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I will deploy your ml model as a dockerized fastapi service
Hardship made me grow real fast
Data Science and Machine Learning Engineer with 6 years of experience designing and deploying end-to-end business solutions that optimize workflow. I specialize in transforming complex datasets into r...
About this Gig
Are you struggling to move machine learning models from Jupyter notebooks into production-ready systems? As an experienced MLOps Engineer, I bridge the gap between data science and production engineering.
I containerize models, track experiments, set up automated testing, or deploy robust cloud pipelines tailored to your needs.
Core MLOps Stack:
- Experiment Tracking & Versioning: MLflow
- Containerization: Docker
- API Development: FastAPI (with Swagger documentation)
- Testing & Quality Assurance: Pytest
- CI/CD Automation: GitHub Actions
- Cloud Deployment: Microsoft Azure
- Data Pipelines: BeautifulSoup & Selenium (for data acquisition)
Why Choose Me?
End-to-End Expertise: Proven track record deploying complex systems, from real-time analytics pipelines to computer vision and NLP apps.
Production-Focused: Clean, modular code with automated testing minimizes bugs and lowers false positives.
Clear Communication: Regular updates and thorough documentation so your team can easily maintain the system post-delivery.
Let's turn your machine learning model into a robust production asset today! Message me before ordering to discuss your custom project stack.
Domain:
Machine Learning
•
Time Series Analysis
Programming language:
Python
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
•
MLflow
•
Azure ML Studio
Technology:
Python
•
scikit-learn
•
MLflow
•
Pandas
•
Excel
My Portfolio
FAQ
What if I already have a model but don't know how to deploy it?
I will take your trained model artifacts (.pkl, .h5, etc.) and transform them into a production-ready asset. This includes containerization (Docker), building a FastAPI API, and setting up CI/CD for reliable deployment to Azure.
Do you handle the data acquisition process if the data isn't ready?
Yes. I have experience implementing data ingestion pipelines using web scraping (BeautifulSoup, Selenium) or handling pre-existing datasets. We can define the required data schema during the scoping phase.
What does "end-to-end MLOps" specifically include?
It is the full life cycle: from tracking experiments and versioning models with MLflow, to automated testing (Pytest), building the API, and deploying the stable infrastructure via CI/CD and Azure.
Is this setup suitable for real-time predictions or just batch processing?
My stack (FastAPI & Docker) is built for high performance and low latency, making it ideal for real-time inference APIs. We can tailor the architecture to meet your specific prediction speed requirements.
Can you integrate the system with my existing cloud infrastructure (e.g., Azure)?
Yes. I specialize in deploying robust ML systems directly onto Microsoft Azure. We will ensure seamless integration, utilizing GitHub Actions for automated pipeline orchestration and deployment.
