Your plant already generates the data. Learn to turn it into decisions that improve production.
A free executive course for leaders and operations managers who want to apply machine learning with business judgment: which problem to tackle first, with which data, how to measure the impact and how to take models to the production line without getting stuck in an endless pilot.
- Language
- Taught in Spanish
- Format
- 100% online, at your own pace
- Access
- Free
- Duration
- 3 h 52 min
- Content
- 9 modules and 41 lessons
- Level
- Intermediate: no coding needed
- Instructor
- Daniel Medina
What it covers
The plant already generates data: sensors, PLC and SCADA, MES, ERP, lab and maintenance. Even so, decisions are still reactive: failures are fixed afterwards, defects are caught at the end, parameters depend on a veteran operator, reports arrive late and planning lives in spreadsheets.
Who it is for
Intermediate level: no coding needed; knowing the operation and its indicators (OEE, availability, performance, quality and costs) helps.
- Operations, production and plant managers and directors.
- Maintenance, quality and continuous improvement leads.
- Digital transformation and Industry 4.0 leads.
- Industrial, process and manufacturing engineers.
- General managers, boards and investment committees.
- Consultants and industrial project leads.
What you will learn
By the end you will be able to:
- Identify machine learning opportunities in your operation.
- Prioritize use cases by value, feasibility and risk.
- Assess whether the plant data is ready.
- Choose the modeling approach.
- Measure the impact on OEE, cost, quality and return.
- Decide when a model is ready for production.
- Design the model’s deployment, monitoring and governance.
- Lead adoption on the plant floor.
- Build an executive roadmap.
Why AI initiatives fail in industry
They are management failures, not technology failures.
- The wrong use case is chosen.
- The data is not ready.
- There is no success metric.
- There is no plan to take the model to production.
- There is no model governance.
- Operators do not trust the result.
What machine learning can do in your operation
- Predictive maintenance: anticipate the failure before the line stops.
- Predictive quality: catch the defect during the process, not at the end.
- Process parameter optimization: turn an operator’s experience into a measurable rule.
- Planning and demand forecasting: move beyond the spreadsheet.
- Energy and resource efficiency: use less per unit produced.
- Inspection and anomaly detection: spot what is out of the ordinary in time.
Syllabus
9 modules and 41 lessons.
| Module | Lessons |
|---|---|
| 1. The industrial opportunity | 1 |
| 2. Priority use cases | 3 |
| 3. Plant data | 5 |
| 4. Models for production | 6 |
| 5. Measurement and decision | 9 |
| 6. Operational optimization | 7 |
| 7. Industrialization and control | 5 |
| 8. Governance and adoption | 4 |
| 9. Executive roadmap | 1 |
What you take away
- A method to prioritize use cases.
- A data readiness checklist.
- Criteria to evaluate vendor proposals.
- A framework to measure return.
- A model governance framework.
- An executive roadmap.
- A common language across operations, IT and data.
Skills
- Industrial machine learning
- Production optimization
- Predictive maintenance
- Predictive quality
- Plant data
- Model evaluation
- Operational deployment
- Model governance
- AI roadmap
What this course is not
- It is not a programming course.
- It is not academic theory.
- It is not a sales pitch.
- It is not a promise of magic.
What makes it different
- Management focus: business decisions, not code.
- From pilot to production: how to take the model to the line.
- Business metrics: OEE, cost, quality and return.
- A concrete result: an executive roadmap for your plant.
- Taught from practice: AI applied in real projects.
Free
You get free access to the 9 modules and 41 lessons, at your own pace. It is a CUANTICO initiative to bring applied artificial intelligence closer to industry in Colombia and Latin America.
Instructor
Daniel Medina is co-founder, CEO and Chief Scientific Officer of CUANTICO and directs CuantaIA, a research group recognized by MinCiencias, Colombia's Ministry of Science, Technology and Innovation.
He is a Ph.D. candidate in Computer Science. He takes AI from the lab to operations: from intelligent reporting systems for industry to mineral traceability platforms, with researchers in Colombia, the United States and Spain.
Frequently asked questions
Do I need to code?
No.
How long does it take?
3 h 52 min. With 30 to 40 minutes a day, you finish it in a week.
Is it useful if my plant has no sensors?
Yes. Module 3 helps you diagnose which data you have and what is missing.
Which industries is it for?
Food and beverages, chemicals, plastics, metalworking, textiles, pharmaceuticals and construction materials.
Is there a certificate?
No.
How much does it cost?
It is free.
Start today, for free
The course is free, online and at your own pace.