모델을 만드는 것에서 끝나지 않고 배포하고 모니터링하는 과정까지 다뤄줘서 실무에 바로 도움이 됐어요.
Foundations of MLOps: Deploy and Monitor Machine Learning Models
Build a solid foundation in machine learning operations by learning how to deploy, monitor, and continuously retrain models in production environments.
-
💬
AI instructor
Ask about any lesson and get a clear answer instantly, anytime. -
🕐
Start anytime
No schedules or deadlines — learn at your own pace, whenever suits you. -
🌐
In English
Lessons, tasks and certificate — all fully in your language.
About this course
Machine learning models only deliver value when they are successfully running in production. However, bridging the gap between a local notebook and a scalable, monitored deployment can be challenging. This course provides a comprehensive introduction to Machine Learning Operations (MLOps). You will start by understanding foundational terminology and lifecycle concepts before progressing to hands-on, text-based exercises. By the end of the program, you will know how to package models, set up continuous integration, and implement monitoring strategies to keep your models accurate over time. What you will learn: Understand foundational MLOps terminology and the machine learning lifecycle. Package and containerize machine learning models for consistent deployment. Configure basic CI/CD pipelines tailored for machine learning workflows. Apply modern model registry patterns to track versions and metadata. Monitor model performance in production to detect data drift and degradation. Design automated retraining loops to keep your models current and accurate. The curriculum flows logically from basic definitions and environment setup to advanced topics like observability and automated retraining. Through clear written explanations and practical code snippets, you will build a robust understanding of production-grade ML systems. This course is designed for beginners, data scientists, and aspiring ML engineers looking to learn deployment skills from scratch, with no prior MLOps experience required. Start building your MLOps foundation today and bring your machine learning models to life.
What you'll get
-
📜
Certificate of completion
Add it to your LinkedIn profile -
💬
Personal AI tutor
Stuck on a lesson? Ask your built-in tutor anything, any time. -
♾️
Lifetime access
Come back anytime, no expiry -
📱
Phone or computer
Works anywhere, any device -
💸
14-day refund
No questions asked -
⚡
Short & focused
2h 42m of practical content
Reviews (15)
Finally connects the dots on MLOps.
Solid intro to real-world MLOps practices.
Malinaw na malinaw ang paliwanag mula sa pag-deploy hanggang sa pag-monitor ng mga machine learning model. Nagustuhan ko yung parte kung paano nila ipinaliwanag ang model drift, dati akala ko complicated ito pero naging simple. Talagang tumulong ito para maintindihan ko ang buong proseso ng MLOps sa totoong trabaho.
モデルを作るところまではできても本番運用となると毎回つまずいていたので、デプロイから監視、再学習までの一連の流れを体系立てて学べたのは大きかったです。特に本番環境でモデルの精度劣化をどう検知するかという話が実務にそのまま役立ちました。書かれている手順に沿って自分のモデルに簡単な監視を組み込むこともできました。再学習の自動化の部分はもう少し踏み込んでほしかったですが、MLOpsの全体像をつかむには十分で、現場志向の良い内容だと思います。
Le module sur le monitoring des modèles en production m'a ouvert les yeux sur des problèmes que je n'avais jamais anticipés.
El curso cubre muy bien todo el flujo de MLOps, desde el despliegue hasta el monitoreo de modelos en producción. Me gustó especialmente la sección sobre cómo detectar cuando un modelo empieza a degradarse con el tiempo. Es de esos cursos que te dan una base sólida para trabajar de verdad con estos sistemas.
デプロイから監視まで実践的で分かりやすい。
Modelleri eğitmeyi biliyordum ama onları üretime alıp canlı tutmak hep karanlık bir alandı, bu eğitim tam da o boşluğu doldurdu. Modeli dağıtmak, performansını izlemek ve zamanla yeniden eğitmek için gereken döngüyü adım adım anlattığı için MLOps mantığı kafamda netleşti. Özellikle model bozulmasını takip etme kısmı çok işime yaradı. Kendi projemde basit bir izleme kurulumunu kurabildim. Sürekli yeniden eğitim otomasyonu biraz daha derin olabilirdi ama yine de sağlam bir temel sundu, tavsiye ederim.
