
Deep Learning & MLOps: CNN, RNN, Transfer Learning
Starting at
$
35
/hrAbout this service
Summary
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.
Skills and tools
Industries