Amazon Web Services
AWS Machine Learning and AI Services
Turn data into production machine learning on AWS, from use case selection and feature engineering to deployed, monitored models built on Amazon SageMaker AI and AWS AI services.
Machine Learning That Reaches Production
Many organizations have trained promising models on AWS; far fewer run them reliably in production. The gap is engineering: reproducible training pipelines, versioned features, model registry and approvals, scalable inference endpoints, monitoring for drift and retraining when performance degrades. Amazon SageMaker AI, the managed ML service formerly known simply as Amazon SageMaker, provides building blocks for each of these steps, while AWS AI services such as Amazon Textract, Amazon Rekognition and Amazon Personalize cover common tasks without custom models.
Hanoi Technology helps you choose the simplest approach that meets your accuracy requirements, build it with MLOps practices from the start and connect predictions to the business processes that use them.
The Challenge
Why ML Projects Stall on AWS
Model accuracy is rarely the main obstacle; operationalization is.
Notebooks Without Pipelines
Models trained manually in notebooks cannot be reproduced, audited or retrained reliably when data changes.
Data Not Ready for ML
Training data is scattered, inconsistently labeled or lacks history, and features used in training differ from those available at inference time.
No Monitoring After Launch
Deployed models degrade silently as customer behavior, prices or seasons change, and nobody notices until business results decline.
Unclear Business Integration
Predictions that do not feed into a workflow, application or decision create no value, however accurate they are.
Business Value
What Production ML on AWS Delivers
Reproducible Models
SageMaker Pipelines and Model Registry capture data, code, parameters and approvals, so every model version can be traced and rebuilt.
Consistent Features
SageMaker Feature Store shares features between training and real-time inference, reducing training and serving skew.
Scalable Inference
Real-time, serverless, asynchronous and batch inference options match deployment to latency and cost requirements.
Early Drift Detection
SageMaker Model Monitor and SageMaker Clarify track data quality, model quality and bias, triggering alerts or retraining.
Faster Results with AI Services
Textract, Rekognition, Comprehend, Transcribe and Personalize solve common problems without training models from scratch.
Governed ML Lifecycle
SageMaker Unified Studio, the SageMaker Catalog and IAM controls bring data and ML assets under consistent governance.
Choose the Simplest Model That Works
A managed AI service or a well-tuned gradient boosting model often beats a complex deep learning system in cost, speed and maintainability. We benchmark options honestly and prefer the approach your team can operate with confidence.
Our Services
Hanoi Technology’s End-to-End AWS Machine Learning and AI Services
Data science and ML engineering across the full lifecycle on AWS.
ML Use Case Discovery
We identify problems where ML can improve decisions, estimate value, check data availability and define success metrics with business owners.
- Use case assessment
- Data readiness review
- Success metrics
Data Preparation and Feature Engineering
We build data pipelines and features with AWS Glue, Amazon EMR and SageMaker AI Processing, and manage them in SageMaker Feature Store.
- Feature pipelines
- Feature Store setup
- Data quality checks
Model Development
We develop and evaluate models in SageMaker AI using frameworks such as XGBoost, PyTorch and scikit-learn, or start from foundation models in SageMaker JumpStart.
- Model experiments
- Evaluation report
- Explainability analysis
MLOps and Deployment
We implement SageMaker Pipelines, Model Registry, CI/CD and inference endpoints, with approval gates and rollback strategies.
- Training and deployment pipelines
- Model registry
- Production endpoints
Model Monitoring and Retraining
We configure Model Monitor, Clarify and CloudWatch alarms, and automate retraining when data or performance shifts.
- Monitoring dashboards
- Drift alerts
- Retraining workflows
AWS AI Services Integration
We integrate Textract, Rekognition, Comprehend, Transcribe, Translate and Personalize into applications, with human review through Amazon Augmented AI where needed.
- Service integration
- Human review workflows
- Accuracy evaluation
Our Approach
How We Deliver Machine Learning on AWS
A phased approach that validates value before scaling.
- 01
Frame the Problem
We define the decision to improve, the target metric and the baseline that the model must beat.
Deliverables: Problem statement · Baseline measurement
- 02
Prepare Data
We collect, clean and label data, build features and split datasets to avoid leakage.
Deliverables: Training datasets · Feature definitions
- 03
Experiment and Validate
We train and compare candidate models, analyze errors and confirm business value with stakeholders.
