Prepare Data for ML APIs on Google Cloud
Earned Nov 25, 2023 EST
Google Cloud Fundamentals: Core Infrastructure
Earned Nov 19, 2023 EST
Cloud Functions: 3 Ways
Earned Nov 19, 2023 EST
BigQuery Fundamentals for Redshift Professionals
Earned Oct 14, 2023 EDT
Set Up an App Dev Environment on Google Cloud
Earned Oct 8, 2023 EDT
Optimize Costs for Google Kubernetes Engine
Earned Oct 7, 2023 EDT
Implement Cloud Security Fundamentals on Google Cloud
Earned Sep 23, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned Sep 17, 2023 EDT
Logging and Monitoring in Google Cloud
Earned Sep 16, 2023 EDT
Generative AI Explorer - Vertex AI
Earned Sep 2, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process
Earned Aug 26, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned Aug 26, 2023 EDT
Launching into Machine Learning
Earned Jun 27, 2023 EDT
How Google Does Machine Learning
Earned Jun 14, 2023 EDT
Introduction to Vertex AI Studio
Earned Jun 9, 2023 EDT
Create Image Captioning Models
Earned Jun 9, 2023 EDT
Transformer Models and BERT Model
Earned Jun 9, 2023 EDT
Attention Mechanism
Earned Jun 9, 2023 EDT
Encoder-Decoder Architecture
Earned Jun 8, 2023 EDT
Introduction to Image Generation
Earned Jun 8, 2023 EDT
Generative AI Fundamentals
Earned Jun 8, 2023 EDT
Introduction to Responsible AI
Earned Jun 8, 2023 EDT
Introduction to Large Language Models
Earned Jun 8, 2023 EDT
Introduction to Generative AI
Earned Jun 8, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Jun 1, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 31, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned May 27, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned May 26, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned May 25, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned May 24, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned May 23, 2023 EDT
Build Google Cloud Infrastructure for AWS Professionals
Earned May 21, 2023 EDT
Deploy and Monitor in Google Cloud for AWS Professionals
Earned May 21, 2023 EDT
Google Cloud Storage and Containers for AWS Professionals
Earned May 21, 2023 EDT
Google Cloud Compute and Scalability for AWS Professionals
Earned May 20, 2023 EDT
Google Cloud IAM and Networking for AWS Professionals
Earned May 19, 2023 EDT
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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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.
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Earn a skill badge by completing the Cloud Functions: 3 Ways course, where you learn how to use Cloud Functions (including 2nd gen) through the Google Cloud console and on the command line. 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 covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Redshift, you also learn about similarities and differences between Redshift and BigQuery to help you get started with data warehouses in BigQuery. After this course, you can continue your BigQuery journey by completing the skill badge quest titled Build and Optimize Data Warehouses with BigQuery.
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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub. 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 skill badge that you can share with your network.
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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge to demonstrate skills in the following: creating and managing multi-tenant clusters, monitoring resource usage by namespace, configuring cluster and pod autoscaling for efficiency, setting up load balancing for optimal resource distribution, and implementing liveness and readiness probes to ensure application health and cost-effectiveness. 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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Complete the intermediate Implement Cloud Security Fundamentals on Google Cloud skill badge to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster. 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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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
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This course teaches participants techniques for monitoring and improving infrastructure and application performance in Google Cloud.
Using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.
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The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.
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This course equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine courses and assumes hands-on experience with the technologies covered in either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to define and balance business and technical requirements to design Google Cloud deployments that are highly reliable, highly available, secure, and cost-effective.
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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you.
In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.
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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 Vertex AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models so you can use their capabilities in your applications. In this course, you learn what Vertex AI Studio is, its features and options, and how to use it by walking through demos of the product. In the end, you will have a quiz to test your knowledge.
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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.
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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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This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 7 AI principles.
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This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.
