Machine learning that works in production -
not just in a notebook.

We build, train, deploy and operate ML models that improve your business metrics continuously. Data pipelines, SageMaker training, production inference endpoints, drift monitoring, and automated retraining. The full lifecycle — not just the interesting part.

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40+

AI & ML systems deployed

Years production ML

AWS

ML certified engineers

Clutch

Trusted by

Machine learning use cases we build and operate

Six ML problem types. Each requires a different approach — and a team that can choose the right one rather than defaulting to the most fashionable algorithm.

Predictive Analytics & Forecasting

Demand forecasting, revenue prediction, customer lifetime value modelling, maintenance scheduling, inventory optimisation. Models trained on your historical data, deployed as APIs or batch processes, monitored continuously. One client reduced overstock by 23% and stockouts by 31% — sustained over 14 months in production.

Classification & Anomaly Detection

Fraud detection, churn prediction, document classification, defect detection, credit risk scoring, operational anomaly detection. Binary and multi-class with calibrated confidence scores

Natural Language Processing

Sentiment analysis, entity extraction, document classification, topic modelling, intent classification, contract analysis, medical coding. Custom models fine-tuned on your domain vocabulary.

Computer Vision

Image classification, object detection, defect inspection, document OCR and layout analysis, video analysis. Models trained on your labelled image data, deployed to SageMaker inference endpoints.

Recommendation Systems

Product recommendations (collaborative filtering, content-based, hybrid), content personalisation, search ranking, next-best-action models. Real-time and batch inference at scale.

LLM Fine-Tuning & Domain Adaptation

Fine-tune Llama 3, Mistral, or domain-specific models on your proprietary data for specialised tasks — medical coding, legal analysis, financial report generation. Training stays in your AWS environment.

Who hires us for machine learning

Data teams who've built a model but can't get it to production

The model achieves great accuracy on the held-out test set. Then it goes into a notebook drawer. We build the SageMaker pipeline, inference endpoint, monitoring, and retraining automation that turns a data science experiment into a production system.

Operations leaders whose ML models have stopped working

The demand forecast was accurate 18 months ago. Now it's consistently wrong. Data drift, distribution shift, upstream data changes — we audit, diagnose, and rebuild the model and monitoring that prevents this from happening again.

Companies with historical data and a specific prediction problem

You have 3 years of transaction history. You know churn is a problem. You suspect there's a pattern in the data. We validate whether the ML problem is solvable, scope the project, and deliver a model that outperforms your current heuristics.

Businesses with domain-specific language tasks

General-purpose LLMs handle your medical coding or legal classification at 70% accuracy. Fine-tuning on your proprietary data gets it to 92%. We run the fine-tuning, evaluation, and deployment — with your data staying in your AWS environment.

Production ML.
Not notebook ML.

Four things that separate an EB Pearls ML project from a model that achieves 94% on a test set and then silently degrades in production.

We distinguish between RAG, fine-tuning, and training from scratch

These are not interchangeable. RAG is fastest for knowledge retrieval and keeps data outside the model. Fine-tuning is for domain adaptation. Training from scratch is rarely justified. We'll tell you which approach is right for your problem — and why the others aren't.

Production ML, not notebook ML

Anyone can build a model that achieves 94% accuracy on a held-out dataset. We build models that maintain that performance in production over 12 months as real-world data shifts — with monitoring that catches degradation and retraining pipelines that fix it.

SageMaker-first for Australian compliance

Training data, model artefacts, and inference logs stay in your AWS environment in ap-southeast-2. No data leaves Australian infrastructure. This matters for healthcare, financial services, and any organisation with data residency requirements.

We work with messy, real-world data

Clean benchmark data is not what real businesses have. We have extensive experience with imbalanced classes, missing values, label noise, shifting distributions, and sparse historical data. Data quality remediation is often where the real performance gains are — not algorithm selection.

Our ML & ML Ops stack

★ marks our preferred production choice for Australian enterprise ML deployments.

Training & Experimentation

★ AWS SageMaker
★ PyTorch
TensorFlow / Keras
scikit-learn
XGBoost / LightGBM
Hugging Face Transformers

Data Engineering

★ Apache Spark / AWS Glue
★ AWS S3 + Delta Lake
Pandas / Polars
dbt
AWS Kinesis
SageMaker Feature Store

MLOps & Pipelines

★ MLflow
★ SageMaker Pipelines
Weights & Biases
DVC
GitHub Actions
ONNX (optimisation)

Monitoring & Inference

★ SageMaker Model Monitor
★ SageMaker Endpoints
Evidently AI
AWS CloudWatch
FastAPI (custom inference)
AWS Lambda

Real models.
Production metrics.

Two ML deployments. Numbers tracked over 12+ months in production — not test set performance.

It can be a little daunting to engage a mobile app development company. They proved they could work within my budget, made me realise the ideas stuck inside my brain, and helped me launch multiple mobile app development projects.

Jesse Milani

Founder, Pocket Fuel

EB Pearls provided great clarity on the scope, timeline, design, and stages of developing our app, which gives users access to a database of more than 1,000 roles. They stuck rigidly to our production schedule and delivered as promised.

Mike Fields

Director, Care Careers

I found EB Pearls great to work with, always willing to make changes and work close with the customer,I have highly recommended them to my friends and colleagues, great work EB Pearls.

Simon Lister

Founder

Throughout the app development process the developers ensure their team work closely with us and also ensure the app meet our needs and aligns with our business goals. They also manage effective communication, collaboration and project management. The app is essential and was also delivered on time within budget. We are extremely satisfied.

Dan Rosensweig

EB Pearls created a unique and special website and web design agency, perfect for our customers. We loved the team’s efficiency in creating us great user experience and a seamless website design plan, with beautiful on-brand illustrations. The team were patient, professional and always there to guide us and I wouldn’t hesitate to recommend them and work with them again in the future.

