Sahithi Mandapuri
Member since 2024
Diamond League
41500 points
Prepare Data for ML APIs on Google Cloud
Earned أبريل 2, 2025 EDT
[DEPRECATED] Feature Engineering
Earned مارس 27, 2025 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned مارس 27, 2025 EDT
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI
Earned مارس 27, 2025 EDT
MLOps with Agent Platform: Manage Features
Earned مارس 27, 2025 EDT
Create ML Models with BigQuery ML
Earned مارس 23, 2025 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud
Earned مارس 23, 2025 EDT
Production Machine Learning Systems
Earned مارس 23, 2025 EDT
Working with Notebooks in Vertex AI
Earned مارس 22, 2025 EDT
Create Generative AI Apps on Google Cloud
Earned مارس 22, 2025 EDT
Responsible AI for Developers: Privacy & Safety
Earned مارس 22, 2025 EDT
Responsible AI for Developers: Interpretability & Transparency
Earned مارس 22, 2025 EDT
Responsible AI for Developers: Fairness & Bias
Earned مارس 21, 2025 EDT
Machine Learning Operations (MLOps): Getting Started
Earned مارس 21, 2025 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned مارس 21, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned مارس 21, 2025 EDT
Introduction to Large Language Models
Earned مارس 21, 2025 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned مارس 12, 2025 EDT
Build a Certification Study Guide: PMLE
Earned يناير 28, 2025 EST
Level 3: Google Cloud Adventures
Earned أكتوبر 22, 2024 EDT
Deploy and Secure Serverless APIs with API Gateway
Earned أكتوبر 20, 2024 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned أكتوبر 19, 2024 EDT
Implement Speech and Language Solutions with Pre-trained APIs
Earned أكتوبر 19, 2024 EDT
Implement Cloud Collaboration and Productivity Workflows
Earned أكتوبر 19, 2024 EDT
Prompt Design in Agent Platform
Earned أكتوبر 19, 2024 EDT
Analyze Images with the Cloud Vision API
Earned أكتوبر 19, 2024 EDT
Implement Event-Driven Messaging and Automation Workflows
Earned أكتوبر 18, 2024 EDT
DEPRECATED Networking Fundamentals on Google Cloud
Earned أكتوبر 17, 2024 EDT
Monitoring in Google Cloud
Earned أكتوبر 17, 2024 EDT
Deploy and Manage Applications on Google App Engine
Earned أكتوبر 16, 2024 EDT
Build Serverless Applications with Cloud Run Functions
Earned أكتوبر 16, 2024 EDT
[Deprecated] Get Started with Looker
Earned أكتوبر 16, 2024 EDT
Organize and Govern Data with Knowledge Catalog
Earned أكتوبر 15, 2024 EDT
Implement Cloud Storage and Data Protection Solutions
Earned أكتوبر 7, 2024 EDT
The Basics of Google Cloud Compute
Earned أكتوبر 7, 2024 EDT
Introduction to Generative AI
Earned مايو 9, 2024 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 Managed Service for Apache Spark, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
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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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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; and building machine learning models using BigQuery ML.
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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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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 Agent Platform Feature Store's streaming ingestion at the SDK layer.
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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 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 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 is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
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Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud?
In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application. Explore other content in the Gemini Agent Ready (GEAR) program.
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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 introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.
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This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud products and open source tools.
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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 equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Agent Platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Gemini Enterprise Agent Platform empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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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. Explore other content in the Gemini Agent Ready (GEAR) program.
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This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.
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Your next Google Cloud Credential is within reach! Complete a series of hands-on labs (adventures!) that will help you build key Google Cloud Skills. You’ll cover topics like Compute, Storage, Networking, IAM, Kubernetes, BigQuery, and more, working through real-world scenarios to boost your understanding. No prior experience required!
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Earn a skill badge by completing the Deploy and Secure Serverless APIs with API Gateway skill badge course, where you learn how to use API Gateway to deploy, secure, and manage APIs with a fully managed gateway.
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Complete the intermediate Develop Gen AI Apps with Gemini and Streamlit skill badge course to demonstrate skills in text generation, applying function calls with the Python SDK and Gemini API, and deploying a Streamlit application with Cloud Run. In this course, you learn Gemini prompting, test Streamlit apps in Cloud Shell, and deploy them as Docker containers in Cloud Run. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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Earn the Introductory Skill Badge by completing the Implement Speech and Language Solutions with Pre-trained APIs course, where you learn how to use speech related API tools to synthesise and transcribe speech.
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Earn an introductory skill badge by completing the Implement Cloud Collaboration and Productivity Workflows course, where you will get introduced to Google's collaborative platform and learn to use Gmail, Calendar, Meet, Drive, Sheets, and AppSheet.
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Complete the introductory Prompt Design in Agent Platform skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Agent Platform. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
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Earn a skill badge by completing the Analyze Images with the Cloud Vision API quest, where you discover how to leverage the Cloud Vision API for various tasks, including extracting text from images.
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Earn a skill badge by completing the Implement Event-Driven Messaging and Automation Workflows skill badge course, where you learn how to use Pub/Sub through the Cloud console, how Cloud Scheduler jobs can save you effort, and when Pub/Sub Lite can save you money on high-volume event ingestion.
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Earn a skill badge by completing the Networking Fundamentals on Google Cloud quest, where you learn how to work with VPC networks and load balancers on Google Cloud. 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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Complete the introductory Monitoring in Google Cloud skill badge course to demonstrate skills in the following: using Cloud Monitoring tools to monitor resources on Google Cloud.
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Earn a Skill Badge by completing the Deploy and Manage Applications on Google App Engine course, where you learn how to use App Engine with Python, Go, and PHP.
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Earn a Introductory skill badge by completing the Build Serverless Applications with Cloud Run Functions course, where you learn how to use Cloud Run functions through the Google Cloud console and on the command line.
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Earn a skill badge by completing the Get Started with Looker skill badge course, where you learn how to analyze, visualize, and curate data using Looker Studio and Looker.
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Complete the introductory Organize and Govern Data with Knowledge Catalog skill badge to demonstrate skills in the following: creating Knowledge Catalog assets, creating aspect types, and applying aspects to entries in Knowledge Catalog.
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Earn a Skill Badge by completing the Implement Cloud Storage and Data Protection Solutions course. Learn to create a Cloud Storage bucket, use the command line, and protect objects with Bucket Lock.
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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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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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