AI Division
Demand, credit and churn models, with the reasons behind each number.
Some problems need a trained model built on your own records. We build forecasting, scoring, classification and computer vision models on your own data, and we show what drives each prediction so your team knows when to trust it.
In short
What does Machine Learning Models from Code Colonies cover?
Forecasting, scoring, classification and computer vision on your own data. Deliverables include data readiness report; trained and tested models; notes on what drives each model; deployment on your cloud or servers. The work is delivered under 2good.dev at USD 2 or INR 199 an hour, a 150 hour minimum, no advance and payment after you accept the work.
- How does the work run?
- Assess (1 to 2 weeks), pilot (2 to 3 weeks), production (rollout) and managed operations (monthly).
- Which tools and standards are used?
- Python, scikit-learn, XGBoost, PyTorch, Databricks and Microsoft Fabric, chosen to fit your systems.
- Who owns the result?
- You do. Once you accept and pay, the documents, code and intellectual property from the work are yours.
- How do I start?
- Book a call or send a short brief. A specialist replies within one working day.
When clients call us
Situations we are asked to resolve
- Stock planning is based on last year's sales and instinct.
- You want lead, credit or churn decisions made the same way every time.
- Quality on the shop floor depends on people inspecting by eye.
- You have years of sales and operations data that nobody uses.
Scope
What the work covers
- Data readiness review
- We look at what data you have, how clean it is and what it can reliably support.
- Forecasting
- Demand, cash flow, staffing and capacity forecasts, with realistic error ranges.
- Scoring and classification
- Lead, credit, risk and churn scores, showing the factors behind each result.
- Computer vision
- Spotting defects, counting items and sorting documents and images.
- Model operations
- Version control, drift monitoring and retraining on a schedule.
- Decision dashboards
- Results shown where people already work, in a dashboard or inside your ERP.
How it runs
A clear path, with gates
- Step 11 to 2 weeksAssessReview the data, define what to predict and agree how accuracy is judged.
- Step 22 to 3 weeksPilotBuild the model and test it against your own history.
- Step 3RolloutProductionDeploy it, connect it to your systems and train the users.
- Step 4MonthlyManaged operationsWatch for drift, retrain and report on accuracy.
Deliverables
What you receive, and what changes
What you receive
- Data readiness report
- Trained and tested models
- Notes on what drives each model
- Deployment on your cloud or servers
- Drift monitoring and a retraining schedule
What changes
- Planning based on better forecasts
- Scoring that is consistent
- Problems caught earlier
- A clear reason behind each prediction
- Typical stack
- Pythonscikit-learnXGBoostPyTorchDatabricksMicrosoft FabricdbtAirflowVertex AIAWS SageMaker
Start here
Talk to us about machine learning models.
Tell us what you are trying to achieve. A specialist replies within one working day, and the first conversation is free.
Questions
How much data do we need?
It depends on the problem. A demand forecast usually needs two years of history or more. If your data is not enough yet, we say so in the assessment and tell you what to start collecting.
Can the model run on our own servers?
Yes. We often deploy models on client infrastructure, private cloud tenants or on premise hardware.