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Jest członkiem od 2022

Liga srebrna

1160 pkt.
Certification Learning Path: Professional Cloud DevOps Engineer Earned lut 26, 2024 EST
Google Cloud Fundamentals: Core Infrastructure - Polski Earned lis 6, 2022 EST
Preparing for Your Associate Cloud Engineer Journey Earned lis 4, 2022 EDT
Machine Learning in the Enterprise Earned paź 28, 2022 EDT
Feature Engineering Earned paź 14, 2022 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned sie 31, 2022 EDT
How Google Does Machine Learning Earned sie 21, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned sie 7, 2022 EDT

Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud DevOps Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.

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Szkolenie Google Cloud Fundamentals: Core Infrastructure wprowadza ważne pojęcia i terminologię potrzebne w pracy z Google Cloud. Za pomocą filmów i praktycznych modułów szkolenie prezentuje oraz porównuje usługi Google Cloud umożliwiające między innymi przetwarzanie i przechowywanie danych, a także zawiera ważne materiały i narzędzia do zarządzania zasadami.

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This course helps you structure your preparation for the Associate Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.

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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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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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