Deep Learning & MLOps: CNN, RNN, Transfer LearningPankaj Kumar Pramanik
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.
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.
Starting at$35 /hr
Tags
AWS
Azure DevOps
Docker
Python
AI Engineer
Service provided by
Pankaj Kumar Pramanik proBhandaria Sadar Union, Bangladesh
Deep Learning & MLOps: CNN, RNN, Transfer LearningPankaj Kumar Pramanik
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.
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.
$35 /hr