Teilnehmen Anmelden

Armielyn Obinguar

Mitglied seit 2022

Modernize Infrastructure and Applications with Google Cloud Earned Okt 28, 2022 EDT
Exploring Data Transformation with Google Cloud Earned Okt 28, 2022 EDT
Digital Transformation with Google Cloud Earned Okt 27, 2022 EDT
Recommendation Systems on Google Cloud Earned Aug 26, 2022 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Aug 25, 2022 EDT
Natural Language Processing on Google Cloud Earned Aug 25, 2022 EDT
Machine Learning in the Enterprise Earned Aug 24, 2022 EDT
Production Machine Learning Systems Earned Aug 23, 2022 EDT
Launching into Machine Learning Earned Aug 23, 2022 EDT
End-to-End Machine Learning with TensorFlow on Google Cloud Earned Aug 15, 2022 EDT
How Google Does Machine Learning Earned Aug 9, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Aug 8, 2022 EDT

Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

Weitere Informationen

Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences. “Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

Weitere Informationen

There's much excitement about cloud technology and digital transformation, but often many unanswered questions. For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin? If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you. This course is part of the Cloud Digital Leader learning path.

Weitere Informationen

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.

Weitere Informationen

This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

Weitere Informationen

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.

Weitere Informationen

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 best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

Weitere Informationen

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, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

Weitere Informationen

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. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

Weitere Informationen

One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. It involves building an end-to-end model from data exploration all the way to deploying an ML model and getting predictions from it. This is the first course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Production Machine Learning Systems course.

Weitere Informationen

This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.

Weitere Informationen

This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Weitere Informationen