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Operationalizing ML Models: MLOps for Scalable AI

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Operationalizing ML Models: MLOps for Scalable AI

Instructeurs : Starweaver

Inclus avec

1 module

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

1 module

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

4 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Implement scalable MLOps workflows that ensure efficient and reliable machine learning operations.
Build CI/CD pipelines for seamless and automated model updates, streamlining the development lifecycle.
Monitor deployed ML models for performance and drift.
Optimize AI infrastructure to handle scalability challenges and support high-performance deployments.

Compétences que vous acquerrez

Catégorie : Continuous Monitoring
Catégorie : Model Optimization
Catégorie : Scalability
Catégorie : Model Training
Catégorie : MLOps (Machine Learning Operations)
Catégorie : DevOps
Catégorie : Containerization
Catégorie : Devops Tools
Catégorie : Continuous Deployment
Catégorie : CI/CD

Outils que vous découvrirez

Catégorie : Kubernetes
Catégorie : AI Workflows
Catégorie : Model Deployment
Catégorie : Docker (Software)

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

1 devoir

Enseigné en Anglais

91%

of learners achieved a positive career outcome

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Il y a un module dans ce cours

In this course you’ll explore how to turn promising ML prototypes into robust, scalable, and maintainable systems that deliver real value. Through hands-on demos, practical tools, and real-world case studies from companies like Netflix, Uber, and Google, you’ll gain a comprehensive understanding of what it takes to run ML systems effectively in production using MLOps.

This course is designed for data scientists, machine learning engineers, AI practitioners, and IT professionals who want to operationalize machine learning workflows, scale AI systems, and streamline deployment and infrastructure management. To get the most out of this course, learners should have a basic understanding of machine learning concepts, be familiar with Python programming, and have experience using Docker and containerization technologies. By the end of this course, learners will be able to operationalize machine learning models by designing scalable MLOps workflows, automating deployments with CI/CD pipelines, monitoring performance and detecting data drift, and optimizing AI infrastructure using tools like Docker, MLflow, and Kubernetes to support robust, real-world AI applications.

Détails du module

In this course, you’ll explore how to turn promising ML prototypes into robust, scalable, and maintainable systems that deliver real value. Through hands-on demos, practical tools, and real-world case studies from companies like Netflix, Uber, and Google, you’ll gain a comprehensive understanding of what it takes to run ML systems effectively in production using MLOps.

Inclus

11 vidéos7 lectures1 devoir1 évaluation par les pairs2 sujets de discussion

11 vidéosTotal 81 minutes

Introduction and Welcome 4 minutes
What is MLOps?6 minutes
Key Components of MLOps 8 minutes
Building Your First MLOps Pipeline with Docker and MLflow 12 minutes
Introduction to CI/CD for ML 6 minutes
Designing Effective CI/CD Pipelines 7 minutes
Automating ML Model Deployments with CI/CD 8 minutes
Model Monitoring Techniques 6 minutes
Automating Model Monitoring with Tools 10 minutes
Building Dashboards for ML Model Monitoring 11 minutes
Congratulations and Continuous Learning Journey2 minutes

7 lecturesTotal 50 minutes

Welcome to the Course: Course Overview5 minutes
Hands On Learning (HOL): Deploying and Monitoring ML Models with MLOps10 minutes
Why MLOps Is Critical to The Future Of Your Business5 minutes
Hands On Learning (HOL): Automating ML Model Deployment with CI/CD Pipelines10 minutes
Building Robust CI/CD for ML Systems 5 minutes
Hands On Learning (HOL): Automating Model Monitoring and Performance Tracking10 minutes
The Importance of Model Monitoring5 minutes