You will get your ML model served with FastAPI and tracked in MLflow
Rising Talent
Rising Talent
Project details
A machine learning model is only useful when the workflow around it is reliable.
If your team has notebooks, scripts, experiments, or an early model but needs a cleaner path toward production, I can help set up a practical MLOps starter layer.
I can support MLflow experiment tracking, PyTorch workflow structure, FastAPI model serving, input and output schema design, basic validation, monitoring structure, and handover documentation.
This is useful for AI startups, computer vision projects, document AI workflows, analytics teams, and small ML teams moving from notebooks toward production.
The goal is to make your ML workflow easier to track, test, serve, monitor, and improve.
If your team has notebooks, scripts, experiments, or an early model but needs a cleaner path toward production, I can help set up a practical MLOps starter layer.
I can support MLflow experiment tracking, PyTorch workflow structure, FastAPI model serving, input and output schema design, basic validation, monitoring structure, and handover documentation.
This is useful for AI startups, computer vision projects, document AI workflows, analytics teams, and small ML teams moving from notebooks toward production.
The goal is to make your ML workflow easier to track, test, serve, monitor, and improve.
Machine Learning Tools
Amazon SageMaker, Apache Spark, Apache Spark MLlib, Azure Machine Learning, Databricks Platform, Databricks MLflow, Kubeflow, MLflow, NumPy, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Scrapy, SQL, Tesseract OCR, XGBoostWhat's included
| Service Tiers |
Starter
$299
|
Standard
$799
|
Advanced
$1,799
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 18 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Frequently asked questions
About Shitanshu
Expert Data Platform Engineer | Airflow, Spark, AWS, ETL, MLOps
New Delhi, India - 12:19 pm local time
🏅 DeepLearning.AI Data Engineering Professional Certificate, AWS
🏅 IBM Data Engineering Professional Certificate
🏅 PyTorch for Deep Learning Professional Certificate
I help businesses turn messy data into reliable, tested, and production-ready data systems.
If your data is scattered across APIs, databases, files, PDFs, cloud storage, dashboards, or ML workflows, I can help you build pipelines and datasets your team can trust.
My focus is simple:
🎯 Reliable data pipelines
🎯 Clean and validated datasets
🎯 Airflow and Spark workflows
🎯 Cloud data jobs on AWS and Azure
🎯 Data quality checks and documentation
🎯 Monitoring, reliability, and handover
🎯 ML-ready datasets and MLOps support when needed
I work with Python, SQL, Apache Airflow, Apache Spark, Kafka, dbt, Docker, Terraform, Snowflake, AWS, Azure, Databricks, PostgreSQL, MySQL, MongoDB, MLflow, and PyTorch.
How I can help:
🚀 Build ETL and ELT data pipelines
🚀 Create Airflow DAGs and workflow orchestration
🚀 Build Spark, AWS Glue, and cloud data jobs
🚀 Clean, transform, and validate messy data
🚀 Create analytics-ready datasets
🚀 Design data lake and lakehouse workflows
🚀 Add data quality checks and testing
🚀 Improve failed or fragile pipelines
🚀 Prepare ML-ready datasets
🚀 Support MLflow, FastAPI, and model monitoring workflows
What makes my work different:
I do not just write scripts. I build data systems that are clean, repeatable, documented, and easier for your team to operate.
My goal is to help you reduce manual fixes, avoid bad-data surprises, improve trust in reports, and make better business or ML decisions from reliable data.
Good first projects:
✅ Audit and improve an existing data pipeline
✅ Clean messy PDFs, APIs, CSVs, or web data into a trusted dataset
✅ Set up MLflow, FastAPI, and monitoring for an ML workflow
✅ Build a small production-ready data pipeline MVP
If you need a reliable Data Platform Engineer who can turn messy data into clean, tested, and usable systems, I would be happy to help!
Steps for completing your project
After purchasing the project, send requirements so Shitanshu can start the project.
Delivery time starts when Shitanshu receives requirements from you.
Shitanshu works on your project following the steps below.
Revisions may occur after the delivery date.
Review ML workflow
I review your notebook, scripts, model files, dataset schema, metrics, and deployment expectations.
Design MLOps setup
I define the tracking, serving, schema, monitoring, and handover structure based on your package.