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In diesem Kurs gibt es 1 Modul
This intermediate-level course is designed for machine learning engineers and developers who want to move beyond experiments and ship reliable ML systems. Learners will learn how to apply core MLOps practices such as version control, pull requests, and CI/CD pipelines to keep an ML codebase healthy and production-ready. Learners will also design modular software components and build a FastAPI microservice that serves a transformer model through a clean, well-defined API.
Through short videos, guided coaching conversations, hands-on learning activities, and an ungraded lab, Learners will practice real workflows used by ML teams in industry. By the end of the course, Learners will be able to confidently collaborate on ML codebases, pass automated quality checks, and deploy machine learning models behind scalable APIs.
This intermediate-level course is designed for machine learning engineers and developers who want to move beyond experiments and ship reliable ML systems. Learners will learn how to apply core MLOps practices such as version control, pull requests, and CI/CD pipelines to keep an ML codebase healthy and production-ready. Learners will also design modular software components and build a FastAPI microservice that serves a transformer model through a clean, well-defined API. Through short videos, guided coaching conversations, hands-on learning activities, and an ungraded lab, Learners will practice real workflows used by ML teams in industry. By the end of the course, Learners will be able to confidently collaborate on ML codebases, pass automated quality checks, and deploy machine learning models behind scalable APIs.
Das ist alles enthalten
6 Videos2 Lektüren3 Aufgaben1 Unbewertetes Labor
Infos zu Modulinhalt anzeigen
6 Videos•Insgesamt 26 Minuten
Course Introduction & Welcome •4 Minuten
From Notebook to Production ML•4 Minuten
CI/CD Pipelines and Automated Testing for ML•5 Minuten
From Model Artifact to API Service•4 Minuten
Designing Clean Prediction APIs with FastAPI•5 Minuten
Congratulations and Continuous Learning Journey•3 Minuten
2 Lektüren•Insgesamt 18 Minuten
GitFlow and Pull Requests for ML Teams•10 Minuten
Using Protobuf for ML Inference Requests•8 Minuten
3 Aufgaben•Insgesamt 50 Minuten
Hands-On Activity: Reviewing a Pull Request with CI Checks•15 Minuten
Hands-On Activity: Sketching a /predict API Contract•15 Minuten
Graded Assessment: Production-Ready ML APIs and MLOps •20 Minuten
1 Unbewertetes Labor•Insgesamt 60 Minuten
Build and Validate a Production-Style ML API•60 Minuten
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