DATA UNIVERSEDescripción detallada del cursoDetailed course description
Contenido en inglés, el idioma en que se imparte el curso.Content in English, the language the course is taught in.
This course gives you a clear, non‑fluffy roadmap to understand and implement MLOps, so that machine learning projects stop dying in notebooks and start delivering real value in production. It is designed for both technical and non‑technical profiles: data scientists, engineers, product managers, and business leaders who need a shared language around ML in production.
We start by defining what MLOps is in 2026 and why it has become essential. You will see how MLOps closes the gap between model development and production, and how it differs from traditional DevOps: data dependency, model decay, experimentation at scale, probabilistic testing, and the added complexity of data and model versioning.
Then we walk through the full ML lifecycle from a production point of view: data preparation pipelines (ingestion, validation, cleaning, transformation), experiment tracking and model training, deployment strategies (batch vs real‑time, canary, blue‑green, A/B), and continuous monitoring with automated retraining.
Qué aprenderás en este cursoWhat you'll learn in this course
A quién va dirigidoWho this course is for
- Analytics professionals and aspiring MLOps practitioners looking for a structured, business‑friendly overview before diving into deeply technical tools and code
- Curious learners from any background who want to understand how modern AI systems are deployed, monitored, and governed in real companies, beyond simple demos and prototypes
- Data scientists and ML practitioners who want a clearer view of how their models move from notebooks into reliable, monitored production systems
- University students, bootcamp graduates, and junior professionals who know the basics of ML or data and want a structured, real‑world introduction to MLOps without heavy math or coding
- Product managers, tech leads, and engineering managers who must make decisions about MLOps investments, roadmaps, and trade‑offs between speed, risk, and cost.
- Software, data, and ML engineers who need to understand MLOps concepts, architectures, and platforms to support large‑scale ML deployments
Requisitos previosPrerequisites
- No formal ML or coding background is required; the course is designed for business and technical audiences who want to understand MLOps at a practical, high level
- Basic familiarity with what machine learning is (e.g., what a “model” or “prediction” means) will help, but key concepts are briefly recapped
- Access to a computer with internet and a modern browser to follow along with platform examples and optional hands‑on explorations
Programa completo del cursoFull course curriculum
Contenido en inglés, el idioma en que se imparte el curso.Content in English, the language the course is taught in.
01WHAT IS MLOPS AND WHY NOW3 claseslectures · 18m
- The MLOps Revolution 20267m
- DevOps vs. MLOps - Key Differences7m
- Business Impact Metrics4m
02THE ML LIFECYCLE4 claseslectures · 18m
- Data Preparation Pipeline4m
- Model Training and Validation4m
- Deployment Strategies5m
- Continuous Monitoring and Retraining5m
03MLOPS PIPELINES AND AUTOMATION4 claseslectures · 21m
- End-to-End Pipeline Architecture5m
- CI/CD for Machine Learning5m
- Cloud-Native MLOps Platforms5m
- Real-Time Inference Pipelines5m
04METRICS AND SUCCESS MEASUREMENT4 claseslectures · 19m
- Technical Success Metrics (Accuracy, Latency, Drift)5m
- Business Success Metrics (ROI, Cost Savings)5m
- SLA and Uptime Standards5m
- Model Governance KPIs5m
05TEAM ROLES AND ORGANIZATION4 claseslectures · 20m
- MLOps Team Structure5m
- Data Scientists vs. ML Engineers5m
- Business Stakeholder Roles5m
- Cross-Functional Collaboration5m
06REAL-WORLD CASE STUDIES4 claseslectures · 23m
- E-commerce Recommendation System5m
- Fraud Detection Pipeline6m
- Customer Churn Prediction6m
- Lessons Learned Summary5m
07GETTING STARTED IN 20264 claseslectures · 21m
- Vendor Selection Matrix4m
- Implementation Roadmap5m
- Budget and ROI Calculator6m
- Next Steps Action Plan6m
08BONUS: AI Governance Toolkit1 claseslectures · 2m
- BONUS: AI Governance Toolkit2m
Preguntas frecuentes sobre este cursoFrequently asked questions about this course
¿En qué idioma está el curso?What language is the course in?
Se imparte íntegramente en inglés. Todo el material del curso está en ese idioma. Puedes comprobar en Udemy si hay subtítulos disponibles para tu idioma.It is taught entirely in English. All course material is in that language. You can check on Udemy whether subtitles are available for your language.
¿Qué nivel se necesita?What level is required?
Está catalogado como todos los niveles. Los requisitos publicados por el instructor están en la pestaña de visión general, en el idioma del curso.It is listed as all levels. The requirements published by the instructor are in the overview tab, in the course language.
¿Cuánto dura y qué incluye?How long is it and what is included?
2h 20m de vídeo en 28 clases repartidas en 8 secciones (sin tests), más el certificado de finalización de Udemy.2h 20m of video across 28 lectures in 8 sections (no tests), plus the Udemy certificate of completion.
¿Cuánto tiempo tengo acceso?How long do I have access?
Acceso de por vida en Udemy, con las actualizaciones incluidas y garantía de devolución de 30 días según las políticas de Udemy.Lifetime access on Udemy, with updates included and a 30-day refund guarantee under Udemy policies.
¿Se puede contratar para un equipo o empresa?Can it be arranged for a team or company?
Sí. Impartimos formación in-company partiendo de este contenido, adaptada al sector y al nivel del equipo. Usa el botón de solicitar formación y te contamos opciones.Yes. We deliver in-company training based on this content, adapted to your sector and your team level. Use the request training button and we will walk you through the options.
Valoración de los alumnosStudent ratings
Las opiniones se gestionan y verifican en la plataforma Udemy. Puedes leerlas todas en la página del curso:Reviews are managed and verified on the Udemy platform. You can read them all on the course page: ver opiniones en Udemy →see reviews on Udemy →