Sanjeev Sarda
Miembro desde 2025
Liga de Diamantes
46415 puntos
Google DeepMind: Entrena un modelo de lenguaje pequeño
Earned abr 11, 2026 EDT
Google DeepMind: 07 Accelerate Your Model
Earned abr 10, 2026 EDT
Google DeepMind: 05 Fine-Tune Your Model
Earned abr 10, 2026 EDT
Google DeepMind: 04 Discover The Transformer Architecture
Earned abr 10, 2026 EDT
Google DeepMind: 03 Design And Train Neural Networks
Earned abr 8, 2026 EDT
Google DeepMind: 02 Represent Your Language Data
Earned abr 8, 2026 EDT
Google DeepMind: 01 Build Your Own Small Language Model
Earned abr 8, 2026 EDT
How to Use TPUs for Inference
Earned abr 7, 2026 EDT
Architecting an AI Inference Stack
Earned abr 7, 2026 EDT
Implementa tu primer agente
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
Optimiza los costos de Google Kubernetes Engine
Earned feb 7, 2025 EST
Implementa aplicaciones de Kubernetes en Google Cloud
Earned feb 6, 2025 EST
Implementa Cloud Load Balancing para Compute Engine
Earned feb 1, 2025 EST
Diseño de arquitecturas con Google Kubernetes Engine: Producción
Earned ene 31, 2025 EST
Diseño de arquitecturas con Google Kubernetes Engine: cargas de trabajo
Earned ene 25, 2025 EST
Diseño de arquitecturas con Google Kubernetes Engine: conceptos básicos
Earned ene 19, 2025 EST
Aspectos básicos de Google Cloud: Infraestructura principal
Earned ene 18, 2025 EST
Obtén la insignia de habilidad avanzada Google DeepMind:Entrena un modelo de lenguaje pequeño completando este curso y demuestra tus habilidades para realizar las siguientes actividades:formular problemas de investigación de modelos de lenguaje del mundo real, crear un tokenizador simple, preparar un conjunto de datos para entrenar un modelo de lenguaje de Transformer y ejecutar el bucle de entrenamiento de un modelo de lenguaje pequeñoAccede a este lab sin costo registrándote en la suscripción sin costo. Recibe 35 créditos gratuitos cada mes.
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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.
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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.
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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.
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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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En este curso, aprenderás a implementar agentes de ADK en Vertex AI Agent Engine y Cloud Run.
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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.
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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.
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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.
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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.
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Completa la insignia de habilidad intermedia del curso Optimiza los costos de Google Kubernetes Engine y demuestra tus habilidades para realizar las siguientes actividades: crear y administrar clústeres multiusuario, supervisar el uso de recursos por espacio de nombres, configurar el ajuste de escala automático de clústeres y Pods para mejorar la eficiencia, configurar el balanceo de cargas para optimizar la distribución de recursos y, además, implementar sondeos de funcionamiento y preparación para garantizar el estado y la rentabilidad de la aplicación.
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Obtén la insignia de habilidad intermedia Implementa aplicaciones de Kubernetes en Google Cloud y demuestra tus habilidades para configurar y crear imágenes de contenedores de Docker, crear y administrar clústeres de Google Kubernetes Engine (GKE), utilizar kubectl para la administración eficiente de clústeres y, además, implementar aplicaciones de Kubernetes con prácticas de entrega continua (CD) sólidas.
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Completa la insignia de habilidad introductoria Implementa Cloud Load Balancing para Compute Engine y demuestra tus habilidades para realizar las siguientes actividades: crear y, luego, implementar máquinas virtuales en Compute Engine, y configurar balanceadores de cargas de red y de aplicaciones.
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En este curso, aprenderás sobre la seguridad de Kubernetes y Google Kubernetes Engine (GKE), los registros y la supervisión, y cómo usar los servicios administrados de almacenamiento y de bases de datos de Google Cloud desde GKE.
Este es el segundo curso de la serie Diseño de arquitecturas con Google Kubernetes Engine. Después de completar este curso, inscríbase en los cursos Infraestructura confiable de Google Cloud: El diseño y el proceso o Hybrid Cloud Infrastructure Foundations with Anthos.
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En Diseño de arquitecturas con Google Kubernetes Engine: cargas de trabajo, te embarcarás en un recorrido completo sobre el desarrollo de aplicaciones nativas de la nube. Durante la experiencia de aprendizaje, explorarás las operaciones de Kubernetes, la administración de implementaciones, las herramientas de redes de GKE y el almacenamiento persistente.
Este es el primer curso de la serie Diseño de arquitecturas con Google Kubernetes Engine. Después de completarlo, inscríbete en el curso Diseño de arquitecturas con Google Kubernetes Engine: Producción.
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En el curso, “Diseño de arquitecturas con Google Kubernetes Engine:
conceptos básicos”, obtendrá información sobre el diseño y los principios de
Google Cloud, además de una introducción a la creación y administración de
contenedores de software y a la arquitectura de Kubernetes.
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Aspectos básicos de Google Cloud: Infraestructura principal presenta conceptos y terminología importantes para trabajar con Google Cloud. Mediante videos y labs prácticos, en este curso se presentan y comparan muchos de los servicios de procesamiento y almacenamiento de Google Cloud, junto con importantes recursos y herramientas de administración de políticas.
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