We design and train ML models for forecasting, classification, scoring, and clustering tasks using Python libraries. Data is engineered into clean, domain-specific features before training. Models undergo hyperparameter tuning, cross-validation, and bias testing. Finally, they’re packaged as versioned API endpoints or workflows ready for production deployment and observability.

We build deep neural networks including CNNs, RNNs, Transformers, and GANs using TensorFlow and PyTorch. Workloads include image detection, conversational AI, speech recognition, and personalization models. Teams get flexible architectures and TensorBoard insights. Models integrate with GPUs, AI accelerators, or cloud-hosted training jobs.

Using Docker, KServe, FastAPI, and MLflow, we deploy scalable inference endpoints. Retraining pipelines and performance drift monitors keep accuracy high in real-world data. Production models can be auto-rolled back, versioned, or A/B tested safely through CI/CD and S3 or feature store integration.

We design reusable preprocessing pipelines with Pandas, NumPy, Scikit-learn, and custom transformers. Metadata, scaling, encodings, and embeddings remain consistent across training and inference. Versioned pipelines ensure auditability and alignment throughout the ML lifecycle across teams and environments.

We apply SHAP, LIME, and fairness metrics to explain predictions and detect bias. Detailed evaluations cover precision, recall, leakage, uplift, drift, and cost metrics. These insights support audits, regulatory compliance, and business confidence, especially in finance, healthcare, and regulated industries.

Using managed GPU clusters, Ray, and distributed training libraries, we scale model training for large datasets efficiently. This ensures shorter experimentation cycles and reproducible results across team environments.