RejoiceHub services
Machine Learning
Build and evaluate machine-learning models for prediction, classification, and data analysis, using data and success criteria agreed with your team.
Scope, milestones, and responsibilities agreed before the build.
How we work
From the first question to working software
Capabilities
What we can build with you
Choose the work your project needs. We define the details and dependencies together.
Data preparation
Review the available data, labels, missing values, and access permissions.
Model development
Compare candidate models with a baseline using representative training and evaluation data.
Deployment and monitoring
Connect the model to your application and monitor inputs, errors, and performance after release.
Delivery process
From scope to launch
Review the work at each stage, with clear responsibilities for delivery and handover.
Discover
Map the current workflow, users, systems, and constraints.
Design
Agree the data flow, user journeys, permissions, and acceptance criteria.
Build
Implement the agreed scope in increments the team can review.
Test
Check the main tasks, failures, access controls, and agreed performance requirements.
Launch
Prepare deployment, monitoring, documentation, and support responsibilities.
Project planning
Agree the work before the build
We review the current workflow and define what the project will deliver.
What we review
The current workflow
Identify the tasks, handoffs, and exceptions that the project needs to address.
Data and system access
Review the information and integrations available, including permissions and constraints.
Delivery requirements
Agree the users, acceptance criteria, timeline, and responsibilities for operating the result.
What you receive
An agreed scope
A definition of the work, dependencies, deliverables, and estimate.
Work your team can review
Designs and working increments checked against the agreed requirements.
A plan for operation
Deployment, documentation, monitoring, and support responsibilities included in the handover.
Technology
A stack that fits your systems
We choose tools around your requirements, existing systems, and operating constraints.
- Language
- Python
- AI/ML
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- Hugging Face
- MLOps
- MLflow
- Apache Airflow
- Cloud
- AWS SageMaker
- Google Vertex AI
- Infrastructure
- Docker
- Database
- PostgreSQL
Scope-based engagement
Start with a defined piece of work
Start with a paid diagnostic or a fixed-scope sprint. We agree the deliverables, dependencies, and estimate before work starts.
Agreed before work starts
- Requirements and acceptance criteria
- Milestones and review points
- Documentation and handover responsibilities
FAQs
Questions about the engagement
Common questions about machine learning, answered.
Share the task, current systems, and constraints. We will review the requirements and agree the scope of the first stage.
We review the interfaces, data access, and permissions your systems provide. Integration work and any required changes are included in the scope.
They depend on the features, integrations, data, and review requirements. We provide an estimate for the agreed scope before work starts.
We define access permissions and review steps with your team. Requirements for sensitive data, hosting, and retention are part of the project scope.
We agree who operates the system, what documentation is needed, and the support and maintenance arrangements before handover.