ML Training Pipeline
Ad-hoc training runs in Jupyter notebooks don't scale. We build automated training pipelines that handle data validation, distributed training across GPUs, hyperparameter tuning, experiment tracking, and model artifact management — reproducible and auditable from the first run.
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We implement, you ship. Async, documented, done in days.
Data Validation & Preprocessing
Every training run starts with data validation — schema checks, distribution analysis, and anomaly detection using Great Expectations or TensorFlow Data Validation. Bad data gets flagged before burning GPU hours. Preprocessing steps (normalization, encoding, feature engineering) run in containerized steps with cached outputs so unchanged data doesn't get reprocessed.
Distributed Training
For models too large for a single GPU, we configure distributed training with PyTorch DistributedDataParallel or Horovod. Multi-node training jobs launch via Kubernetes Training Operator with proper NCCL configuration and GPU topology awareness. Checkpointing saves progress every N steps so a node failure doesn't lose hours of training.
Hyperparameter Tuning
Katib, Optuna, or Ray Tune handles hyperparameter search — Bayesian optimization for expensive training jobs, grid search for fast ones. Tuning jobs run in parallel across available GPUs with early stopping on unpromising trials. Results feed into MLflow so you can compare runs across the entire search space.
Reproducibility & Artifacts
Every run logs the exact code commit, data version (DVC hash), hyperparameters, environment spec, and random seeds. Model artifacts get stored with their full lineage in the model registry. You can reproduce any historical training run exactly — critical for debugging production model issues months after deployment.
Why Anubiz Engineering
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Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.