Every model release is a manual, risky event
03 / Services / LLMOps / MLOps
MLOpsMove AI safely into production.
Hire a skilled MLOps developer to streamline machine learning operations, automate infrastructure, and deploy scalable models seamlessly.
01 / The problem
What usuallygets in the way.
Evaluate, deploy and monitor models and LLM applications with tracing, versioning, cost controls and rollback plans.
Quality and cost are only noticed after complaints
Nobody can reproduce last month's model
02 / What I build
Capabilitiesin this service.
01CI/CD for Machine Learning
Build automated pipelines for training, testing, packaging, deployment, and updates to speed up delivery and improve reliability across ML workflows.
02Containerization & Orchestration
Use Docker and Kubernetes to package models, manage dependencies, and orchestrate deployments across environments with better consistency and scalability.
03Cloud Infrastructure & Automation
Set up ML infrastructure on AWS, GCP, Azure, and modern cloud platforms using automation, infrastructure management, and deployment best practices.
04Monitoring & Model Performance
Track model health, data drift, inference behavior, and operational metrics to ensure ML systems remain accurate, reliable, and aligned with business goals.
05Lifecycle Management & Versioning
Manage models, datasets, experiments, and releases with structured versioning and reproducible workflows for better collaboration and long-term maintainability.
- CI/CD for models and prompts, with evaluation gates
- Tracing, quality and cost dashboards
- Versioned artefacts, runbooks and rollback
03 / How it works
A loop, not a launch
A typical architecture for this kind of work. Scroll to follow the flow, or hover a stage for detail.
Model
A versioned model or prompt with its data lineage.
Evaluation
Automated checks against a fixed test set.
Deployment
Gated releases with canary and rollback.
Monitoring
Latency, cost, drift and quality in one view.
Feedback
Real usage flows back into the next evaluation.
04 / In depth
The details.
MLOps Services
Build reliable, scalable, and production-ready MLOps systems for model deployment, monitoring, automation, and lifecycle management. I help businesses streamline machine learning operations from training to serving to continuous improvement.
My Expertise in MLOps
I specialize in building custom MLOps solutions for businesses across healthcare, legal tech, e-commerce, SaaS, and other data-driven industries. My work combines machine learning, cloud infrastructure, automation, containerization, monitoring, and deployment workflows to create systems that can move models from experimentation to reliable production use.
- 🤖 Model Deployment & Serving
- ⚙️ CI/CD for Machine Learning
- 🐳 Containerization & Orchestration
- ☁️ Cloud Infrastructure & Automation
- 📈 Monitoring & Model Performance
- 🔄 Lifecycle Management & Versioning
Model Deployment & Serving
Deploy machine learning models as scalable APIs and production services that support real-time inference, batch processing, and business application integration.
My Expertise in Data & AI Industry
From raw data to AI-powered decisions - I help businesses implement end-to-end machine learning workflows, analytics dashboards, and automation pipelines.
Hire MeRemote Data & AI Solutions
I’m a freelance Data Science and ML Engineer with 7+ years of experience transforming data into actionable insights. I specialize in building intelligent solutions, custom ML models, and scalable pipelines for startups and SMEs worldwide.
- United States (Remote Projects)
- Germany (AI Consultancy)
- EUROPE (Remote Projects)
- Remote - Available Worldwide
You Can find me here
As an independent Data & AI Engineer, I help companies unlock the full potential of their data. From predictive analytics to workflow automation, I offer strategic and technical support that drives real impact.
Book Free Consultation View Portfolio05 / Tech stack
Tools I usefor this work.
- MLflow
- DVC
- LangSmith
- Evidently
- Docker
- GitHub Actions
- AWS
06 / Related work
Proof,not promises.
Project screenshotApplication interface
JourneyMeshMulti-Agent AI Travel Planner LangGraph + MCP + LLMOps
Supervisor-routed multi-agent travel planning with guardrails, evaluation and human review
- Python
- LangGraph
- LangChain
- MCP
- +8
Project screenshotApplication interface
Bird Disease PredictionEnd-to-End MLOps Deep Learning
End-to-End MLOps Deep Learning Pipeline for Computer Vision CNN Training | Transfer Learning | Production Deployment
- Docker
- CI/CD
- REST
- Computer Vision
- +1
Project screenshotApplication interface
End-to-End MLOps ClassificationAWS + FastAPI + CI/CD
End-to-End MLOps Pipeline with CI/CD & Production Monitoring Data Ingestion → Training → Deployment → Monitoring
- FastAPI
- Docker
- CI/CD
- GitHub Actions
- +4
Project screenshotApplication interface
Bird Disease Classification (MLOps)
Built an end-to-end deep learning MLOps pipeline for bird disease classification using TensorFlow/Keras, PyTorch, OpenCV for image processing, FastAPI for model serving, and Docker for containerization. Implemented automated training pipelines, model versioning, and deployment workflows.
Project screenshotApplication interface
U.S. Visa Approval Prediction (MLOps)
Developed a complete MLOps pipeline for visa approval prediction using Python, FastAPI, Docker, AWS (EC2, ECR, S3), XGBoost, and CatBoost. Implemented automated model training, versioning, deployment, and monitoring with CI/CD integration for production-grade machine learning.
Uber Data Engineering ETL Project
- Python
- GCP
- BigQuery
- Mage.ai
Project overview
Uber Data Analytics Pipeline
Built a robust time-series forecasting solution with over 90% accuracy using Python and statistical modeling.It enabled a growing online retailer to better manage inventory, reduce overstock, and forecast seasonal demand shifts.
Project screenshotApplication interface
CareSage - RAG Medical Chatbot
Developed an intelligent medical chatbot using Retrieval-Augmented Generation (RAG) with Flask, LangChain, Pinecone vector database, OpenAI embeddings, and sentence-transformers. The system processes medical PDFs using PyPDF to provide accurate, context-aware responses for healthcare queries with citation support.
Project screenshotApplication interface
n8n Workflow Automation
Built 8 comprehensive automation workflows using n8n platform integrated with Retell AI, GoHighLevel, Twilio, OpenAI GPT-4, and PostgreSQL. Projects included AI Voice Agent for customer service, Multi-Channel Communication Hub, Lead Qualification System, CRM Sync Automation, Content Generation Pipeline, Customer Onboarding Workflow, Voice-Based Appointment Scheduler, and BI Reporting Dashboard.
07 / FAQ
Common questions.
What is the difference between MLOps and LLMOps?
MLOps covers the training, deployment and monitoring of machine learning models. LLMOps applies the same discipline to language model applications, adding prompt versioning, evaluation sets, cost tracking and guardrails.
How do you monitor AI systems in production?
I track quality, latency, cost and errors for each model or prompt version with tools such as LangSmith, MLflow and Evidently. Alerts fire on drift or failures, and every release runs the evaluation set first.
LLMOps / MLOps
Need MLOps?Let’s scope it together.
Tell me about the goal, the data and the constraints. I’ll reply with questions and a practical first step.