Insights
Product, design, AI, and engineering perspectives from our team.
MLflow vs Weights & Biases: Experiment Tracking and Model Registry
MLflow and Weights & Biases are the two platforms most growing ML teams evaluate for experiment tracking and model registry. This guide compares them honestly across deployment model, data residency, collaboration, and production reproducibility — so you can make the right call for your team and regulatory context.
PyTorch vs TensorFlow in 2025: Choosing a Production ML Framework
PyTorch and TensorFlow are both production-capable ML frameworks in 2025, but they suit different teams, workloads, and deployment environments. This guide helps technical leaders make a defensible framework choice based on ecosystem fit, serving requirements, and team context — not benchmarks or hype.
LLM Evaluation in Production: A Three-Layer Approach
Shipping an LLM feature is the easy part. Knowing whether it still works correctly six weeks later — after a prompt change, a model version bump, or a shift in user behaviour — is where most teams struggle. This post covers a three-layer evaluation approach that gives engineering teams real confidence in production LLM systems.
AWS SageMaker vs Self-Hosted GPU Serving: Cost and Control
AWS SageMaker and self-hosted GPU serving on A100 or H100 hardware each make sense under different conditions — and the wrong choice becomes expensive quickly. This article breaks down the cost structure, operational trade-offs, and decision framework for Australian engineering teams moving ML models to production.
AI Readiness Assessment: A Practical Guide for Australian Businesses
An AI readiness assessment is the structured process of evaluating whether your organisation has the data, infrastructure, talent, and cultural foundations needed to adopt AI successfully. This guide covers the five readiness dimensions, maturity models, and a practical assessment framework for Australian businesses considering AI adoption.
AI Readiness Assessment: A Complete Guide for Australian Businesses
An AI readiness assessment is the structured diagnostic that tells Australian businesses what they actually need before investing in AI development. This guide covers the six readiness dimensions, Australian regulatory obligations, cost models, and how to build a credible implementation roadmap from the assessment results.
Digital Engineering Consultancies in Australia: A Market Guide
digital transformation achieved
Claude vs GPT-4 vs Gemini: Choosing the Right Enterprise LLM
Claude, GPT-4, and Gemini are all genuinely capable enterprise LLMs — but they have different strengths, deployment models, and compliance profiles. This guide helps Australian technical leaders compare the three across reasoning, coding, multimodal capability, cost, latency, and data residency, and choose the right model for each task.
Kubernetes for AI Workloads: When It's Worth the Complexity
Kubernetes is worth the complexity for AI workloads when you are serving multiple models in production, managing GPU scheduling across workloads, and have platform engineering capacity to operate it. For teams at earlier stages, managed services are the more pragmatic starting point. This guide helps technical leaders make the call clearly.