Sanjeev Sarda
Menjadi anggota sejak 2025
Diamond League
46415 poin
Google DeepMind: Melatih Model Bahasa Kecil
Earned Apr 11, 2026 EDT
Google DeepMind: 07 Accelerate Your Model
Earned Apr 10, 2026 EDT
Google DeepMind: 05 Fine-Tune Your Model
Earned Apr 10, 2026 EDT
Google DeepMind: 04 Discover The Transformer Architecture
Earned Apr 10, 2026 EDT
Google DeepMind: 03 Design And Train Neural Networks
Earned Apr 8, 2026 EDT
Google DeepMind: 02 Represent Your Language Data
Earned Apr 8, 2026 EDT
Google DeepMind: 01 Build Your Own Small Language Model
Earned Apr 8, 2026 EDT
How to Use TPUs for Inference
Earned Apr 7, 2026 EDT
Architecting an AI Inference Stack
Earned Apr 7, 2026 EDT
Men-deploy Agen Pertama Anda
Earned Mar 28, 2026 EDT
[DEPRECATED] Orchestrate Complex Multi-Agent Workflows
Earned Mar 28, 2026 EDT
Coordinate Multiple Agents
Earned Mar 28, 2026 EDT
Hybrid Cloud Multi-Cluster with Anthos
Earned Feb 15, 2025 EST
Hybrid Cloud Service Mesh with Anthos
Earned Feb 9, 2025 EST
Hybrid Cloud Infrastructure Foundations with Anthos
Earned Feb 8, 2025 EST
Mengoptimalkan Biaya untuk Google Kubernetes Engine
Earned Feb 7, 2025 EST
Men-deploy Aplikasi Kubernetes di Google Cloud
Earned Feb 6, 2025 EST
Mengimplementasikan Cloud Load Balancing untuk Compute Engine
Earned Feb 1, 2025 EST
Architecting with Google Kubernetes Engine: Production
Earned Jan 31, 2025 EST
Architecting with Google Kubernetes Engine: Workloads
Earned Jan 25, 2025 EST
Architecting with Google Kubernetes Engine: Foundations
Earned Jan 19, 2025 EST
Dasar-Dasar Google Cloud: Infrastruktur Inti
Earned Jan 18, 2025 EST
Dapatkan badge keahlian tingkat lanjut Google DeepMind: Melatih Model Bahasa Kecil dengan menyelesaikan kursus ini untuk menunjukkan keahlian Anda dalam hal berikut: merumuskan masalah riset model bahasa di dunia nyata; membangun tokenizer sederhana; menyiapkan set data untuk melatih model bahasa transformer; menjalankan loop pelatihan model bahasa kecil.Akses lab ini tanpa biaya dengan mendaftar ke langganan gratis. Dapatkan 35 kredit gratis setiap bulan.
Pelajari lebih lanjut
Train more powerful models with a single GPU. In this course, you will learn how hardware can speed up model training and the key considerations when training models on a GPU. First, you will learn how to estimate the number of computations and the amount of computer memory required to train large neural networks. You will then discover techniques for reducing the computing and memory requirements when training a model. Techniques which you will apply for fine-tuning a Gemma model with 4 billion parameters. Finally, you will consider the potential environmental impacts of machine learning, with a focus on where questions of energy, water, and e-waste intersect with justice and equity.
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Unleash the power of language models with fine-tuning. In this course, you will learn how to adjust a pre-trained model to a specific task. You will start with full-parameter fine-tuning using a small language model. To tune larger models like Gemma, you will learn parameter-efficient techniques with a focus on LoRA. Finally, you will be briefly introduced to reinforcement learning as an alternative to supervised fine-tuning (SFT). You will also explore how AI is imagined and made sense of in cultural contexts. You will consider why responsible AI is not just about technical safety but also about building governance systems that reflect community values and protect the public interest.
Pelajari lebih lanjut
In this Google DeepMind course you will discover the mechanisms of the transformer architecture. You will investigate how transformer language models process prompts to make context-sensitive next-token predictions. Through practical activities you will explore the attention mechanism, visualize attention weights, and encounter advanced concepts like masked attention and multi-head attention. You will also learn other techniques that are necessary to build neural networks that are well-suited to be used as language models. Finally, through activities on values, stakeholder mapping and community engagement, you will practice concrete tools for ensuring AI projects are developed with communities, not just for them.
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In this Google DeepMind course you will focus on the training process for machine learning models. You will learn how to spot and mitigate issues when training a model, such as overfitting and underfitting. In practical coding labs, you will implement and evaluate the multilayer perceptron for simple classification tasks. This will provide insights into the mechanics of training a neural network model and the backpropagation algorithm. Research case studies will demonstrate how neural networks power real-world models. Additionally, you will consider the broader social impacts of innovation by looking beyond immediate benefits to anticipate potential risks, safety concerns, and further-reaching societal consequences.
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In this Google DeepMind course you will learn how to prepare text data for language models to process. You will investigate the tools and techniques used to prepare, structure, and represent text data for language models, with a focus on tokenization and embeddings. You will be encouraged to think critically about the decisions behind data preparation, and what biases within the data may be introduced into models. You will analyze trade-offs, learn how to work with vectors and matrices, how meaning is represented in language models. Finally, you will practice designing a dataset ethically using the Data Cards process, ensuring transparency, accountability, and respect for community values in AI development.
