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Michael Otmar Kaiser

Member since 2020

Badge for Machine Learning Operations (MLOps) with Vertex AI: Manage Features Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned Apr 24, 2024 EDT
Badge for Machine Learning Operations (MLOps): Getting Started Machine Learning Operations (MLOps): Getting Started Earned Apr 24, 2024 EDT
Badge for Computer Vision Fundamentals with Google Cloud Computer Vision Fundamentals with Google Cloud Earned Apr 23, 2024 EDT
Badge for Production Machine Learning Systems Production Machine Learning Systems Earned Apr 16, 2024 EDT
Badge for Machine Learning in the Enterprise Machine Learning in the Enterprise Earned Apr 14, 2024 EDT
Badge for Build and Deploy Machine Learning Solutions on Vertex AI Build and Deploy Machine Learning Solutions on Vertex AI Earned Apr 13, 2024 EDT
Badge for Engineer Data for Predictive Modeling with BigQuery ML Engineer Data for Predictive Modeling with BigQuery ML Earned Apr 11, 2024 EDT
Badge for Get Started with TensorFlow on Google Cloud Get Started with TensorFlow on Google Cloud Earned Apr 8, 2024 EDT
Badge for Feature Engineering Feature Engineering Earned Mar 27, 2024 EDT
Badge for TensorFlow on Google Cloud TensorFlow on Google Cloud Earned Mar 24, 2024 EDT
Badge for BigQuery for Machine Learning BigQuery for Machine Learning Earned Mar 18, 2024 EDT
Badge for Prepare Data for ML APIs on Google Cloud Prepare Data for ML APIs on Google Cloud Earned Mar 17, 2024 EDT
Badge for Launching into Machine Learning Launching into Machine Learning Earned Mar 4, 2024 EST
Badge for Introduction to AI and Machine Learning on Google Cloud Introduction to AI and Machine Learning on Google Cloud Earned Feb 26, 2024 EST

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 will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.

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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 Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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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 custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

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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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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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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, tune, explain, and deploy machine learning solutions. This skill badge course is for professional Data Scientists and Machine Learning Engineers. The datasets and labs are built around high business impact enterprise machine learning use cases; these include retail customer lifetime value prediction, mobile game churn prediction, visual car part defection identification, and fine tuning BERT for review sentiment classification. Learners who complete this skill badge will gain hands-on experience with Vertex AI for new and existing ML workloads and be able to leverage AutoML, custom training, and new MLOps services to significantly enhance development productivity and accelerate time to value. A skill badge is an exclusive digital badge issued b…

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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; building machine learning models using BigQuery ML; and using Cloud Composer to copy data across multiple locations. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.

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Earn a skill badge by completing the Get Started with TensorFlow on Google Cloud quest where you will learn how to use TensorFlow and Vertex AI. This quest will teach you how to build simple machine learning models with TensorFlow while simultaneously walking you through how to leverage Vertex AI to build and deploy your models. You will primarily interact with Vertex AI Workbench user-managed notebooks. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.

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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 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.

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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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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 calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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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 introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.

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