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Naveen Chandragiri Poornachandra

Menjadi anggota sejak 2022

Gold League

38965 poin
Orchestrate LLM solutions with LangChain Earned Des 19, 2024 EST
Search with AI Applications Earned Des 11, 2024 EST
Custom Search with Embeddings in Vertex AI Earned Des 4, 2024 EST
Penelusuran Vektor dan Embedding Earned Des 3, 2024 EST
Orchestrating Gen AI Applications with LangChain Earned Des 1, 2024 EST
Security Best Practices in Google Cloud Earned Nov 27, 2024 EST
Infrastruktur Google Cloud Elastis: Penskalaan dan Otomatisasi Earned Nov 25, 2024 EST
Infrastruktur Google Cloud yang Penting: Layanan Inti Earned Nov 23, 2024 EST
Pengantar Responsible AI Earned Nov 21, 2024 EST
Generative AI Fundamentals Earned Nov 21, 2024 EST
Infrastruktur Google Cloud yang Dapat Diandalkan: Desain dan Proses Earned Nov 20, 2024 EST
Membangun Infrastruktur dengan Terraform di Google Cloud Earned Nov 16, 2024 EST
Menyiapkan Jaringan Google Cloud Earned Nov 16, 2024 EST
Preparing for your Professional Cloud Architect Journey Earned Nov 15, 2024 EST
Mengembangkan Jaringan Google Cloud Anda Earned Nov 15, 2024 EST
Pengantar Vertex AI Studio Earned Nov 12, 2024 EST
Build Custom Processors with Document AI Earned Nov 12, 2024 EST
Document AI: Building a Custom Document Extractor Earned Nov 11, 2024 EST
Menginspeksi Dokumen Multimedia dengan Multimodalitas Gemini dan RAG Multimodal Earned Nov 10, 2024 EST
Data Warehousing for Partners: Analyze Data with Looker Earned Nov 10, 2024 EST
Build and Deploy a Generative AI solution using a RAG framework Earned Okt 11, 2024 EDT
Text Prompt Engineering Techniques Earned Okt 9, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned Okt 9, 2024 EDT
Production Machine Learning Systems Earned Jul 21, 2023 EDT
Machine Learning Operations (MLOps): Getting Started Earned Jul 20, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Jul 16, 2023 EDT
Launching into Machine Learning Earned Jul 13, 2023 EDT
How Google Does Machine Learning Earned Jul 8, 2023 EDT
Feature Engineering Earned Jul 4, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Jun 25, 2023 EDT

Learn to use LangChain to call Google Cloud LLMs and Generative AI Services and Datastores to simplify complex applications' code.

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(Previously named "Developing apps with Vertex AI Agent Builder: Search". Please note there maybe instances in this course where previous product names and titles are used) Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities. This course teaches you to use AI Applications to integrate enterprise-grade generative AI search.

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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search. You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.

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Menjelajahi teknologi, alat, dan aplikasi penelusuran yang didukung AI dalam kursus ini. Mempelajari penelusuran semantik dengan memanfaatkan embedding vektor, penelusuran campuran yang menggabungkan pendekatan semantik dan kata kunci, serta Retrieval-Augmented Generation (RAG) yang meminimalkan halusinasi AI sebagai agen AI yang di-grounding. Mendapatkan pengalaman praktis dengan Vertex AI Vector Search untuk membangun mesin telusur yang cerdas.

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This course equips full-stack mobile and web developers with the skills to integrate generative AI features into their applications using LangChain. You'll learn how to leverage LangChain’s capabilities for backend flows and seamless model execution, all within the familiar environment of Python. The course guides you through the entire process, from prototyping to production, ensuring a smooth journey in building next-generation AI-powered applications.

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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Storage access control technologies, Security Keys, Customer-Supplied Encryption Keys, API access controls, scoping, shielded VMs, encryption, and signed URLs. It also covers securing Kubernetes environments.

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Kursus akselerasi sesuai permintaan ini memperkenalkan peserta pada infrastruktur dan layanan platform yang komprehensif dan fleksibel yang disediakan oleh Google Cloud. Melalui kombinasi video materi edukasi, demo, dan lab interaktif, peserta akan mengeksplorasi dan men-deploy berbagai elemen solusi, termasuk membuat interkoneksi jaringan yang aman, load balancing, penskalaan otomatis, otomatisasi infrastruktur, serta layanan terkelola.

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Kursus akselerasi sesuai permintaan ini memperkenalkan peserta pada infrastruktur dan layanan platform yang komprehensif dan fleksibel yang disediakan oleh Google Cloud, dengan fokus pada Compute Engine. Melalui kombinasi video materi edukasi, demo, dan lab praktis, peserta akan mengeksplorasi dan men-deploy berbagai elemen solusi, termasuk komponen infrastruktur seperti jaringan, sistem, dan layanan aplikasi. Kursus ini juga membahas cara men-deploy solusi praktis termasuk kunci enkripsi yang disediakan pelanggan, pengelolaan keamanan dan akses, kuota dan penagihan, serta pemantauan resource.

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Ini adalah kursus pengantar pembelajaran mikro yang dimaksudkan untuk menjelaskan responsible AI, alasan pentingnya responsible AI, dan cara Google mengimplementasikan responsible AI dalam produknya. Kursus ini juga memperkenalkan 7 prinsip AI Google.