Pelajari lebih lanjut
In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.
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This course is for developers interested in learning how to use TPUs for inference—from architecture to deployment, and how to solve common implementation challenges.
Pelajari lebih lanjut
This course is designed for developers looking to build an optimized AI inference stack on Google Cloud. Whether you’re working with GPUs or TPUs, you’ll explore the fundamental components of an inference stack, learn design principles for maximizing performance and reliability, and explore practical techniques to take your workloads from 0 to 1.
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Kursus ini mengajarkan cara men-deploy agen ADK ke Vertex AI Agent Engine dan Cloud Run, dengan memori lintas sesi opsional melalui Memory Bank.
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Learn to orchestrate complex multi-agent workflows. This lesson teaches you to choose the right workflow patterns, manage state across agents, understand when custom logic is needed, and introduces distributed agent systems with A2A Protocol.
Pelajari lebih lanjut
Learn to coordinate multiple specialized agents working together. This lesson teaches you when to use multi-agent systems, how to orchestrate agents with workflow patterns, and how agents communicate through shared state. By the end, you’ll build a complete multi-agent application.
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This on-demand course equips students to understand, configure, and maintain
multi-cluster Kubernetes infrastructures using Anthos GKE, and Istio-based
service mesh, whether deployed with Anthos on Google Cloud or with Anthos
deployed on VMware.
This is the third, and final, course of the Architecting Hybrid Cloud
Infrastructure with Anthos series.
Completion of the Architecting with Google Kubernetes Engine path is a
prerequisite for this course.
Pelajari lebih lanjut
This on-demand course equips students to understand and adopt Istio-based
service-mesh with Anthos for centralized observability, traffic management,
and service-level security.
This is the second course of the Architecting Hybrid Cloud Infrastructure with
Anthos series. After completing this course, learners should continue to the
Hybrid Cloud Multi-Cluster with Anthos course.
Completion of the Architecting with Google Kubernetes Engine path is a
prerequisite for this course.
Pelajari lebih lanjut
Welcome to Hybrid Cloud Infrastructure Foundations with Anthos!
This is the first course of the Architecting Hybrid Cloud Infrastructure with Anthos path. Anthos enables you to build and manage modern applications, and gives you the freedom to choose where to run them. Anthos gives you one consistent experience in both your on-premises and cloud environments.
During this course, you will be presented with modules that will take you through skills that you will use as an architect or administrator running Anthos environments. The modules in this course include videos, hands-on labs, and links to helpful documentation.
Pelajari lebih lanjut
Selesaikan badge keahlian tingkat menengah Mengoptimalkan Biaya untuk Google Kubernetes Engine untuk menunjukkan keterampilan dalam hal berikut: membuat dan mengelola cluster multi-tenant, memantau penggunaan resource berdasarkan namespace, mengonfigurasi penskalaan otomatis pada cluster dan pod untuk tujuan efisiensi, menyiapkan load balancing untuk mengoptimalkan distribusi resource, dan menerapkan pemeriksaan keaktifan serta kesiapan untuk memastikan kondisi aplikasi dan efektivitas biaya.
Pelajari lebih lanjut
Selesaikan badge keahlian Men-deploy Aplikasi Kubernetes di Google Cloud tingkat menengah untuk menunjukkan keterampilan dalam hal berikut ini: mengonfigurasi dan membangun image container Docker, membuat dan mengelola cluster Google Kubernetes Engine (GKE), memanfaatkan kubectl untuk pengelolaan cluster yang efisien, dan men-deploy aplikasi Kubernetes dengan praktik continuous delivery (CD) yang andal.
Pelajari lebih lanjut
Selesaikan badge keahlian pengantar Mengimplementasikan Cloud Load Balancing untuk Compute Engine untuk menunjukkan keterampilan dalam hal berikut: membuat dan men-deploy virtual machine di Compute Engine serta mengonfigurasi load balancer aplikasi dan jaringan.
Pelajari lebih lanjut
In this course, you'll learn about Kubernetes and Google Kubernetes Engine (GKE) security; logging and monitoring; and using Google Cloud managed storage and database services from within GKE.
This is the second course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Reliable Google Cloud Infrastructure: Design and Process course or the Hybrid Cloud Infrastructure Foundations with Anthos course.
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In "Architecting with Google Kubernetes Engine- Workloads", you'll embark on a comprehensive journey into cloud-native application development. Throughout the learning experience, you'll explore Kubernetes operations, deployment management, GKE networking, and persistent storage. This is the first course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Architecting with Google Kubernetes Engine- Production course.
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In this course, "Architecting with Google Kubernetes Engine: Foundations," you get a review of the layout and principles of Google Cloud, followed by an introduction to creating and managing software containers and an introduction to the architecture of Kubernetes.
This is the first course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Architecting with Google Kubernetes Engine: Workloads course.
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Dasar-Dasar Google Cloud: Infrastruktur Inti memperkenalkan konsep dan terminologi penting untuk bekerja dengan Google Cloud. Melalui video dan lab interaktif, kursus ini menyajikan dan membandingkan banyak layanan komputasi dan penyimpanan Google Cloud, bersama dengan resource penting dan alat pengelolaan kebijakan.
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