know how badly this approach fails until it’s too late.
Ecommerce LOVES AI
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?
AI Won't Replace Testers-It'll Challenge Them to Think Smarter
AI isn’t a shortcut to perfect testing. It won’t magically fix your processes or write flawless code. But if used right, it will push testers and developers to think more critically. Instead of asking if AI should be part of testing, the real question is how to make it a true collaborator. That means: Using AI to highlight gaps, not blindly trusting its output Treating it as a thought partner, not an automation machine.
Is manual testing becoming obsolete with advancements in automation and AI?
According to Cristiano Caetano, VP of Product Management at Katalon, human intuition is irreplaceable in this field. It is better to leverage AI for repetitive and labor-intensive tasks while using the freed up bandwidth to focus on strategy - things machines can't replicate. Stay tuned for upcoming episodes in our series!
Using Apache Flink for Model Inference: A Guide for Real-Time AI Applications
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.