AI SOLUTIONS > ai-solutionStop paying people to do
Stop paying people to do
what software can do automatically.
Custom AI and machine learning models built on your data, trained, validated and shipped into production, not left in a notebook
THE EDITORIAL DEFINITION
Intelligence that initiates.
AI/ML development is the discipline of turning raw business data into a model that makes a decision, a price, a risk score, a recommendation, a forecast, reliably and at scale.\n\nMost AI initiatives stall not at the model but at the handoff: a notebook that works once but never reaches production, retrains, or monitoring. iSkylar builds the full pipeline, data engineering, model training, evaluation and MLOps — so the model your data scientists prove out is the same one running in production six months later.
This is for you if...
You have valuable data but no model in production
Years of transaction, usage or sensor data sitting in a warehouse, with no model turning it into a forecast, score or recommendation your team can act on.
Your data science work never makes it past the notebook
A model proved out in a Jupyter notebook isn't a product. We build the pipeline, API and monitoring that turns a proof of concept into something your application can call in production.
You need ML that keeps working after launch
Models decay as real-world data drifts. We build retraining, monitoring and evaluation into the system from day one, not as an afterthought.
The Implementation Roadmap
01
Problem & Data Scoping
Define the prediction or decision target, available data sources, label quality and success metrics with stakeholders
TIMELINE
Week 1-2
02
Data Engineering & Feature Pipeline
Build the ingestion, cleaning and feature engineering pipeline that feeds the model reliably and repeatably
TIMELINE
Week 2-4
03
Model Development & Training
Experiment across candidate architectures, train and tune the model, and validate against held-out data
TIMELINE
Week 4-7
04
Evaluation & MLOps Integration
Benchmark accuracy, bias and latency; wire up experiment tracking, versioning and CI/CD for the model
TIMELINE
Week 7-9
05
Production Deployment & Monitoring
Ship the model behind an API or batch pipeline with drift detection, retraining triggers and performance dashboards
TIMELINE
Week 9-11
THE ARCHITECT'S TOOLKIT
PyTorch
TensorFlow
scikit-learn
Python
MLflow
Kubernetes
AWS SageMaker
PostgreSQL
Frequently Asked Questions
What's the difference between AI development and ML development?
AI is the broader umbrella; machine learning is one approach within it where a model learns patterns from data rather than following hand-written rules. Most production AI/ML work we do is applied machine learning.
How much data do we need before a custom model is worth building?
It depends on the problem, but most use cases need at least several thousand labeled examples or a reliable proxy signal. We assess this in the scoping phase before committing to a build.
How long does an AI/ML development engagement take?
Most projects go from scoping to a monitored production deployment in 9 to 11 weeks, depending on data readiness and model complexity.