ML Pipelines on Google Cloud

Google Cloud via Coursera

Go to Course: https://www.coursera.org/learn/ml-pipelines-google-cloud

Introduction

### Course Review: ML Pipelines on Google Cloud #### Overview If you are passionate about machine learning and are looking to deepen your knowledge of ML pipelines, the course "ML Pipelines on Google Cloud" on Coursera is an excellent choice. Delivered by seasoned ML Engineers and Trainers from Google Cloud, this course is designed to equip you with the skills to manage and orchestrate robust machine learning workflows using state-of-the-art tools and techniques. This course is primarily focused on TensorFlow Extended (TFX), Google’s flagship production machine learning platform that simplifies the process of managing ML pipelines and metadata. Throughout the course, you will gain hands-on experience with pipeline components, orchestration, and CI/CD practices, which are vital for data-driven projects. #### Course Content The course syllabus is structured as follows: 1. **Welcome to ML Pipelines on Google Cloud**: This introductory module sets the stage for the course, providing a concise overview and outline of what you can expect in the following modules. 2. **Introduction to TFX Pipelines**: Here, you’ll learn the fundamental components of TFX, which is essential for understanding how to build effective ML pipelines. 3. **Pipeline Orchestration with TFX**: This module dives deeper into orchestrating your pipelines, allowing you to streamline various machine learning tasks effectively. 4. **Custom Components and CI/CD for TFX Pipelines**: You will learn how to create custom components tailored to your needs and implement Continuous Integration and Continuous Deployment (CI/CD) best practices in your ML workflows. 5. **ML Metadata with TFX**: Understanding ML metadata is crucial for tracking and managing the data and models that flow through your pipelines. 6. **Continuous Training with Multiple SDKs, KubeFlow & AI Platform Pipelines**: This module addresses advanced topics such as continuous training, using various SDKs, and leveraging tools like KubeFlow and AI Platform Pipelines. 7. **Continuous Training with Cloud Composer**: Learn how to orchestrate your workflows using Cloud Composer, facilitating seamless pipeline execution in a scalable manner. 8. **ML Pipelines with MLflow**: Explore the integration of MLflow to manage the ML lifecycle, from tracking experiments to deploying models. 9. **Summary**: The final module recaps the key learnings throughout the course, solidifying your understanding of ML pipelines. #### Review What I appreciate most about this course is the industry-relevant knowledge embedded in each module. The instructors not only cover theoretical aspects but also provide practical insights based on real-world applications. Their experience at Google Cloud adds immense value, as they share best practices and cutting-edge developments in ML pipelines. The course is well-paced for both beginners and those with some prior knowledge in machine learning concepts. Moreover, the emphasis on hands-on learning through practical exercises makes it engaging and applicable, allowing students to grasp complex topics easily. The diversity of tools discussed, such as TFX, KubeFlow, Cloud Composer, and MLflow, ensures that you are equipped with the skills needed to adapt to various scenarios in the rapidly evolving landscape of machine learning. #### Recommendation I highly recommend "ML Pipelines on Google Cloud" to anyone looking to enhance their skills in ML engineering. If you are an aspiring data scientist, machine learning engineer, or even a seasoned professional wanting to update your skills, this course offers essential training in building scalable and efficient ML pipelines. With its comprehensive syllabus, expert instruction, and the focus on practical application, this course will undoubtedly prepare you for real-world challenges in machine learning. Whether you aim to implement ML solutions in a corporate environment or manage personal projects, the insights gained from this course will be invaluable as you navigate the complexities of ML deployment.

Syllabus

Welcome to ML Pipelines on Google Cloud

This module introduces the course and shares the course outline

Introduction to TFX Pipelines

Pipeline orchestration with TFX

Custom components and CI/CD for TFX pipelines

ML Metadata with TFX

Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines

Continuous Training with Cloud Composer

ML Pipelines with MLflow

Summary