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Akshay Attri

Member since 2022

Bronze League

20159 points
Reinforcement Learning with Human Feedback (RLHF) Earned Sep 4, 2025 EDT
Improve Performance by Fine-Tuning Foundation Models Earned Sep 4, 2025 EDT
Model evaluation on Vertex AI Earned Sep 1, 2025 EDT
Enterprise Search with Grounding Earned May 8, 2024 EDT
Machine Learning Operations (MLOps): Getting Started Earned May 3, 2024 EDT
Create Image Captioning Models Earned Apr 29, 2024 EDT
Attention Mechanism Earned Apr 20, 2024 EDT
Application Development with Cloud Run Earned Mar 29, 2024 EDT
Build Custom Processors with Document AI Earned Mar 12, 2024 EDT
Deploy Kubernetes Applications on Google Cloud Earned Mar 8, 2024 EST
Develop Serverless Apps with Firebase Earned Mar 7, 2024 EST
Develop Serverless Applications on Cloud Run Earned Mar 7, 2024 EST
App Deployment, Debugging, and Performance Earned Mar 1, 2024 EST

RHLF is a technique for fine-tuning language models by incorporating human feedback into the training process. This course explores how you can use RHLF to improve the performance of language models on various tasks, such as text summarization and question answering.

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Model tuning is an effective way to customize large models to your tasks. It's a key step to improve the model's quality and efficiency. Model tuning provides benefits such as higher quality results for your specific tasks and increased model robustness. You learn some of the tuning options available in Vertex AI and when to use them.

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This course delves into the complexities of assessing the quality of large language model outputs. It examines the challenges enterprises face due to the subjective and sometimes incorrect nature of LLM responses, including hallucinations and inconsistent results. The course introduces various evaluation metrics for different tasks like classification, text generation, and question answering, such as Accuracy, Precision, Recall, F1 score, ROUGE, BLEU, and Exact Match. It also explores evaluation methods offered by Vertex AI LLM Evaluation Services, including computation-based, autorater, and human evaluation, providing insights into their application and benefits. Finally, the module covers how to unit test LLM applications within Vertex AI.

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This course equips students with the knowledge and skills to leverage advanced search engine functionalities beyond basic keyword queries. Through exploring adapters, grounding techniques, and the capabilities of powerful language models, participants will learn to design and implement effective solutions for improved search quality, information relevance, and contextual understanding.

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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 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 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 introduces you to fundamentals, practices, capabilities and tools applicable to modern cloud-native application development using Google Cloud Run. Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to on Google Cloud using Cloud Run.design, implement, deploy, secure, manage, and scale applications

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Earn a skill badge by completing the Build Custom Processors with Document AI course. You learn how to extract data and classify documents by creating custom ML models specific to your business needs. This course teaches the foundation skills of building your own processors, working with optical character recognition, form parsing, processor creation, and uptraining the DocumentAI model.

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Complete the intermediate Deploy Kubernetes Applications on Google Cloud skill badge to demonstrate skills in the following: configuring and building Docker container images, creating and managing Google Kubernetes Engine (GKE) clusters, utilizing kubectl for efficient cluster management, and deploying Kubernetes applications with robust continuous delivery (CD) practices. 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 Develop Serverless Apps with Firebase skill badge to demonstrate skills in the following: architecting and building serverless web applications with Firebase, utilizing Firestore for database management, automating deployment processes using Cloud Build, and integrating Google Assistant functionality into your applications. 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 Develop Serverless Applications on Cloud Run skill badge to demonstrate skills in the following: integrating Cloud Run with Cloud Storage for data management, architecting resilient asynchronous systems using Cloud Run and Pub/Sub, constructing REST API gateways powered by Cloud Run, and building and deploying services on Cloud Run. 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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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance. Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.

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