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I will deploy ai ml models to AWS, gcp or azure
I am a DevOps & Cloud Engineer with hands-on expertise in designing, automating, and managing modern infrastructures across AWS, Azure,Digital Ocean,Huwaie Cloud,Render,Vercel and GCP. I specialize in...
About this Gig
Trained your ML model or LLM but stuck on deployment?
I deploy AI and machine learning models to production on AWS, GCP, and Azure fast, clean, and built to scale. Whether it's a pre-trained model, a fine-tuned LLM, or a full MLOps pipeline, I handle everything from containerisation to monitoring.
What I deliver:
Deploy any ML/LLM model as a production REST API
Docker + Kubernetes deployment (EKS, GKE, AKS)
Automated CI/CD pipeline push code, auto-deploy
FastAPI / TorchServe / Triton Inference Server setup
GPU inference support (CUDA, T4, A100)
Model versioning and registry with MLflow
Monitoring with Prometheus and Grafana
Auto-scaling for traffic spikes
Full MLOps pipeline training, validation, deployment, monitoring
Infrastructure as Code with Terraform and Helm
Frameworks: PyTorch · TensorFlow · Hugging Face · scikit-learn · XGBoost
Clouds: AWS · GCP · Azure · SageMaker · Vertex AI · Azure ML
Every delivery includes full documentation. Message me before ordering for a free consultation I will review your model and give you a clear plan.
Other DevOps Engineering Services I Offer
FAQ
What types of AI and ML models can you deploy to cloud?
I deploy any Python-based ML or LLM model PyTorch, TensorFlow, Hugging Face, scikit-learn, XGBoost, and custom models. I also handle LLM inference, computer vision APIs, and NLP models on AWS, GCP, and Azure using Docker and Kubernetes.
What is included in the MLOps pipeline setup?
The full MLOps pipeline covers training automation, model validation, deployment with CI/CD, model versioning with MLflow, and production monitoring with Prometheus and Grafana. Every time you retrain your model, the pipeline automatically validates and deploys it no manual steps.
Which cloud platforms do you support AWS, GCP or Azure?
I support all three. On AWS I use EKS, SageMaker, and EC2. On GCP I use GKE and Vertex AI. On Azure I use AKS and Azure ML. I can also recommend the most cost-effective cloud based on your model size and expected traffic.
Do you support GPU deployment for deep learning and LLM inference?
Yes. I configure GPU instances with CUDA support and set up high-performance inference servers like NVIDIA Triton or TorchServe for deep learning models and LLMs that require GPU acceleration.
What if I only have a trained model file and no cloud setup yet?
No problem that is the most common situation. I handle everything from scratch: cloud account setup, networking, containerising your model with Docker, and deploying it as a live API. Just share your model file and I take it from there.
Will my ML model API be able to handle high traffic and auto-scale?
Yes. With the Elite and Prime packages I configure Kubernetes horizontal pod autoscaling so your API automatically spins up more instances under load and scales back down to save cost fully managed and production-grade.
