Production

The Challenge of Scale Transformer architectures have revolutionized natural language processing and are increasingly applied to vision, audio, and multimodal tasks. But moving from research notebooks to production systems presents unique challenges. In this post, we’ll explore practical strategies for scaling transformer models to handle real-world workloads. Architecture Considerations Model Size vs. Inference Speed The eternal trade-off: larger models deliver better performance but slower inference. Finding the sweet spot requires: ...

Why MLOps Matters Machine learning in production is fundamentally different from research. Models need to be versioned, monitored, retrained, and maintained—often by teams beyond the original developers. MLOps brings engineering discipline to ML systems, making them reliable, reproducible, and maintainable. Core Principles 1. Everything is Code Treat all ML artifacts as code: Model code: Training scripts, architectures, preprocessing Infrastructure code: Terraform, Kubernetes manifests Pipeline code: Orchestration, scheduling, monitoring Configuration: Hyperparameters, feature definitions # version_config.yaml model_version: "v2.3.1" training_config: learning_rate: 0.001 batch_size: 32 epochs: 100 data_version: "2024-02-01" features: - user_engagement_7d - session_duration - click_through_rate 2. Reproducibility is Non-Negotiable Every experiment must be reproducible: ...