:: AI services / Machine learning development
Machine learning development services for better decisions
Not every AI problem needs a large language model. Forecasting demand, scoring leads, spotting fraud or reading images are often done better, faster and cheaper with classic machine learning trained on your own data. We build those models and keep them accurate.
Trained on your data
Explainable results
Monitored for drift
:: What we do
AI and ML development services
We start with the decision you want to improve, then choose the simplest model that does it well.
01
Predictive analytics
Forecast demand, revenue, churn and stock levels from your history, with confidence ranges you can plan around.
02
Scoring and classification
Lead scoring, credit and risk scoring, fraud flags and ticket routing, with the reasons behind each score.
03
NLP development
Classify, tag and extract information from emails, reviews, contracts and support tickets at scale.
04
Computer vision
Count, inspect and recognise objects in images and video for quality control, retail and field operations.
05
Recommendation systems
Product, content and next-best-action recommendations based on behaviour and context.
06
MLOps
Pipelines that retrain, test and deploy models automatically, and alert you when accuracy drops.
:: Use cases
Machine learning use cases with a clear payoff
Machine learning earns its keep where a business makes the same kind of decision thousands of times. These blueprints show where it fits and how it is built.
Distribution
Demand forecasting
Forecasts by product and location from sales history, promotions and seasons, so buyers order closer to what sells.
Read the blueprint
Utilities
Predictive maintenance
Sensor readings and repair history used to predict which assets will fail next and plan crews before they do.
Read the blueprint
Manufacturing
Defect detection
Vision models trained on images of good and bad parts, flagging defects on the line in real time.
Read the blueprint
Lending
Risk scoring and document checks
Applications scored with the reasons shown, and supporting documents read and checked automatically.
Read the blueprint
Insurance
Claims triage and fraud flags
Claims ranked by urgency and risk, with unusual patterns sent to an investigator for review.
Read the blueprint
Retail
Recommendations
Products and offers ranked for each customer from behaviour and context, tested against your current rules.
:: Step by step
The machine learning development process, step by step
Most ML projects that fail do so before any model is trained: the wrong question, the wrong data or no plan for production. This is the order we work in.
01
Frame the decision
Name the decision the model will improve, who acts on it and what a wrong prediction costs. That sets the target and the measure of success.
02
Audit the data
Check how much history there is, how clean it is, whether the outcome you want to predict is recorded, and whether anything leaks the answer.
03
Set a baseline
Measure how today’s process or a simple rule performs. A model is only worth building if it beats this by a margin that matters.
04
Engineer features and train
Turn raw records into useful signals, train several model types and pick on accuracy, speed and how easily the results can be explained.
05
Validate like it’s live
Test on data the model has never seen, from a later period, and check results across customer groups, regions or product lines.
06
Deploy where decisions happen
Serve predictions through an API, a nightly batch or straight into your CRM, ERP or dashboard, so people use them without changing tools.
07
Monitor and retrain
Track input data and accuracy, alert on drift, and retrain on a schedule or when performance drops below the agreed level.
:: How it works
How we develop machine learning models
A model is only as good as the data and the way it is measured.
01
Frame the decision
What will change if the prediction is right? We agree on the metric and the baseline to beat.
02
Prepare the data
Collect, clean and label data, and check it for gaps and bias.
03
Train and compare
Several model types tested against the baseline. We pick the one that is accurate and explainable.
04
Deploy and monitor
An API or batch job connected to your systems, with drift alerts and scheduled retraining.
:: Cost and timeline
How much does machine learning development cost?
Typical prices for our ML projects, based on a senior team at $25 to $35 an hour. A short data review comes first, so the quote reflects your actual data.
Proof of concept
$5k – $15k
3 to 6 weeks
One prediction task on your historical data, compared against today’s baseline, with a clear go or no-go.
Production model
$20k – $50k
2 to 4 months
A model deployed into your systems with data pipelines, monitoring, retraining and documentation.
ML platform
$50k – $120k+
4 to 8 months
Several models sharing pipelines, a feature store, automated testing and deployment, and full MLOps.
What it costs to run
Most ML models are cheap to run: hosting and scheduled retraining often cost tens to a few hundred dollars a month. Vision models on video and very large datasets cost more. We size this before build.
What moves the price
State and spread of your data
Number of models and prediction tasks
Real-time versus batch predictions
Explainability and compliance needs
Integration with your CRM, ERP or apps
:: Data readiness
Is your data ready for machine learning?
You don’t need perfect data. You need enough of the right data. This quick check tells you where you stand before spending anything.
Signs you are ready
At least one to two years of history
The outcome you want to predict is recorded
Data sits in systems you can export or query
Someone knows what the fields mean
A team that will act on the predictions
A way to measure if decisions improved
Signs to fix first
Key outcomes only live in people’s heads
Records change meaning between systems
Only a few hundred examples of the outcome
No owner for the data or the model
Predictions with nowhere to go
:: Why Sterling
What you get from our machine learning team
A clear baseline, so improvement is measured, not claimed
Explanations for each prediction where decisions affect people
Models delivered as APIs or batch jobs your systems can call
Retraining pipelines so accuracy holds over time
Full ownership of code, data pipelines and models
:: Tools we use
Python
scikit-learn
XGBoost
PyTorch
TensorFlow
spaCy
Hugging Face
OpenCV
MLflow
Airflow
AWS SageMaker
Vertex AI
Azure ML
4.9
Average rating on Clutch · 350+ projects since 2019
:: Questions
Questions about machine learning
What do machine learning development services include?
Do we have enough data for machine learning?
Machine learning or generative AI: which do we need?
How much does machine learning development cost?
How do you keep models accurate over time?
What are common machine learning use cases in business?
What is the machine learning development process?
What is MLOps and do we need it?
Can you explain why the model made a prediction?
Which machine learning tools do you use?
How long does a machine learning project take?
:: Related AI services
Other ways we can help
Generative AI development
LLM apps, RAG and fine-tuning on your data
AI software development
Custom AI products and internal tools, built to production
AI integration services
Connect AI to Salesforce, ERP and internal systems
AI consulting services
Plan before you build: readiness, strategy and proof of concept
Send us your brief. We’ll help turn it into reality
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