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Bintang Siahaan

Member since 2025

Gold League

19470 points
Advanced ML: ML Infrastructure Earned فبراير 12, 2025 EST
Intermediate ML: TensorFlow on Google Cloud Earned فبراير 12, 2025 EST
Work with Gemini Models in BigQuery Earned فبراير 11, 2025 EST
Responsible AI for Developers: Privacy & Safety Earned فبراير 11, 2025 EST
Workspace: Add-ons Earned فبراير 11, 2025 EST
Gemini for Data Scientists and Analysts Earned فبراير 11, 2025 EST
Boost Productivity with Gemini in BigQuery Earned فبراير 10, 2025 EST
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI Earned فبراير 9, 2025 EST
Secure Software Delivery Earned فبراير 7, 2025 EST
Automate Data Capture at Scale with Document AI Earned فبراير 7, 2025 EST
Secure BigLake Data Earned فبراير 6, 2025 EST
Get Started with Sensitive Data Protection Earned فبراير 6, 2025 EST
The Basics of Google Cloud Compute Earned فبراير 6, 2025 EST
Cloud Speech API: 3 Ways Earned فبراير 6, 2025 EST
Build Real World AI Applications with Gemini and Imagen Earned فبراير 5, 2025 EST
Prompt Design in Vertex AI Earned فبراير 4, 2025 EST

Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on Google Cloud.

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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.

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This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.

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This course introduces important topics of AI privacy and safety. It explores practical methods and tools to implement AI privacy and safety recommended practices through the use of Google Cloud products and open-source tools.

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This course demonstrates the power of integrating Google Cloud services and tools with Workspace applications - like using Node.js to build a survey bot, the Natural Language API to recognize sentiment in a Google Doc, and building a chat bot with Apps Script.

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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows. Duet AI was renamed to Gemini, our next-generation model.

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This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.

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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.

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Complete the intermediate Secure Software Delivery skill badge to demonstrate your proficiency in proactively integrating security into the software development lifecycle (SDLC) with DevSecOps principles. You'll learn how to utilize Google Kubernetes Engine (GKE) and Cloud Run for secure container image deployment, implement automated vulnerability scanning to proactively identify risks, and streamline application development with Artifact Registry while maintaining a focus on security. Additionally, you'll gain skills in integrating Cloud Build for robust development processes and implementing Admission Control Policies for fine-grained control over your environment.

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Earn the introductory skill badge by completing the Automate Data Capture at Scale with Document AI course. In this course, you learn how to extract, process, and capture data using Document AI.

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Complete the introductory Secure BigLake Data skill badge course to demonstrate skills with IAM, BigQuery, BigLake, and Dataplex to create and secure BigLake tables.

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Complete the introductory Get Started with Sensitive Data Protection skill badge course to demonstrate skills in the following: using Sensitive Data Protection services (including the Cloud Data Loss Prevention API) to inspect, redact, and de-identify sensitive data in Google Cloud.

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Earn a skill badge by completing the The Basics of Google Cloud Compute skill badge course, where you learn how to work with virtual machines (VMs), persistent disks, and web servers using Compute Engine.

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Earn the Introductory skill badge by completing the Cloud Speech API: 3 Ways course, where you learn how to use speech related API tools to synthesise and transcribe speech.

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Complete the introductory Build Real World AI Applications with Gemini and Imagen skill badge to demonstrate skills in the following: image recognition, natural language processing, image generation using Google's powerful Gemini and Imagen models, deploying applications on the Vertex AI platform.

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Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.

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