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Earn a skill badge 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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Kursus ini membekali peserta dengan keterampilan untuk membangun solusi yang sangat andal dan efisien di Google Cloud menggunakan pola desain yang telah terbukti. Kursus ini merupakan kelanjutan dari kursus Membangun dengan Google Compute Engine atau Membangun dengan Google Kubernetes Engine dan memberikan pengalaman interaktif dengan teknologi yang dibahas dalam kursus tersebut. Melalui kombinasi presentasi, aktivitas desain, dan lab interaktif, peserta akan mempelajari cara menentukan serta menyeimbangkan kebutuhan bisnis dan teknis untuk merancang deployment Google Cloud yang sangat andal, sangat tersedia, aman, dan hemat biaya.

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Selesaikan badge keahlian Membangun Infrastruktur dengan Terraform di Google Cloud tingkat menengah untuk menunjukkan keterampilan dalam hal berikut: Prinsip Infrastruktur sebagai Kode (IaC) menggunakan Terraform, penyediaan dan pengelolaan resource Google Cloud dengan konfigurasi Terraform, pengelolaan status yang efektif (lokal dan jarak jauh), serta modularisasi kode Terraform agar dapat digunakan kembali dan diatur. Badge keahlian akan memvalidasi pengetahuan praktis Anda terkait produk tertentu melalui lab interaktif dan penilaian tantangan. Dapatkan badge dengan menyelesaikan kursus atau langsung ikuti Challenge Lab untuk mendapatkan badge Anda hari ini. Badge membuktikan kemahiran Anda, meningkatkan profil profesional Anda, dan pada akhirnya membantu meningkatkan peluang karier Anda. Kunjungi profil Anda untuk memantau badge yang telah Anda peroleh.

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Dapatkan badge keahlian dengan menyelesaikan kursus Menyiapkan Jaringan Google Cloud, untuk mempelajari cara menjalankan tugas-tugas networking dasar di Google Cloud Platform, yakni membuat jaringan kustom, menambahkan aturan firewall subnet, lalu membuat VM dan menguji latensi saat VM berkomunikasi satu sama lain. Badge keahlian adalah badge digital eksklusif yang diberikan oleh Google Cloud sebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud, serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan praktik yang interaktif. Selesaikan badge keahlian ini dan penilaian akhir challenge lab untuk menerima badge digital yang dapat Anda bagikan dengan jaringan Anda.

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This course helps learners create a study plan for the PCA (Professional Cloud Architect) 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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Dapatkan badge keahlian dengan menyelesaikan kursus Mengembangkan Jaringan Google Cloud Anda yang berisi pelajaran tentang berbagai cara untuk men-deploy dan memantau aplikasi, termasuk cara: menjelajahi peran IAM dan menambahkan/menghapus akses project, membuat jaringan VPC, men-deploy dan memantau VM Compute Engine, menulis kueri SQL, men-deploy dan memantau VM di Compute Engine, serta men-deploy aplikasi menggunakan Kubernetes dengan beberapa pendekatan deployment. Badge keahlian adalah badge digital eksklusif yang diberikan oleh Google Cloud sebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan praktik yang interaktif. Selesaikan badge keahlian ini dan challenge lab penilaian akhir untuk menerima badge keahlian yang dapat Anda bagikan ke jaringan Anda.

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Kursus ini memperkenalkan Vertex AI Studio, sebuah alat untuk berinteraksi dengan model AI generatif, membuat prototipe ide bisnis, dan meluncurkannya ke dalam produksi. Melalui kasus penggunaan yang imersif, pelajaran menarik, dan lab interaktif, Anda akan menjelajahi siklus proses dari perintah ke produk dan mempelajari cara memanfaatkan Vertex AI Studio untuk aplikasi multimodal Gemini, desain perintah, rekayasa perintah, dan tuning model. Tujuan kursus ini adalah agar Anda dapat memanfaatkan potensi AI generatif dalam project Anda dengan Vertex AI Studio.

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This skill badge course is designed to offer hands-on experience through labs, enabling participants to master Document AI for document processing and extraction tasks. By the end of the course, participants will be proficient in creating and testing Document AI processors, customizing document extraction using Document AI Workbench, and building custom processors to tackle real-world document processing challenges.

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This workload aims to upskill Google Cloud partners to perform specific tasks associated with building a Custom Doc Extractor using the Google Cloud AI solution. The following will be addressed: Service: Document AI Task: Extract fields Processors: Custom Document Extractor and Document Splitter Prediction: Using Endpoint to programmatically extract fields

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Menyelesaikan badge keahlian tingkat menengah Menginspeksi Dokumen Multimedia dengan Multimodalitas Gemini dan RAG Multimodal untuk menunjukkan keterampilan dalam hal berikut ini: menggunakan prompt multimodal untuk mengekstrak informasi dari data teks dan visual dengan menghasilkan deskripsi video, dan mengambil informasi tambahan di luar video menggunakan multimodalitas dengan Gemini; membangun metadata dokumen yang berisi teks dan gambar dengan mendapatkan semua potongan teks yang relevan, dan mencetak kutipan dengan menggunakan Multimodal Retrieval Augmented Generation (RAG) dengan Gemini. Badge keahlian adalah badge digital eksklusif yang diberikan oleh Google Cloud sebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan praktis yang interaktif. Selesaikan kursus badge keahlian ini dan challenge lab penilaian akhir untuk menerima badge keahlian yang dapat Anda bagikan ke jarin…

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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.

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Demonstrate your ability to implement updated prompt engineering techniques and utilize several of Gemini's key capacilities including multimodal understanding and function calling. Then integrate generative AI into a RAG application deployed to Cloud Run. This course contains labs that are to be used as a test environment. They are deployed to test your understanding as a learner with a limited scope. These technologies can be used with fewer limitations in a real world environment.

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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.

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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.

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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 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 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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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 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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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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