Need MLOps that just works? I build MLOps pipelines, model deployment infrastructure, and monitoring systems. MLflow, Kubernetes, FastAPI, Django, and AWS SageMaker. Your ML infrastructure, automated.
Need MLOps that just works? I build MLOps pipelines, model deployment infrastructure, and monitoring systems. MLflow, Kubernetes, FastAPI, Django, and AWS SageMaker. Your ML infrastructure, automated.
End-to-end MLOps
From training to production
Cloud-native
AWS, GCP, Azure deployments
Complete MLOps solutions with automated pipelines, model monitoring, and scalable infrastructure for production AI systems.
Clear outcomes, the right guardrails, and async updates while we work.
Availability: 1–2 concurrent builds max.
Timeframe: Typical engagement 6–10 weeks.
Collaboration: Weekly demos, shared roadmap, <24h async response.
How we break down the work so you stay unblocked at every phase.
Built ML training pipelines with Airflow, MLflow, and Kubeflow. Includes data validation, hyperparameter tuning, distributed training, and model versioning.
Deployed models with TorchServe, TensorFlow Serving, and BentoML on Kubernetes. Features auto-scaling, A/B testing, canary deployments, and GPU optimization.
Implemented comprehensive ML monitoring with Prometheus, Grafana, and custom metrics. Tracks model drift, performance degradation, and data quality issues.
Built feature store with Feast and custom tooling. Includes offline/online feature serving, feature versioning, and lineage tracking for reproducibility.
Designed and deployed ML infrastructure on AWS SageMaker, GCP Vertex AI, and Azure ML. Includes cost optimization, security, and compliance.
Founders and operators keeping us honest.
Built our LLM integration—handles 10K requests/day.
Shubham integrated LLMs into our product with RAG architecture. The system handles 10K requests per day seamlessly. He set up proper error handling, rate limiting, and monitoring. Production-ready from day one.
MLOps pipeline reduced our model deployment time by 80%.
We were manually deploying ML models. Shubham built an MLOps pipeline with automated training, versioning, and deployment. Model deployment time went from days to hours. The monitoring and alerting he added caught issues early.
Fine-tuned our LLM model—accuracy improved 25%.
Shubham fine-tuned our LLM model for our specific use case. Accuracy improved by 25%, and inference time stayed the same. He also built the API and monitoring infrastructure. Great work.
I specialize in machine learning, deep learning, LLM integration, computer vision, and MLOps. I work with TensorFlow, PyTorch, FastAPI, Django, and production deployment pipelines.
I offer AI model development, LLM integration, computer vision solutions, MLOps pipeline setup, and AI consulting. Available for contract and hourly work. Contact me to discuss your AI project needs.
I start by understanding your AI requirements and data, then design and develop models tailored to your needs. I focus on production-ready solutions with proper MLOps pipelines, monitoring, and documentation.
I have experience deploying AI models to production using FastAPI, Django, Kubernetes, AWS SageMaker, and MLflow. I ensure models are scalable, monitored, and maintainable in production environments.