Julia Kish

Manager, Intellihub

They make sure to understand the purpose and goals of our mobile app, identifying the target audience, and defining our desired outcomes. They also identify and document the functional and non-functional requirements of the mobile app, such as features, functionalities, user interface, performance, security, and integration with other systems.

Lindsay Dawicki

The Discovery phase saved us from making expensive mistakes, which we didn’t even realise were there. It challenged some of our early assumptions, clarified what actually needed to be built first, and gave us a much stronger foundation before development started.

Kym Herbert

Product Lead, Optus

From data assessment to production model

Stage 01

Data & Problem Assessment

Audit your data quality, volume, and labelling. Validate the ML problem is solvable. Define success in business terms. Scope document with fixed-price quote.

Weeks 1–2

Stage 02

Data Engineering & Features

Build data pipelines. Engineer features. Address data quality issues. Establish train/validation/test splits. Build the evaluation framework.

Weeks 3-5

Stage 03

Model Development

Train and evaluate candidate models. Hyperparameter optimisation on SageMaker. Interpret model behaviour. Validate against business success metrics. Select production candidate.

Weeks 6-10

Stage 04

MLOps & Production

Build training pipeline. Deploy inference endpoint. Set up Model Monitor and alerting. Configure automated retraining triggers. Documentation and handover.
Weeks 11-14

How to work with us

Fixed-Price ML Project

Defined scope, price, and timeline. Best for well-scoped ML problems where the data, objective, and success metrics are clear before we start.

AUD $40,000–$200,000+

ML Operations Retainer

We own the model in production — monitoring, retraining, performance reporting, and improvement. You focus on the business; we keep the model working.

AUD $5,000–$15,000/month

Data & Feasibility Assessment

We audit your data, validate the ML problem, and tell you honestly whether a model will solve your business problem — and what it will take. Fixed fee, 2-week turnaround.

From AUD $9,500

Every question answered.

Can't find what you need?

AI is the broad field of intelligent systems. Machine learning is a subset where systems learn from data rather than explicit programming. Training a model on your historical data for a specific prediction task is ML. Using a pre-trained foundation model like GPT-4 for language tasks is generative AI. Both are often used together.

MLOps is the practice of reliably deploying, monitoring, and maintaining ML models in production. Without it, models degrade silently as data drifts, retraining is manual, and there is no audit trail. With MLOps, you have automated retraining, drift detection, model versioning, and production monitoring — so your model keeps working over time.

A focused ML model with SageMaker deployment: 10–14 weeks. A full MLOps platform with automated pipelines and monitoring: 14–20 weeks. Data quality remediation is usually the biggest variable — not algorithm selection.

Yes. Fine-tuning on proprietary data can significantly improve performance on domain-specific tasks — medical coding, legal analysis, financial reporting, technical support. We use SageMaker or Hugging Face on AWS, keeping training data in your AWS environment.

Yes. Imbalanced classes, missing values, label noise, shifting distributions, and sparse historical data are common in real businesses. Data quality remediation and feature engineering are often where the real performance gains are — not in algorithm selection.

Retail (demand forecasting, inventory optimisation), financial services (fraud detection, credit scoring), healthcare (risk scoring, medical coding), manufacturing (defect detection, predictive maintenance), and SaaS (churn prediction, customer lifetime value).

Train a custom ML model for specific prediction or classification tasks (churn, fraud, demand forecasting) when you have labelled historical data and need high accuracy on a narrow problem. Use an LLM for language understanding and generation where a general-purpose model handles the task well. The approaches are often complementary.

Focused ML model with SageMaker deployment: AUD $40,000–$90,000. Full MLOps platform with pipelines and monitoring: $90,000–$200,000+. Ongoing ML operations retainer: $5,000–$15,000 per month. We provide fixed-scope quotes after a data assessment.

AWS SageMaker is our preferred platform for training, fine-tuning, and deployment. It handles infrastructure complexity, provides managed inference endpoints, integrates with AWS, and keeps data in your environment. For experiment tracking we use MLflow.

Data drift detection, model performance monitoring against labelled production samples, automated alerting on metric degradation, and scheduled retraining pipelines. Model degradation is expected — the question is whether you catch it immediately or in 6 months.

Yes. We deploy using SageMaker within your AWS VPC, or using containerised inference services in your own environment. Training data, model artefacts, and inference logs stay in your infrastructure.

We define success metrics in business terms at the start of every project — not just model accuracy. For demand forecasting that might be overstock reduction percentage. For churn prediction it is revenue retained. We build evaluation frameworks that measure business impact.

1 Your Information

2 Book Meeting

3 Confirmation

Put machine learning into production.

45 minutes. We'll assess your data, define the right problem formulation, and give you a realistic view of what ML can achieve for your specific use case. No sales deck.

What to expect on your call

What to expect

1 Share a few details
Complete the form with your contact details and what you need help with.
2 Book your free discovery call
Once you submit the form, choose a time that suits you for your discovery call.
3 Privacy comes first
Sign an optional NDA to ensure the highest privacy level and protection of your idea.
4 Discovery call
We’ll discuss your goals, the support you need and answer your questions. If we’re a good fit, we’ll outline the next steps.

What to expect

1 Share a few details
Complete the form with your contact details and what you need help with.
2 Book your free discovery call
Once you submit the form, choose a time that suits you for your discovery call.
3 Privacy comes first
Sign an optional NDA to ensure the highest privacy level and protection of your idea.
4 Discovery call
We’ll discuss your goals, the support you need and answer your questions. If we’re a good fit, we’ll outline the next steps.

Machine learning that works in production -
not just in a notebook.