Nitin Gopala Krishna Sontineni
Mitglied seit 2023
Production Machine Learning Systems
Earned Jul 13, 2023 EDT
Generative AI Fundamentals
Earned Jul 13, 2023 EDT
Einführung in die verantwortungsbewusste Anwendung von KI
Earned Jul 13, 2023 EDT
Einführung in Large Language Models
Earned Jul 13, 2023 EDT
Machine Learning in the Enterprise
Earned Jul 12, 2023 EDT
[DEPRECATED] Feature Engineering
Earned Jul 5, 2023 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Jun 27, 2023 EDT
Einführung in generative KI
Earned Jun 23, 2023 EDT
Launching into Machine Learning
Earned Jun 20, 2023 EDT
How Google Does Machine Learning
Earned Jun 16, 2023 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned Apr 30, 2023 EDT
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.
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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. By passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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In diesem Einführungskurs im Microlearning-Format wird erklärt, was verantwortungsbewusste Anwendung von KI bedeutet, warum sie wichtig ist und wie Google dies in seinen Produkten berücksichtigt. Darüber hinaus werden die 7 KI-Grundsätze von Google behandelt.
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In diesem Einführungskurs im Microlearning-Format wird untersucht, was Large Language Models (LLM) sind, für welche Anwendungsfälle sie genutzt werden können und wie die LLM-Leistung durch Feinabstimmung von Prompts gesteigert werden kann. Darüber hinaus werden Tools von Google behandelt, die das Entwickeln eigener Anwendungen basierend auf generativer KI ermöglichen.
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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.
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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 engineering using BigQuery ML, Keras, and TensorFlow.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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In diesem Einführungskurs im Microlearning-Format wird erklärt, was generative KI ist, wie sie genutzt wird und wie sie sich von herkömmlichen Methoden für Machine Learning unterscheidet. Darüber hinaus werden Tools von Google behandelt, mit denen Sie eigene generative KI-Anwendungen entwickeln können.
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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.
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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.
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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.
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