A+ rating
Average 5.00 ratingMachine Learning Development Services
CDU delivers full-stack machine learning development services, from early-stage ML consulting to fully managed model operations. Every service below runs as a standalone engagement or as part of a broader AI and data transformation.
Machine Learning Consulting
We review your data, use case and business goals to scope a machine learning strategy that's realistic for your budget and timeline.
Recommendation Engines
Personalisation and recommendation systems tuned to your product's catalogue and user behaviour.
Custom ML Model Development
Our machine learning engineers design, train and validate models for classification, regression, forecasting and recommendation tasks.
Data Engineering for ML
Clean, reliable feature pipelines so your models train on data you can trust.
Predictive Analytics
We build forecasting and propensity models that turn historical data into decisions your team can act on.
Model Monitoring & Retraining
Ongoing drift detection, performance tracking and scheduled retraining so accuracy doesn't quietly decay.
Computer Vision Development
Image classification, object detection and quality-inspection models trained on your own data.
AI Strategy & Roadmapping
A prioritised roadmap that separates genuine ML opportunities from problems better solved with simple automation.
Natural Language Processing (NLP)
Text classification, sentiment analysis, entity extraction and document intelligence built for your domain language.
Custom AI Software Development
End-to-end AI-powered applications, from the model through to the interface your team and customers actually use.
LLM & Generative AI Integration
We fine-tune, prompt-engineer and integrate large language models, including retrieval-augmented generation (RAG) over your own data.
MLOps & Model Deployment
We package, deploy and version your models with CI/CD pipelines built for machine learning, not just application code.
Machine Learning Technology We Work With
CDU is platform-agnostic across AWS, Azure and Google Cloud, and we're comfortable recommending against a tool or framework when it isn't the right fit for your use case.
We work across the ML ecosystem, from Python, TensorFlow and PyTorch to Scikit-learn and XGBoost, with MLOps powered by MLflow, Kubeflow, Docker and Kubernetes. Our capabilities extend across AWS SageMaker, Azure Machine Learning and Google Vertex AI, supported by OpenAI, Hugging Face, LangChain and modern data platforms. This approach keeps your machine learning environment scalable, production-ready and aligned with your business goals.
Benefit’s the Services
Get the right Machine Learning stack, tailored to your business needs.
Tailored ML Stack Selection
Flexible Model Architecture
Scalable ML Infrastructure
Connected Data Ecosystem
ML Frameworks & Languages
Python, TensorFlow, PyTorch, Scikit-learn, XGBoost
MLOps & Deployment Tools
MLflow, Kubeflow, Docker, Kubernetes, Airflow
Cloud AI & ML Platforms
AWS SageMaker, Azure Machine Learning, Google Vertex AI
LLM, NLP & Generative AI
OpenAI, Hugging Face ,LangChain, spaCy, Vector databases
Data Engineering & Analytics
Pandas, Apache Spark, SQL, Power BI, Snowflake
How We Deliver Machine Learning Projects
A structured path from first data review to fully managed model operations, built to reduce the risk that's typical of machine learning projects.
ML readiness & data assessment
Roadmap & fixed-scope quote
Build, train & validate
Deployment & handover
Managed monitoring & retraining
Hey 👋 I am Gokul, Sr. Business Consultant
Let's talk about your growthWhy Choose CDU for Machine Learning Development
CDU's machine learning engineers have built production ML systems across finance, retail and logistics, so data pipelines, models and infrastructure are designed together not as separate projects that never quite connect.
Typical Outcomes After a Machine Learning Engagement
The figures below reflect the typical ranges our clients have reported across recent machine learning engagements, not guarantees. Your results depend on your starting data quality and use case.
User Training
60%
Faster OnboardingIntegrations
130+
Dealerships ConnectedOperations
99.9%
Platform Availability
Choose how you want to start
Org Health Check
Audit your existing org to find quick wins before investing further.
- Full architecture review
- Security & compliance audit
- Performance analysis
- Recommendations report
Implementation
POPULARFull cloud setup from scoping to go-live with fixed-price delivery.
- Sales or Service Cloud Core
- Custom Apex / LWC logic
- Full data migration & QA
- Comprehensive user training
Managed Services
Dedicated admin support without the overhead of a full-time hire.
- 24/7 technical helpdesk
- Automation & workflow shifts
- Release and health compliance
- Config and report build-outs
Trusted by Businesses.
Backed by Results.
Real partnerships. Honest feedback. See why organisations across Australia trust Cloud Downunder to design, build, and support their digital products.
Top B2B Company
4.8
out of 5Verified Google Reviews
4.8
out of 5Top Software Development Company
4.8
out of 5Frequently Asked Questions
Everything you need to know about Cloud Downunder's services, process, and Australian-local approach.
Still have questions?
We're here to help. Reach out directly and our specialists will respond within 2 business hours.
A machine learning development company designs, builds and deploys models that learn from your data — covering everything from data engineering and model training to MLOps, deployment and ongoing monitoring. Most Australian businesses engage a machine learning partner either for a single project, an embedded team, or fully managed ML operations.
Cost depends on the scope and data readiness of your project. A fixed-price ML readiness assessment is the usual starting point, followed by a scoped model development project or an ongoing managed ML retainer. Most engagements are quoted after a short discovery call once we've reviewed your data and use case.
A machine learning model development project typically covers data preparation and feature engineering, model training and validation, deployment into your production environment, monitoring setup, and full documentation handed over to your engineering team.
A direct hire suits teams that need a permanent, embedded resource working across multiple projects. A managed machine learning service suits teams that need senior-level coverage — including deployment, monitoring and retraining — without the cost and hiring risk of a full-time role. Many Australian businesses combine both.
Machine learning is the broader discipline of building models that learn patterns from data, including classification, forecasting and recommendation systems. Generative AI is a subset focused on models — including large language models — that generate new text, images or content, often through fine-tuning or prompt engineering rather than training from scratch.
A single machine learning model typically takes three to eight weeks from data review to production deployment, depending on data quality, feature complexity and how many systems it needs to integrate with. Larger or multi-model projects are usually delivered in phases.
Yes. Our MLOps services cover model versioning, CI/CD for ML pipelines, drift detection, performance monitoring and scheduled retraining, supported during Australian business hours for both cloud-native and self-managed deployments.
Yes. We scope generative AI and LLM integrations — including retrieval-augmented generation (RAG), prompt design and evaluation — as a proof of concept first, so the use case is proven with your own data before it's rolled out more broadly.
A data scientist focuses on analysis, experimentation and generating insight, often working in notebooks and presenting findings. A machine learning engineer focuses on taking models into production — building the pipelines, infrastructure and monitoring needed to run them reliably at scale.
Not necessarily — data quality issues are common and part of what an ML readiness assessment identifies. What matters most is having enough relevant historical data for your use case; our data engineering work then cleans, structures and prepares it for model training.
Ready to scale your growth?
We help ambitious teams build digital products and campaigns that convert.
How can I get in touch?
Reach us via the contact form, email, or phone. We'll connect you with the right expert.
What support do you offer?
From strategy and design to development, cloud, AI, and ongoing support we're with you every step.
How fast will I get a reply?
We typically respond to all enquiries within 1 business day.
