MLOps (Machine Learning Operations) Fundamentals Course Overview

Course Prerequisites

Target Audience for MLOps (Machine Learning Operations) Fundamentals

The MLOps Fundamentals course by Koenig Solutions equips professionals with the skills to improve ML workflows using Google Cloud.

Target audience and job roles for the MLOps (Machine Learning Operations) Fundamentals course:

• Data Scientists seeking to streamline machine learning workflows
• ML Engineers aiming to enhance operational efficiency of ML systems
• DevOps Engineers looking to specialize in MLOps
• IT Professionals interested in the intersection of DevOps and machine learning
• Data Engineers who wish to understand the deployment and management of ML models
• Cloud Architects focusing on building scalable ML infrastructure on Google Cloud
• AI/ML Consultants wanting to advise on best practices for ML operations
• Software Engineers and Developers with a focus on integrating ML into production environments
• Technical Managers overseeing teams that run ML models in production
• Technology Instructors and Trainers aiming to enhance their teaching portfolio in MLOps

Learning Objectives - What you will Learn in this MLOps (Machine Learning Operations) Fundamentals?

Introduction to MLOps Fundamentals Course Learning Outcomes

Gain a comprehensive understanding of MLOps, from foundational principles to practical implementation using Google Cloud technologies, Kubernetes, and AI Platform Pipelines.

Learning Objectives and Outcomes

• Grasp the Need for MLOps: Understand why MLOps is crucial in scaling machine learning (ML) models from research to production environments.
• Identify Data Scientists' Pain Points: Discuss common challenges faced by data scientists without MLOps, such as model deployment and monitoring.
• ML Engineering Challenges: Recognize the unique characteristics and hurdles in ML engineering compared to traditional software development.
• MLOps on Google Cloud: Learn how Google Cloud's services facilitate MLOps practices, enhancing the ML lifecycle.
• DevOps vs. MLOps: Compare the methodologies of DevOps and MLOps, highlighting how MLOps extends DevOps principles to the ML domain.
• Kubernetes and Containers: Understand the role of Docker containers and Kubernetes in creating scalable ML systems, and gain practical skills in managing these components.
• AI Platform Pipelines: Identify the benefits of AI Pipelines, set them up, and create and run ML pipelines to streamline model training and deployment.
• Reproducible ML Workflows: Learn to create reproducible datasets and models, ensuring consistent results across different environments.
• CI/CD for ML: Implement continuous integration and continuous deployment (CI/CD) practices specifically tailored for ML workflows on Kubeflow Pipelines.
• Best Practices in MLOps: Adopt best practices in implementing MLOps to build robust, scalable, and maintainable ML systems.