Deliverables: Experiment results · Model selection rationale
- 04
Deploy with MLOps
We deploy the selected model through automated pipelines and integrate predictions into business systems.
Deliverables: Production deployment · Integration
- 05
Monitor and Improve
We track model and business metrics, retrain as needed and add improvements over time.
Deliverables: Monitoring reports · Retraining schedule
Use Cases
Machine Learning Use Cases on AWS
Examples of ML solutions we build.
Demand Forecasting
A distributor forecasts weekly demand by product and warehouse with SageMaker AI, improving replenishment planning.
Churn Prediction
A subscription business scores customers by churn risk daily and routes high-risk accounts to retention teams in the CRM.
Document Data Extraction
Invoices and forms are processed with Amazon Textract, with low-confidence fields routed to human reviewers.
Product Recommendations
An online store uses Amazon Personalize or a custom ranking model to show relevant products on home and product pages.
Price and Valuation Models
Property or asset prices are estimated with gradient boosting models trained on historical transactions and location features.
Route and Dispatch Optimization
Delivery ETA predictions feed dispatch logic to assign orders to couriers more efficiently.
Technology
ML and AI Technologies on AWS
AWS services and frameworks we work with.
- Amazon SageMaker AI
- SageMaker Unified Studio
- SageMaker Pipelines
- SageMaker Feature Store
- SageMaker Model Monitor
- SageMaker Clarify
- SageMaker JumpStart
- SageMaker HyperPod
- Amazon Textract
- Amazon Rekognition
- Amazon Comprehend
- Amazon Personalize
- Amazon Transcribe
- AWS Glue
- PyTorch
- XGBoost
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · Logistics
Route optimization and fleet visibility for a regional logistics company
A regional delivery company handling thousands of shipments a day was planning routes and dispatching by hand. We built dynamic route optimization, a fleet dashboard and a driver app.
Read the Full StoryMore Success Stories
Real estate
90%pricing accuracyTransparent AI property valuation for a multi-city marketplace
A growing real estate marketplace wanted buyers to trust its prices. We built a valuation engine and property pages that compare the estimated value with the asking price.
E-commerce and retail
+32%conversion ratePersonalized shopping experience for a US fashion retailer
A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Put Machine Learning to Work on AWS
Book a free consultation to discuss a decision you want to improve with ML. We will assess data readiness and suggest the fastest route to a measurable first result.
AWS Machine Learning FAQ
What is the difference between SageMaker AI and the new Amazon SageMaker?
AWS renamed the original machine learning service Amazon SageMaker AI and now uses the Amazon SageMaker name for a broader platform that unifies data, analytics and AI. That platform includes SageMaker Unified Studio, the SageMaker Catalog and a lakehouse architecture, with SageMaker AI available inside it and as a standalone service. For ML engineering, the core capabilities such as training, pipelines and endpoints remain in SageMaker AI.
Should we use an AWS AI service or build a custom model?
If a managed service such as Textract, Rekognition, Comprehend or Personalize meets your accuracy needs, it is usually faster and cheaper to implement and maintain. Custom models make sense when your problem is specific to your data, when managed services do not reach required accuracy, or when you need full control. We benchmark both options on your data before recommending one.
How much data do we need?
It depends on the problem and the approach. Forecasting needs enough history to capture seasonality, classification needs sufficient labeled examples of each class, and recommendations need interaction data. Pretrained and foundation models reduce data requirements for some tasks. During discovery we assess data volume, quality and labels, and tell you honestly if a use case is not yet feasible.
How do you keep models accurate over time?
We monitor input data distributions, prediction distributions and, where ground truth arrives later, actual model accuracy, using SageMaker Model Monitor and custom metrics. Alerts trigger investigation or automated retraining through SageMaker Pipelines. New model versions are evaluated against the current production model and approved in the Model Registry before deployment, with rollback available if needed.
What drives the cost of ML on AWS?
Main costs are compute for training and processing, inference endpoints that run continuously or on demand, storage and data processing. Choices such as serverless or asynchronous inference for spiky traffic, spot training for interruptible jobs and right-sized instances reduce costs considerably. We estimate costs during design and track them per model in production.
How long until a model is in production?
For a well-defined use case with available data, a first production model typically takes two to four months, including discovery, data preparation, experimentation, deployment and integration. Simpler integrations of AWS AI services can be faster. Projects requiring significant data collection or labeling take longer. We demonstrate progress through sprint demos and weekly written updates throughout.