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Fiverr Pro
English
$
USD
I will deploy and productionize ml models using fastapi and mlops
Maki
About this gig
Jupyter Notebooks are where ML models go to die.
Dont let your investment vanish in a .ipynb file. You've built a powerful model, but now you're facing the "Production Wall": slow inference, rising cloud costs, and instability. Most devs build models; I build the high-performance machinery that keeps them running 24/7.
I am Muhammad Abubakar Nadeem, a Senior AI/ML Engineer. Ive built production-grade platforms (including university-scale tutoring systems) featuring advanced RAG pipelines, semantic search, and real-time Kafka backends. I dont just write codeI architect systems that scale.
What Youll Receive:
- High-Speed Serving: FastAPI backends optimized for sub-second latency.
- MLOps Excellence: Automated CI/CD, MLflow tracking, and DVC versioning.
- Deployment: Full Docker + Kubernetes manifests for AWS, GCP, or Azure.
- Observability: Prometheus & Grafana dashboards for drift and latency.
- Inference Opt: Quantization (ONNX/TensorRT) to slash infra costs.
Specializing in:
Computer Vision (YOLO), NLP/LLMs (vLLM/Triton), and Real-time Data Pipelines.
Message me with your tech stack, and lets turn your experiment into a reliable production feature today!
Get to know Maki
Maki
AI Specialist, Large Language Models, RAG and MLOps, PyTorch and TensorFlow
- FromPakistan
- Member sinceJan 2024
- Avg. response time1 hour
Languages
Urdu, English, Punjabi
Expert AI/ML Engineer with a BS in Data Science, specializing in building production-ready systems with PyTorch, TensorFlow, and Transformers. Proven track record in implementing RAG pipelines, Agentic AI, and Neural Networks to optimize business processes. Skilled in NLP, Computer Vision, Vector Databases, and Cloud Deployment (Docker, FastAPI, Flask).
FAQ
Is the source code and ownership included?
Yes, 100%. Upon completion, you receive full ownership of the FastAPI code, Dockerfiles, CI/CD scripts, and all configuration files.
Can you optimize my inference costs?
Absolutely. I implement quantization (ONNX/TensorRT) and batching techniques that reduce GPU/CPU usage, significantly lowering your monthly cloud infrastructure bills.
Which cloud providers do you support?
I build containerized solutions using Docker, which means they can run on any provider, including AWS (SageMaker/EKS), Google Cloud (Vertex AI), Azure ML, or private VPS servers.
Do you handle model retraining and drift?
In the Standard and Premium tiers, I set up MLOps pipelines (MLflow/DVC) and monitoring (Prometheus) to track model drift and ensure you know exactly when a model needs retraining.
What if my model is too slow?
I use quantization (ONNX/TensorRT) and batching to speed up inference by up to 5x.
How do I know when the model fails?
I set up Prometheus/Grafana alerts that notify you via Slack/Email the moment your model’s accuracy or latency drops.
Can you deploy LLMs locally?
Yes, I specialize in vLLM and Ollama for cost-effective local deployment.
Do you work with my existing dev team?
Absolutely. I provide full documentation and a handover session to ensure your team can maintain the system.
Can you work with my existing messy code?
Yes. I specialize in taking experimental Jupyter Notebooks or raw Python scripts and refactoring them into clean, modular, and production-grade software.
