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Source: https://www.cloudskillsboost.google/paths/17
Machine Learning Engineer Learning Path
A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems.
person: Managed by Google Cloud
A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems. This learning path guides you through a curated collection of on-demand courses, labs, and skill badges that provide you with real-world, hands-on experience using Google Cloud technologies essential to the ML Engineer role. Once you complete the path, check out the Google Cloud Machine Learning Engineer certification to take the next steps in your professional journey.
01 - A Tour of Google Cloud Hands-on Labs
In this first hands-on lab you will access the Google Cloud console and use these basic Google Cloud features: Projects, Resources, IAM Users, Roles, Permissions, and APIs.
02 - Introduction to AI and Machine Learning on Google Cloud
This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML...
03 - Launching into Machine Learning
The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code....
04 - TensorFlow on Google Cloud
This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.
05 - Feature Engineering
This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature...
06 - Machine Learning in the Enterprise
This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the...
07 - Production Machine Learning Systems
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training,...
08 - Computer Vision Fundamentals with Google Cloud
This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building...
09 - Natural Language Processing on Google Cloud
This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
10 - Recommendation Systems on Google Cloud
In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
11 - Machine Learning Operations (MLOps): Getting Started
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine...
12 - Machine Learning Operations (MLOps) with Vertex AI: Manage Features
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Learners...
13 - ML Pipelines on Google Cloud
access_time: 13 hours 15 minutes
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production...
14 - Prepare Data for ML APIs on Google Cloud
access_time: 6 hours 30 minutes
Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and...
15 - Build and Deploy Machine Learning Solutions on Vertex AI
access_time: 8 hours 15 minutes
Earn a skill badge by completing the Build and Deploy Machine Learning Solutions with Vertex AI course, where you will learn how to use Google Cloud's unified Vertex AI platform and its AutoML and custom training services to train, evaluate,...
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