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Jeena Shaji

Member since 2022

Gold League

7145 points
Detect Manufacturing Defects Using Visual Inspection AI Earned אוג 1, 2025 EDT
Create ML Models with BigQuery ML Earned מאי 28, 2025 EDT
Preparing for your Professional Data Engineer Journey Earned ספט 11, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned דצמ 28, 2022 EST
Implementing Cloud Load Balancing for Compute Engine Earned אוק 13, 2022 EDT
Build Streaming Data Pipelines on Google Cloud Earned ספט 26, 2022 EDT
Build Batch Data Pipelines on Google Cloud Earned אוג 22, 2022 EDT
Migration Summit 2022 Earned אוג 9, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned יול 17, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned יול 5, 2022 EDT

Earn a skill badge by completing the Detect Manufacturing Defects using Visual Inspection AI course, where you learn how to use Visual Inspection AI to deploy a solution artifact and test that it can successfully identify defects in a manufacturing process.

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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

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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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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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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.

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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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The GSI Migration Summit is a curated collection of hands-on labs and quests that provide coverage of application migration. Security and analytics labs and quests have been added to provide coverage of key platform concepts and services.

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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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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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