Services

Hire a dedicated Amazon SageMaker AI developer to bring your machine learning project to the next level. Our consultants work as part of your existing development team / process or as a stand alone resource, ensuring the highest standards of communication throughout the project.

We work across both generations of the service: Amazon SageMaker AI (the build / train / deploy service most teams know, renamed at re:Invent 2024) and the next-generation Amazon SageMaker unified platform (Unified Studio, Lakehouse, and Catalog). If your team is unsure which one you are actually using, start here.

Our expert machine learning developers / architects can help with:

GENERATIVE AI

• Fine-tuning open-weight models (Llama, Mistral, Qwen) with SageMaker JumpStart and HyperPod
• Retrieval-augmented generation (RAG) on Amazon Bedrock Knowledge Bases or custom OpenSearch / pgvector stacks
• LLM inference engineering with the Large Model Inference (LMI) and Hugging Face TGI containers, Inferentia2, and quantization
• Evaluation harnesses, guardrails, and build-vs-buy decision support

See our dedicated Generative AI on AWS page.

DEVELOP / BUILD MODELS

• Preparation and collection of training data
• Data labeling strategy (SageMaker Ground Truth, human-in-the-loop review)
• Development in SageMaker Studio JupyterLab and Code Editor spaces, or SageMaker Unified Studio
• Algorithm selection and optimization
• Custom training and inference containers (bring-your-own-container)
• PyTorch, TensorFlow/Keras, Hugging Face Transformers, XGBoost, LightGBM, scikit-learn, and Spark ML, plus SageMaker JumpStart foundation models and the Hugging Face / PyTorch deep learning containers
• Reinforcement learning and RLHF / RLAIF fine-tuning (Ray RLlib, TRL) on SageMaker training jobs

TRAINING

• Distributed training with PyTorch FSDP / DDP, DeepSpeed, and the SageMaker distributed training libraries
• Large-scale and foundation-model training on SageMaker HyperPod, including flexible training plans for reserved accelerator capacity
• Environment set up, experiment tracking, and reproducibility
• Hyperparameter tuning and model optimization
• Training cost engineering: spot training, checkpointing, right-sizing accelerator instances

MLOPS

• SageMaker Pipelines and the Model Registry for CI/CD of models
• Feature Store, Model Monitor, and drift detection
• Multi-account MLOps landing zones built with CDK or Terraform
• Cost governance and tagging strategy

See our dedicated MLOps on SageMaker AI page.

DEPLOYMENT

• Real-time, serverless, asynchronous, and batch inference endpoints on SageMaker AI
• Multi-model and multi-container endpoints
• LLM serving with LMI / TGI containers
• Edge deployment via ONNX Runtime and AWS IoT Greengrass V2 on NVIDIA Jetson Orin, Raspberry Pi 5, and AMD / Arm targets
• Cost optimization with Inferentia2 and Graviton instances

MIGRATION & MODERNIZATION

• Migrating Apache MXNet, Chainer, and TensorFlow 1.x workloads to PyTorch
• Moving notebook-instance estates to Studio spaces
• Replacing SageMaker Edge Manager (discontinued April 2024) with ONNX Runtime and IoT Greengrass V2
• Adopting SageMaker AI and the unified SageMaker platform from classic setups

See our dedicated Legacy ML Modernization page.

USE CASES

• Recommendation engines
• Computer vision and object recognition
• Real-time video analysis
• Forecasting: price, demand, and capacity
• Image classification and biomedical image segmentation
• Fraud and anomaly detection
• Document understanding and extraction with LLMs
• Internal knowledge assistants and customer-facing chat
• Text to speech and speech to text
• Hazard detection

... and much more