Machine Learning
ENGINEERING EXPERTISE · MACHINE LEARNING
Machine Learning
BlastAsia builds machine learning models into your product using Azure Machine Learning — predictive maintenance, demand forecasting, and diagnostic support trained on your actual operating data, not generic industry benchmarks. Available across every engagement model.
25 Years, Since 2001
Trained on Your Data, Not Benchmarks
Built on Azure Machine Learning
WHAT THIS COVERS
Machine learning here means models built for a specific operational problem inside your product — not a generic ML platform. Built using Azure Machine Learning, applicable across a range of industries and data types.
MANUFACTURING
Predictive Maintenance
Models trained on equipment sensor and event data to flag likely failures before they happen, reducing unplanned downtime and reactive maintenance costs.
SALES & MARKETING
Demand & Revenue Forecasting
Forecasting models built on historical sales and campaign data, supporting inventory planning, staffing, and marketing spend decisions with actual predictive signal.
MEDICINE & HEALTHCARE
Diagnostic Support Models
Models that assist (not replace) clinical decision-making, trained on structured patient and diagnostic data, built with the validation rigor healthcare use cases require.
WHERE THIS SHOWS UP
Applied Across Every Engagement Model
Machine learning models get built as part of your product's roadmap, whichever engagement model you're running.
APPLIED IN PRACTICE
Where Our Machine Learning Work Has Shown Up
A few real engagements where a machine learning model was built into a client's operations, not just prototyped.
INDUSTRIAL IOT
Sensor-driven event data from connected hardware fed into a global reporting dashboard, laying groundwork for predictive maintenance modeling on top of the same data pipeline.
MARITIME / CREWING
Operational data consolidated across a crewing platform's booking and compliance workflows, structured in a way that supports forecasting and staffing-demand modeling downstream.
FINANCE
Structured transaction and reporting data from a private banking platform's subsystems, the kind of clean, integrated dataset machine learning models depend on.
Client identities abstracted. These engagements reflect the data foundations machine learning work is built on — not standalone ML case studies.
What the software layer underneath a model actually needs to look like before it can ship reliably.
BlastAsia · Blog
How a trained model gets connected into the legacy data pipelines it actually has to run on.
BlastAsia · Blog