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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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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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Earn a skill badge by completing the Build Google Cloud Infrastructure for AWS Professionals course, where you learn how to configure IAM permission, orchestrate workloads using Kubernetes, host a web application using compute engine, and configure load balancing. 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 is the fourth course of a four-course series for cloud architects and engineers with existing AWS knowledge. It compares Google Cloud and AWS solutions and guides professionals on their use.
This course focuses on deploying and monitoring applications in Google Cloud. The learners apply the knowledge of monitoring and application deployment processes in AWS to explore the differences with Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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This is the third course of a four-course series for cloud architects and engineers with existing AWS knowledge, and it compares Google Cloud and AWS solutions and guides professionals on their use.
This course focuses on Storage Options and containers in Google Cloud. The learners apply the knowledge of storage and containers in AWS to explore the similarities and differences with storage and containers in Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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This is the second course of a four-course series for cloud architects and engineers with existing AWS knowledge. It aims to compare Google Cloud and AWS solutions and guide professionals on their use.
This course focuses on compute resources and load balancing in Google Cloud. The learner will apply the knowledge of using virtual machines and load balancers in AWS to explore the similarities and differences with configuring and managing compute resources and load balancers in Google Cloud. Learners will get hands-on practice building and managing Google Cloud resources.
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This is the first course of a four-course series for cloud architects and engineers with existing AWS knowledge, and it compares Google Cloud and AWS solutions and guides professionals on their use.
This course focuses on Identity and Access Management (IAM) and networking in Google Cloud. The learners apply the knowledge of access management and networking in AWS to explore the similarities and differences with access management and networking in Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
Prepare Data for ML APIs on Google Cloud
Earned Nov 25, 2023 EST
Google Cloud Fundamentals: Core Infrastructure
Earned Nov 19, 2023 EST
Cloud Functions: 3 Ways
Earned Nov 19, 2023 EST
BigQuery Fundamentals for Redshift Professionals
Earned Oct 14, 2023 EDT
Set Up an App Dev Environment on Google Cloud
Earned Oct 8, 2023 EDT
Optimize Costs for Google Kubernetes Engine
Earned Oct 7, 2023 EDT
Implement Cloud Security Fundamentals on Google Cloud
Earned Sep 23, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned Sep 17, 2023 EDT
Logging and Monitoring in Google Cloud
Earned Sep 16, 2023 EDT
Generative AI Explorer - Vertex AI
Earned Sep 2, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process
Earned Aug 26, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned Aug 26, 2023 EDT
Launching into Machine Learning
Earned Jun 27, 2023 EDT
How Google Does Machine Learning
Earned Jun 14, 2023 EDT
Introduction to Vertex AI Studio
Earned Jun 9, 2023 EDT
Create Image Captioning Models
Earned Jun 9, 2023 EDT
Transformer Models and BERT Model
Earned Jun 9, 2023 EDT
Attention Mechanism
Earned Jun 9, 2023 EDT
Encoder-Decoder Architecture
Earned Jun 8, 2023 EDT
Introduction to Image Generation
Earned Jun 8, 2023 EDT
Generative AI Fundamentals
Earned Jun 8, 2023 EDT
Introduction to Responsible AI
Earned Jun 8, 2023 EDT
Introduction to Large Language Models
Earned Jun 8, 2023 EDT
Introduction to Generative AI
Earned Jun 8, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Jun 1, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 31, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned May 27, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned May 26, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned May 25, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned May 24, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned May 23, 2023 EDT
Build Google Cloud Infrastructure for AWS Professionals
Earned May 21, 2023 EDT
Deploy and Monitor in Google Cloud for AWS Professionals
Earned May 21, 2023 EDT
Google Cloud Storage and Containers for AWS Professionals
Earned May 21, 2023 EDT
Google Cloud Compute and Scalability for AWS Professionals
Earned May 20, 2023 EDT
Google Cloud IAM and Networking for AWS Professionals
Earned May 19, 2023 EDT