Data Modeling: Best Practices for Scalable Python Workflows
A customer churn model crashes in production when it encounters unexpected null values in a revenue field that never appeared in training data. The model performed beautifully in notebooks, but when deployed, the pipeline can’t handle the bad data. Features that worked locally fail in production, and team members can’t reproduce results. After days of […]
Small Models, Big Impact: Enterprise Use Cases for Lightweight LLMs
AI Catalyst is Anaconda’s enterprise AI development suite that gives you access to pre-validated, quantized open source models with built-in governance. Think of it as the enterprise-ready version of downloading open-source models from Hugging Face, but with all the security, compliance, and optimization work already done for you. Since data remains on your company’s infrastructure, […]
3 Ways to Scale AI Across Your Enterprise
The technical work is done. You’ve chosen an open-source AI model, benchmarked its performance, and determined its cost. You know exactly how it’ll be used, and you’ve defined what success looks like. But how do you scale it without causing chaos? Many enterprises face this binary trap. Either they implement rigorous one-off approval processes that […]
No, AI Isn’t Replacing Developers. Here’s What’s Actually Happening
You’ve probably seen the headlines by now. Vibe coding tools that promise “anyone can build apps now.” Tech leaders claiming they’ll need fewer developers thanks to AI. The conclusion they’re drawing is pretty clear: software development is a dying field. I don’t buy it. And more importantly, that’s not what I’m seeing in the field. […]
Model Curation: Breaking the Bottleneck in Enterprise AI Deployment
The pressure is on, and the assignment is clear: Build with AI, and do it faster than your competitors. But before you can build anything, you need to answer a fundamental question: Which AI models should power your applications? This is where most enterprises hit their first roadblock. Public repositories have more than 2 million […]
Scaling GenAI in Production: Best Practices and Pitfalls
As organizations move from experimentation to production-grade GenAI systems, traditional MLOps alone isn’t enough. Below, we share a direct excerpt from Generative AI in Action by Amit Bahree (Manning, 2024), covering key practices for LLMOps, monitoring, and deployment checklists. The following text is excerpted with permission. LLMOps and MLOps Machine learning operations (MLOps) apply DevOps […]