Endlich habe ich verstanden, was nach dem Trainieren eines Modells kommt, denn genau da fängt MLOps ja erst an. Der Kurs führt sauber durch Deployment, Monitoring und das kontinuierliche Nachtrainieren in der Produktion, und alles baut logisch aufeinander auf. Besonders wertvoll fand ich den Teil über das Erkennen von Modell-Drift, weil mir das in der Praxis ständig Probleme gemacht hat. Ich konnte direkt eine einfache Überwachungs-Pipeline für mein eigenes Modell aufsetzen. Auch die Erklärungen zu Retraining-Strategien waren sehr klar und gut nachvollziehbar. Für mich der erste Kurs, der den Produktionsalltag ehrlich abbildet, klare Empfehlung.
O curso mostra bem o ciclo completo de MLOps, desde o deploy até o monitoramento contínuo dos modelos. Gostei muito da parte prática sobre detecção de drift, algo que eu só conhecia de forma teórica antes. Ajudou bastante a organizar as ideias que eu tinha meio soltas sobre esse assunto.
Ce cours m'a vraiment permis de comprendre ce que signifie déployer un modèle en production et pas seulement l'entraîner dans un notebook. Les explications sur le monitoring des modèles sont claires et bien illustrées avec des exemples concrets. J'ai particulièrement aimé la partie sur la détection de dérive des données, un sujet que je trouvais flou avant. Le rythme est progressif, on ne se sent jamais perdu même sans expérience préalable en DevOps. Les exercices pratiques obligent vraiment à mettre les mains dans le cambouis plutôt que de rester passif. C'est le genre de cours qui comble vraiment le fossé entre le machine learning théorique et une vraie pratique en production.
Finalmente un corso che spiega bene come monitorare un modello dopo averlo messo in produzione, non solo come addestrarlo.
Хороший обзор MLOps, но местами не хватает деталей.
Model dağıtımı ve izleme konularını gayet iyi anlatıyor, tek eksik yönü izleme araçlarıyla ilgili biraz daha fazla örnek olabilirdi.
Learners also took
⚡ Best to start
🎓 With certificate
Foundations of Data Science and Modern Analytics
Certificate
Hands-on
₦69,000
→
🔥 In demand
🎓 With certificate
Code-Free Data Science with KNIME
Certificate
Hands-on
₦210,000
→
💼 Job-ready
🎓 With certificate
Foundations of Analytic Combinatorics: Analyzing Algorithms and Data
Certificate
Hands-on
₦69,000
→
🏆 Most popular
🎓 With certificate
Data Science Profession: A Beginner's Guide to Real-World Applications
Certificate
Hands-on
₦69,000
→
Frequently asked
What do I need to take this course? +
Just a phone or computer with internet. No installs, no special hardware.
How do I pay? +
By card via Stripe. We don’t store card details — Stripe handles them securely.
Can I get a refund? +
Yes — full refund within 14 days, no questions asked.
How long will I have access? +
Forever. Once you purchase, the course is yours to revisit anytime.
Will I get a certificate? +
Yes. On completion you'll receive a certificate you can add to your LinkedIn profile.
Built for learners in
Tech
Design
Finance
Marketing
Healthcare
Education
Hospitality
Manufacturing
×2
Top up once, pay half
Add ₦140,000 → get 200 credits, so each course works out to about ₦17,500.00. Credits never expire.
₦140,000
200 credits
₦17,500.00 / course
Best value
₦350,000
550 credits
₦15,909.09 / course
₦690,000
1200 credits
₦14,375.00 / course
No subscription. Credits apply to any course and never expire.