Put ML into Production on AWS — Not Just Notebooks
We take machine learning from Jupyter notebooks to production inference endpoints on AWS. SageMaker pipelines, Bedrock integrations, real-time and batch prediction APIs — built for teams that need ML to work reliably at scale, not just win a Kaggle competition.
Trusted by innovative teams worldwide
AWS ML Certified Engineers
Our ML engineering team combines AWS platform certifications with deep data science and MLOps expertise.
Full-Stack ML on AWS — From Data to Deployed Models
We handle the entire ML lifecycle on AWS — data pipelines, model training, deployment, monitoring, and optimization. Not just the fun modeling part.
SageMaker ML Pipelines
End-to-end ML workflows with SageMaker Pipelines — data preprocessing, feature engineering, model training, hyperparameter tuning, and model registry. Automated, reproducible, and version-controlled.
Bedrock & Foundation Models
Amazon Bedrock integration for generative AI use cases — RAG architectures, fine-tuned foundation models, and Claude/Titan/Llama deployments with proper guardrails, prompt management, and cost controls.
Real-Time Inference
SageMaker endpoints with auto-scaling, multi-model endpoints for cost efficiency, and inference optimization using model compilation and quantization — sub-10ms P99 latency for production workloads.
Model Monitoring & Drift Detection
SageMaker Model Monitor for data quality, model quality, bias detection, and feature attribution drift — with automated alerts and retraining triggers when model performance degrades.
Feature Store & Data Pipelines
SageMaker Feature Store for online and offline feature serving, Glue ETL for data processing, and Kinesis for real-time feature computation — ensuring consistent features across training and inference.
ML Cost Optimization
Spot instances for training, serverless inference for bursty workloads, model compilation for faster/cheaper inference, and right-sized instance selection — most clients see 40-50% reduction in ML compute costs.
Your ML Team Built a Great Model. Now Who's Going to Deploy It?
We bridge the gap between data science and production engineering on AWS. Let's talk about your ML deployment challenges.
We're ML engineers, not data scientists. We make models work in production.
The gap between a working notebook and a reliable production ML system is massive. We specialize in that gap — taking trained models and building the infrastructure, pipelines, monitoring, and automation needed to serve predictions reliably at scale on AWS.
MLOps That Keeps Models Working Long After Deployment
Deploying a model once is easy. Keeping it accurate, fast, and cost-efficient over months and years — that's where MLOps matters.
Why ML Teams Choose OpenMalo for AWS Deployment
We focus on the MLOps and infrastructure side — the part most data science teams struggle with and most DevOps teams don't understand.
Let's Deploy Your ML Models on AWS
Tell us about your ML challenges — whether it's a first deployment or scaling an existing system, we'll design the right approach.
Our Engagement Process
ML Assessment
Review of your current models, data pipelines, infrastructure, and deployment challenges — identifying the fastest path from notebooks to production endpoints.
MLOps Architecture
Target-state ML infrastructure design — SageMaker pipeline configuration, inference strategy, monitoring plan, and cost projections tailored to your model types and traffic patterns.
Build & Deploy
SageMaker pipeline construction, model optimization, endpoint deployment, feature store setup, and monitoring configuration — built incrementally with your data science team.
Validate & Optimize
Load testing, latency optimization, cost benchmarking, A/B testing infrastructure, and drift detection validation — ensuring models perform correctly and efficiently at production scale.
Monitor & Iterate
Production monitoring dashboards, automated retraining triggers, model performance reports, and ongoing optimization — keeping your ML system healthy long after initial deployment.
What Our Clients Say
“Our fraud detection model sat in a notebook for 8 months because nobody could figure out how to deploy it reliably. OpenMalo had it serving real-time predictions on SageMaker within 3 weeks — with monitoring, auto-scaling, and automated retraining.
“They cut our SageMaker inference costs by 52% using multi-model endpoints and serverless inference for our low-traffic models. That's $11K/month back in our budget — and latency actually improved.
“The Bedrock RAG architecture OpenMalo built for our medical literature search is remarkable. Accurate, fast, and properly guardrailed for healthcare use. Their understanding of both the ML side and the compliance side is unique.
Fraud Detection Model: From Notebook to Production in 3 Weeks
ML Production Deployment for FraudShield
How we took a fraud detection model from an 8-month-old Jupyter notebook to a real-time SageMaker inference endpoint — processing 5,000 transactions per second with sub-8ms latency and automated retraining.
High-performing fraud model stuck in a notebook
FraudShield's data science team had built a fraud detection model with 97% precision, but it had been sitting in a Jupyter notebook for 8 months. The engineering team lacked ML deployment experience, and early attempts at self-hosted serving were unreliable and expensive.
Our Approach: Model optimization and compilation for SageMaker, real-time endpoint with auto-scaling, SageMaker Model Monitor for drift detection, automated retraining pipeline with SageMaker Pipelines, and A/B testing infrastructure — deployed in 3 weeks.
Read Full Case StudyFrequently Asked Questions
Our core stack includes SageMaker (training, endpoints, pipelines, feature store, model monitor), Bedrock for foundation models, Glue and Kinesis for data pipelines, and Lambda for lightweight inference. We also work with Comprehend, Rekognition, Textract, and Personalize for specific use cases.
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