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Fiverr Pro
English
$
USD
I will build mlops pipelines using databricks pyspark and mlflow
Level 1
Has met certain performance criteria and shows strong potential in the marketplace.
About this Gig
Are you looking for a reliable engineer to build scalable Data Engineering, MLOps, or DevOps solutions? You're in the right place.
I will design and implement end-to-end data and machine learning pipelines using Databricks, PySpark, MLflow, Docker, Kubernetes, CI/CD, and AWS/Azure/GCP/Digital Ocean. Whether you need ETL pipelines, ML workflow automation, or production deployment, I can help build secure, scalable, and maintainable solutions.
My Services Include:
- Databricks & PySpark ETL Pipelines
- MLflow Experiment Tracking & Model Registry
- Docker & Kubernetes Deployment
- CI/CD Pipelines (GitHub Actions, GitLab CI, Jenkins)
- AWS & Azure Cloud Deployment
- Feature Engineering & Data Processing
- Machine Learning Model Deployment
- Infrastructure Automation with Terraform
- Monitoring & Logging Integration
Why Choose Me?
- Clean, production-ready code
- Scalable and well-documented solutions
- Best practices for security and performance
- Fast communication and on-time delivery
- Post-delivery support
Please contact me before placing an order so we can discuss your requirements and recommend the best package for your project.
My Portfolio
Other DevOps Engineering Services I Offer
FAQ
What information do you need before starting the project?
Please share your project requirements, preferred cloud platform (AWS or Azure), source data, expected output, existing infrastructure (if any), and any technical documentation or architecture diagrams. This helps me recommend the best solution.
Do you work with Databricks and PySpark?
Yes. I can build and optimize ETL pipelines, perform data transformations using PySpark, and develop scalable workflows in Databricks for data engineering and machine learning projects.
Can you build complete MLOps pipelines?
Yes. I can help implement end-to-end MLOps workflows including MLflow experiment tracking, model versioning, Docker containerization, CI/CD pipelines, deployment, and monitoring on AWS or Azure.
Which cloud platforms do you support?
I primarily work with AWS and Azure. I can deploy applications and machine learning pipelines using services such as EC2, EKS, ECS, S3, Azure Virtual Machines, AKS, Azure Storage, and related cloud services.
Will I receive the source code?
Yes. All packages include clean, well-documented source code along with deployment instructions, unless otherwise agreed before the project starts.
Can you deploy existing machine learning models?
Yes. If you already have a trained model, I can containerize it with Docker, deploy it using FastAPI or other suitable frameworks, integrate MLflow if required, and automate deployment with CI/CD.
Do you provide support after delivery?
Yes. I provide post-delivery support to ensure the solution works as expected and to assist with minor issues related to the delivered project.
Can you work with healthcare, finance, or custom datasets?
Yes. I can work with structured datasets from various domains including healthcare, finance, retail, IoT, and other business applications while following your project requirements.
