AI gateways are key to managing operational and security channels that companies face when scaling GenAI apps for public use, ensuring they’re secure, efficient and ethical. Redis seamlessly handles caching, routing, rate limiting, credentials management, guardrails, and PII redaction, and gives AI gateways the speed and flexibility—including fast vector search benchmarks and support for multiple data types—developers need. Read more and connect with our team to chat about AI gateways: https://lnkd.in/gKHezjpD
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Product marketing leader who is driven by empathy for the customer, my team, and continuous improvement with broad experience across customer facing functions and product lifecycle
I’m hosting our demo series this week where I build an AI assistant with RAG. 🤖 I walk through all the components you need to optimize chatbots for performance and enterprise scale, while reducing your costs. 💸 So, who's ready to join the fun? Sign up now and let's make some chatbot magic happen! ✨
Ready to speed up your chatbot? Join Jim Allen Wallace for this week's Redis demo to learn how you can supercharge your chatbot responses while cutting costs. Our semantic caching technique is a game-changer, cutting out unnecessary database queries and pricey operations. Sign up today. https://lnkd.in/g4sVBP2f
Build fast RAG apps with Redis - Redis
https://redis.io
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Character.AI improved user experience through caching by migrating to Memorystore for Redis Cluster eliminating the need for manual sharding and improving scalability. #memorystore #caching #ai #google #googlecloud
Character.AI’s storybook ending with Memorystore for Redis Cluster | Google Cloud Blog
cloud.google.com
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Character.AI utilizes Google Cloud's Memorystore for Redis Cluster to optimize performance, improve scalability, and focus on delivering transformative AI experiences to users worldwide. Check out this Google Cloud Blog post on Character.AI's storybook ending with Memorystore for Redis Cluster:
Character.AI's storybook ending with Memorystore for Redis Cluster
google.smh.re
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Head Of Technology at Amazon Web Services EMEA SARL, Niederlassung Deutschland / Germany Branch
This blog post is excellent food for thought for anyone building LLM-based systems in a multi-tenant environment - think SaaS ISVs, for instance. Well done, Ulrich Hinze & Florian Mair!
Just released a new blog post with Florian Mair on multi-tenant AI assistants. Key takeaways: ❌ Don't rely on an LLM to securely handle user and tenant context. LLMs are probabilistic system components and can be manipulated through user input (i.e. prompt injection). 🔒 Instead, always pass security context between deterministic system components (i.e. classic compute, databases), and only give the LLM access to the specific user and tenant information it needs at that point in time. 🖥️ Walk through the blog post to set up an entire serverless sample application on AWS. This uses Amazon Bedrock agents, AWS Amplify, Amazon AppSync, AWS Lambda, among others. 👩💻 Explore our Github repo for inspiring your own implementation. Blog: https://lnkd.in/ds33A5UZ Repo: https://lnkd.in/dZdjmvEP #aws #genai #security #saas
Implementing tenant isolation using Agents for Amazon Bedrock in a multi-tenant environment | Amazon Web Services
aws.amazon.com
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A leader in personalized AI, Character.AI improves user experience by optimizing its caching layer with Memorystore for Redis Cluster. Read its journey—from initial integration to overcoming scaling hurdles and embracing Memorystore for Redis Cluster → https://goo.gle/4cDE6vq
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A leader in personalized AI, Character.AI improves user experience by optimizing its caching layer with Memorystore for Redis Cluster. Read its journey—from initial integration to overcoming scaling hurdles and embracing Memorystore for Redis Cluster → https://goo.gle/4cDE6vq
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Retrieval augmented generation Implement the RAG pattern using a combination of vCore-based #AzureCosmosDB for #MongoDB, Azure #OpenAI, Azure Functions, and Azure Web Apps. https://lnkd.in/e8evmab9
Best practices and solutions using AI - Azure Cosmos DB
learn.microsoft.com
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Retrieval augmented generation Implement the RAG pattern using a combination of vCore-based #AzureCosmosDB for #MongoDB, Azure #OpenAI, Azure Functions, and Azure Web Apps. https://lnkd.in/eXvqZpYr
Best practices and solutions using AI - Azure Cosmos DB
learn.microsoft.com
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Ockam is a key part of the #AI Infrastructure story. It's simple to understand why.... AI is connected to data and apps. To create these cross-company, cross-network connects quickly, securely, privately, and with integrity....Ockam. Here's an example with #AWS #Bedrock: https://lnkd.in/d82ipZea
Amazon Bedrock | Ockam
docs.ockam.io
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It’s is so good not only Gaming Architecture but also Financial Business so on😀
In this post, we explore how to transition from using Rockset to OpenSearch Service for your DynamoDB use-case effectively. To illustrate this integration, we consider a real-world example of a gaming company that tracks user interactions, such as in-game purchases and player scores, using DynamoDB. This data needs to be analyzed in real time to provide insights into user behavior, detect anomalies, and personalize the gaming experience. #AWS #AmazonWebServices #AWSBlogs #Cloud #CloudComputing #Serverless #DynamoDB
Achieve near real-time analytics with Amazon DynamoDB and zero-ETL for Amazon OpenSearch Service | Amazon Web Services
aws.amazon.com
