2 дня 5:22 Introducing AWS App Studio - Generative AI-Powered Low-Code App Builder | Amazon Web Services
The Fastest and Easiest Way to Build Applications.
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3 дня 3:45 Meta Llama in Amazon Bedrock - Build the future of AI | Amazon Web Services
For over the past decade, Meta has been focused on putting open source tools and capabilities into the hands of developers.
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2 дня 14:16 Implementing a scalable shared framework for RAG based workflows | Amazon Web Services
In this video, you will learn how to implement a scalable and reusable framework for RAG-based workflows.
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13.7
3 дня 4:42 Nomura uses Llama models from Meta in Amazon Bedrock to democratize generative AI
Aniruddh Singh, Nomura's Executive Director and Enterprise Architect, outlines the financial institution’s journey to democratize generative AI firm-wide using Amazon Bedrock and Llama models from
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3 дня 3:39 TaskUs revolutionizes customer experiences using Llama in Amazon Bedrock | Amazon Web Services
TaskUs, a leading provider of outsourced digital services and next-generation customer experience to the world’s most innovative companies, helps its clients represent, protect, and grow their brands.
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13.3
3 дня 6:25 Prudential Financial: Delivering Data Velocity at Scale with AWS Glue and AWS Step Functions
Prudential Financial has built a low-touch, integrated data platform with a range of capabilities that service a large community of technical & non-technical users.
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14.2
3 дня 1:12 Fullstack TypeScript for your Data - AWS Amplify | Amazon Web Services
AWS Amplify brings a fullstack TypeScript developer experience to AWS, built with frontend developers in mind. Host an app frontend globally with CI/CD.
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21.6
4 дня 22:45 Working Backwards to Lead Digital Transformation at a 100-Year-Old Organization
Nobuo Asahi and Dr. Shoji Tanaka of Mitsubishi Electric discuss their journey to drive digital transformation across the 100-year-old manufacturing conglomerate.
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29.1
4 дня 3:59 Future Self – E5 Meet Vilija Vainaite | AWS Scholarship
Learn more about the AWS AI & ML Scholarship program: go.aws/3V9twGy Vilija Vainaite is an AWS AI & ML Scholarship student who is breaking barriers and inspiring change for women leaders in
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14.8
7 часов 3:47 Transforming Manufacturing with AWS: The e-bike smart factory | Amazon Web Services
This video showcases how manufacturers leverage AWS services and partner solutions to drive digital transformation, improve product quality, and achieve operational excellence across their factories
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5.1
1 день 34:22 AWS Innovation with Manchester Airports Group | Innovation Ambassadors
Get a behind-the-scenes look at MAG's journey delivering innovative, human-centered AI solutions for smarter travel in collaboration with the AWS prototyping team.
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3 дня 14:37 [Japanese] Hot and Cold Data in DynamoDB - Amazon DynamoDB Nuggets | Amazon Web Services
Working with Hot and Cold data in Amazon DynamoDB presented by Takahiro Iwase Solutions Architect - AWS.
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16.2
2 дня 29:27 Generative AI Governance on Amazon SageMaker | Amazon Web Services
In this video, you will learn how to use Amazon SageMaker to safeguard and run governance controls for your end-to-end generative AI project.
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2 дня 19:06 Foundation model monitoring on Amazon SageMaker | Amazon Web Services
In this video, you will learn how to monitor LLMs, including prompt injections, toxicity, sentiment, and more, to ensure your model is secure, so you can build your generative AI applications
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2 дня 7:11 Wētā FX's Cloud Burst Rendering is Shaping the Future of VFX
While preparing its visual effects assets for Avatar 2: The Way of Water, Wētā FX maxed out its on-premises render capabilities, putting its delivery deadlines in jeopardy.
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3 дня 5:31 Generative AI Journeys - Fireside Chat with BRIA | Amazon Web Services | Amazon Web Services
Join Swami Sivasubramanian, senior vice president of artificial intelligence and data at AWS, as he talks to Vered Horesh, chief strategic AI partnerships at BRIA, about BRIA's innovative approach
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2 дня 16:12 Pre-training foundation models on Amazon SageMaker | Step 1: Prepare data | Amazon Web Services
Amazon SageMaker helps you reduce the time and cost of training foundation models (FMs) at scale without managing infrastructure.
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2 дня 39:45 Customizing foundation models on Amazon SageMaker | Step 6: Retrieval Augmented Generation (RAG)
To equip the FM with new knowledge not included in the pre-training dataset, you can use RAG, a technique that involves fetching data from company data sources and enriching the prompt with that
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2 дня 23:21 Run inference on Amazon SageMaker | Step 6: Deploying FMs at scale
You need scalable and cost-effective ways to serve hundreds of foundation models. In this video, you will learn how to use SageMaker inference components to deploy multiple models at scale.
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2 дня 13:01 Customizing foundation models on Amazon SageMaker | Step 3: Prompt engineering
Effective prompt engineering is crucial for directing model behavior and achieving desired responses. It helps you to modify the performance of FMs.
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2 дня 11:24 Run inference on Amazon SageMaker | Step 1: Deploy models | Amazon Web Services
Amazon SageMaker makes it easier to deploy FMs to make inference requests at the best price performance for any use case.
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2 дня 31:18 Run inference on Amazon SageMaker | Step 5: Serving hundreds of fine-tuned models
As the demands for personalized and specialized AI solutions grow, organizations are managing hundreds of fine-tuned models tailored to specific customer needs or use cases.
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2 дня 31:35 Run inference on Amazon SageMaker | Step 2: Select the inference option | Amazon Web Services
Amazon SageMaker makes it easier to deploy FMs to make inference requests at the best price performance for any use case.
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2 дня 19:20 Create reapable fine-tuning pipelines on Amazon SageMaker | Amazon Web Services
In this video, you will learn how to create reliable and repeatable fine-tuning pipelines on SageMaker.
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2 дня 23:45 Run inference on Amazon SageMaker | Step 4: Enforcing Responsible AI guradrails
In this video, you will learn how to enforce responsible AI with a safety guard model LlamaGuard and Llama2-7b on Amazon SageMaker using inference components for cost effective and safe deployment.
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2 дня 18:51 Customizing foundation models on Amazon SageMaker | Step 1: Explore models
Amazon SageMaker JumpStart provides access to hundreds of foundation models (FMs). In this video, you will learn how to explore, fine-tune, and deploy foundation models using SageMaker JumpStart.
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2 дня 12:44 Run inference on Amazon SageMaker | Step 3: Optimize model deployment | Amazon Web Services
Amazon SageMaker makes it easier to deploy FMs to make inference requests at the best price performance for any use case.
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2 дня 24:29 Pre-training foundation models on Amazon SageMaker | Step 2: Train model | Amazon Web Services
Amazon SageMaker helps you reduce the time and cost of training foundation models (FMs) at scale without managing infrastructure.
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2 дня 7:26 Increase efficiencies in foundation model experimentation and deployment on Amazon SageMaker
It’s critical to increase efficiencies in foundation model experimentation and deployment to scale model development. In this video, you will learn how to efficiently run prompt management.
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2 дня 19:29 Customizing foundation models on Amazon SageMaker | Step 5: Fine-tune models
In this video, you will learn how to fine-tune foundation models using Amazon SageMaker JumpStart.
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