What's included

• Project scoping & data assessment

  I review your use case, data sources, and success metrics to define the problem, choose suitable deep learning architectures (CNN, RNN, or hybrid), and outline an implementation plan.

• Data pipeline & preprocessing setup

  I build a reusable pipeline to ingest, clean, transform, and split your data (images, text, time series, etc.) so it’s ready for model training and future updates.

• Baseline deep learning model (CNN/RNN)

  I implement an initial CNN/RNN model suited to your task, train it on your data, and establish a performance baseline to compare future improvements.

• Transfer learning & fine-tuning

  I apply transfer learning from strong pre-trained models (e.g., vision or language backbones) and fine-tune them on your domain data to improve accuracy and reduce training time.

• Experiment tracking & model evaluation

  I set up experiment tracking (hyperparameters, versions, metrics) and deliver a clear evaluation report with key metrics, comparisons, and recommendations for the best model.

• MLOps pipeline for training & deployment

  I create an MLOps workflow (CI/CD, model versioning, automated training) that allows you to reliably train, package, and deploy models from development to production

• Production deployment (API or service)

  I deploy the selected model as a production-ready API, microservice, or batch job so your applications or team can consume predictions in a stable, scalable way.

• Monitoring, logging & retraining strategy

  I implement monitoring for model performance and data drift, set up logging/alerts, and define a retraining strategy so your deep learning system stays accurate over time.

• Documentation & handover session

  I provide concise technical documentation and a live walkthrough covering pipelines, models, MLOps workflows, and how your team can operate or extend the solution.