ML Journey

A production guide to running Ollama on Kubernetes: a complete Deployment manifest with GPU resource limits, PersistentVolumeClaim for model storage, and ClusterIP Service, pulling models via kubectl exec, an init container pattern that pre-pulls models before the main container starts, exposing via NGINX Ingress with long proxy timeouts, a CPU-only configuration for clusters without GPUs, and deploying via the otwld/ollama-helm community Helm chart.

A practical guide to model calibration for ML engineers: measuring calibration with ECE and reliability diagrams, fixing overconfidence with temperature scaling using LBFGS on a held-out validation set, Platt scaling for binary classification, non-parametric alternatives with isotonic regression and histogram binning, calibrating LLMs on multiple-choice benchmarks via log-likelihood scoring, and recalibrating after every fine-tuning step as part of the model release checklist.

A practical guide to instrumenting Ollama calls with OpenTelemetry: setting up the OTel SDK with OTLP exporter and Jaeger, a traced_chat wrapper function that records model name, token counts, latency, and tokens per second as span attributes, tracing a full RAG pipeline with nested spans for embedding, retrieval, and generation, viewing traces in the Jaeger UI, and exporting to cloud backends including Honeycomb and Grafana Tempo.

A practical guide to LLM watermarking for ML engineers: how green/red list watermarking biases token sampling at decoding time, PyTorch implementation of the Kirchenbauer et al. scheme with detection via z-score, integrating the logit processor into a generation loop, the main attack surfaces (paraphrasing, substitution, translation), quality vs delta tradeoffs for code versus prose tasks, and the regulatory context under the EU AI Act and voluntary provider commitments.

A complete guide to using Celery and Redis with Ollama for async AI background processing: why slow LLM inference belongs in a task queue, defining Celery tasks for text summarisation and document classification with Pydantic structured output, starting workers with configurable concurrency, a FastAPI integration with immediate task ID response and polling endpoint, and batch processing with celery group for parallel document processing.

A practical guide to einops for ML engineers: rearrange for readable dimension splitting, merging, and transposing with named axes, reduce for explicit pooling over named dimensions, repeat as a drop-in for unsqueeze/expand chains, einsum with readable named contractions, the Rearrange nn.Module layer for use in Sequential and torch.compile, and a complete ViT patch embedding implementation using einops throughout.

A complete guide to local AI in Swift: a URLSession-based OllamaClient with non-streaming and streaming chat using AsyncStream, SwiftUI integration with async token display, configuring Ollama for LAN access from iOS devices on the same network, and Apple Foundation Models for true on-device inference on iOS 18+ and macOS 15+ using the Neural Engine with zero network access required.

A practical guide to PyTorch Automatic Mixed Precision for ML engineers: the numerical difference between fp16 and bf16 and when to use each, complete AMP training loop with autocast and GradScaler, how GradScaler adaptive scaling works and how to tune it, which ops autocast converts vs keeps in float32, AMP with HuggingFace Trainer, mixed precision inference with permanently converted model weights, and a hook-based debugger for finding which operation first produces NaN under fp16.

A complete guide to Ollama integration in C# and .NET: the OllamaSharp library for chat and model listing, Microsoft.Extensions.AI with OllamaChatClient registered as IChatClient for dependency injection, streaming with IAsyncEnumerable, Semantic Kernel with AddOllamaChatCompletion and prompt templates, an ASP.NET Core streaming SSE endpoint, and generating embeddings with cosine similarity in C#.

A practical guide to Online Hard Example Mining (OHEM) for ML engineers: how the forward-select-backward mechanism concentrates gradient signal on informative samples, PyTorch implementation with per-sample cross-entropy and topk selection, segmentation-specific OHEM with threshold-based pixel selection, OHEM vs focal loss and when to combine them, OHEM for metric learning and embedding training, and tuning the keep_ratio hyperparameter without destabilising training.