ML Pipelines

Understands ML pipeline stages for computer vision: data loading, augmentation, training, and evaluation. Runs existing pipelines and tracks experiment metrics using MLflow or Weights & Biases.

Creates basic ML pipelines via scikit-learn Pipeline: preprocessing, feature engineering, model training. Understands pipeline importance for reproducibility and data leakage prevention. Saves pipelines as single artifacts for deployment.

Knows ML pipeline basics: data loading, preprocessing, training, evaluation. Builds simple pipelines for LLM fine-tuning data preparation using Hugging Face Datasets.

Builds end-to-end ML pipelines for vision models: dataset versioning, distributed training, hyperparameter tuning, and model registry integration. Implements data quality checks for image datasets.

Designs production ML pipelines using Airflow, Prefect, or Dagster. Automates the full ML cycle: data ingestion, validation, feature engineering, training, evaluation, deployment. Configures scheduling and retry logic.

Independently builds production ML pipelines for LLM: data ingestion, cleaning, tokenization, training, evaluation. Uses Airflow or Prefect for orchestration, ensures idempotency.

Designs scalable ML pipeline architectures for large-scale vision systems: multi-GPU training orchestration, model distillation workflows, and production inference pipelines with latency optimization.

Designs scalable ML pipelines for enterprise: Kubeflow, Vertex AI Pipelines, SageMaker Pipelines. Implements continuous training, automated model promotion. Optimizes pipeline performance through caching, parallel execution, and incremental processing.

Designs complex ML pipelines for LLM platforms: multi-stage data processing, continuous training, automated retraining triggers. Optimizes pipeline throughput and reliability.

Defines ML Pipelines strategy at the team/product level. Establishes standards and best practices. Conducts reviews.

Defines ML pipeline infrastructure strategy for the data science team. Establishes pipeline development, testing, and monitoring standards. Coordinates unification of pipeline approaches across projects and teams.

Defines ML pipeline standards for the LLM team. Establishes guidelines for pipeline architecture, monitoring, error handling. Coordinates pipeline infrastructure for training and inference.

Defines ML Pipelines strategy at the organizational level. Establishes enterprise approaches. Mentors leads and architects.

Shapes ML orchestration platform strategy at organizational level. Defines enterprise requirements: scalability, governance, cost management. Designs unified ML pipeline platform for all data science teams.

Shapes enterprise ML pipeline platform. Defines approaches to unified pipeline frameworks, cross-team pipeline components, and SLA and cost management for data/training/serving pipelines.