Sandeep Adwankar

One of the more fascinating sessions at #GoogleCloudNext was the advancements in Retrieval Augmented Generation (RAG) for applications. Rather than relying purely on knowledge developed during training, apps equipped with RAG can retrieve information most relevant to a user’s prompt from an external knowledge base (think 10-K filings, public announcements, recent news), then add that information to the prompt before sending it to the generative model. Here's a quickstart article on how to set this up and start exploring the capabilities: https://lnkd.in/dnJeDcyk #GenAI #RAG #AI #GoogleCloud

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Google has big plans for AI agents. At Google Cloud Next ‘24 , Google introduced the Vertex AI Agent Builder, and previewed six AI agents they envision transforming the future of work. Google defines agents as, “Intelligent entities that take actions to achieve goals,” and Google views these agents as the cornerstone of adoption and value creation in the enterprise. Here’s an overview from Google: ***Customer Agents*** Customer agents are able to listen carefully, understand your needs, and recommend the right products and services. They work seamlessly across channels including the web, mobile, and point of sale, and can be integrated into product experiences with voice and video. ***Employee Agents*** Employee agents help workers be more productive and collaborate better together. These agents can streamline processes, manage repetitive tasks, answer employee questions, as well as edit and translate critical communications. ***Creative Agents*** Creative agents can expand your organization with the best design and production skills, working across images, slides, and exploring concepts with workers. With creative agents, anyone can become a designer, artist, or producer. ***Data Agents*** Data agents are like having knowledgeable data analysts and researchers at your fingertips. They can help answer questions about internal and external sources, synthesize research, develop new models, and help find the questions we haven’t even thought to ask yet, and then help get the answers. ***Code Agents*** Code agents are helping developers and product teams to design, create, and operate applications faster and better, and to ramp up on new languages and code bases. ***Security Agents*** Security agents assist security operations by radically increasing the speed of investigations, automating monitoring and response for greater vigilance and compliance controls. They can also help guard data and models from cyberattacks, such as malicious prompt injection. This week’s podcast covers all the Google news, plus hot AI tech (Udio and Captions), an updated GPT model from OpenAI, Adobe training data issues, and some AI legislation updates. 00:02:25 — Google Cloud Next 00:18:43 — Google’s Blockbuster Ad Deal with WPP 00:30:39 — Hot AI Tech Getting Attention - Humane AI Pin’s Brutal Early Feedback 00:37:05 — Hot AI Tech Getting Attention - Udio AI Music Generator 00:41:40 — Hot AI Tech Getting Attention - Captions AI-Powered Creative Studio 00:47:16 — Brands Adding AI Restrictions to Agency Contracts 00:51:35 — AI Legislation Updates 00:55:09 — Google’s Demis Hassabis Chafes Under New AI Push 00:58:27 — Gemini for Google Workspace Prompt Guide 01:02:23 — TikTok Plots Using Virtual Influencers for Advertising 01:04:50 — ChatGPT Is Now “More Direct, Less Verbose” 01:07:40 — Adobe’s ‘Ethical’ Firefly AI Was Trained on Midjourney Images   https://lnkd.in/gVH_V7ZF

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Many #AIfounders have asked me recently to help them navigate the process of acquiring credits for cloud and cloud-based #generativeai services. I've had to admit that I need a refresher myself. So I set out to get some answers. One of the ways I plan to educate myself will be attending AiSalon Phoenix's April event, that will take place tomorrow night, Tuesday April 16th, from 6-9pm, at ASU Skysong Room 201. For #aistartups and #aifounders and builders eager to learn about the many practical ways AWS supports AI companies, the guest speakers at this event will provide a lot of answers. Sergio Ortega, leader in AI / ML at AWS will be on hand to share his deep knowledge of the AWS Machine Learning and Artificial Intelligence stack, Large Language Models (LLM), and Generative AI in general. John "Jack" Blanchette, Sr Account Manager at AWS for Startups will share details about AWS resources available to startups, and in particular address how AWS can support AI startups. We will post key takeaways and video clips at in the past events section on AiSalon.ai for those who want the lowdown. AI founders and builders are encouraged to apply to demo during this or any AISalon event. Demos are informal and low-pressure, designed to help you get valuable feedback, connect and build relationships. Register now: https://lu.ma/i2li1ub3 Ryan Hendrix Colleen Schwab John Rome Rachel Hayden Mike Chipman Camille Campbell Philippos Savvides

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For those interested in how cloud scale database systems work, below is a deep dive on Amazon MemoryDB. MemoryDB delivers microsecond-level read latencies, and throughput in the hundreds of millions of queries per second, while offering multi-AZ durability with automatic failover.

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Google Cloud Vertex AI is rolling out multiple updates to make AI accessible for every enterprise. Up to 85% lower input costs and 80% lower output costs, and 100+ new languages coming to Gemini 1.5. #GoogleCloud #VertexAI #Gemini

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Google Cloud Vertex AI is rolling out multiple updates to make AI accessible for every enterprise. Up to 85% lower input costs and 80% lower output costs, and 100+ new languages coming to Gemini 1.5. #GoogleCloud #VertexAI #Gemini

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Google Cloud Vertex AI is rolling out multiple updates to make AI accessible for every enterprise. Up to 85% lower input costs and 80% lower output costs, and 100+ new languages coming to Gemini 1.5. #GoogleCloud #VertexAI #Gemini

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This is a must see - 5mins of clear articulation of how and why Google Cloud differentiates in #GenAI with an unique approach and strategy relative to competitive #hyperscalers https://lnkd.in/gywMw7Dp

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Google Cloud Vertex AI is rolling out multiple updates to make AI accessible for every enterprise. Up to 85% lower input costs and 80% lower output costs, and 100+ new languages coming to Gemini 1.5. #GoogleCloud #VertexAI #Gemini

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Google Cloud Vertex AI is rolling out multiple updates to make AI accessible for every enterprise. Up to 85% lower input costs and 80% lower output costs, and 100+ new languages coming to Gemini 1.5. #GoogleCloud #VertexAI #Gemini

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➤ (Now GA) Google Distributed Cloud: Air-Gapped Appliance Why is this GA release significant for the media and entertainment industry? What does GDC AGA mean for the future of content production? 🎥 🎬 ☁  Here are 10 ways GDC can be used to accelerate content production and fuel innovation across the media and entertainment industry: ➊ Empower Remote Production - Break free from connectivity constraints and enable processing, editing, and collaboration on content in-studio, from the field, on-set, or in mobile remote units ➋ Real-Time Magic - Unleash AI for live event analysis, enable instant highlights, and offer a next-level viewer experience for audiences around the world ➌ Mobile Studio Powerhouse - Turn any location into a production hub with high-performance computing, data processing, rendering, and AI on the go ➍ Disaster-Proof Your Content - Keep the show going, no matter what with rugged, secure backup and recovery for your most valuable assets ➎ Unlock Immersive Experiences - Power AR/VR content creation and immersive experiences on-site for live events, awards shows, theme parks, theatrical premieres, and beyond ➏ Secure Your Creative Collaborations - Share sensitive scripts, dailies, and intellectual property with confidence ➐ Data Security Fortified - Protect high-value content and intellectual property with the ultimate air-gapped security ➑ Regulatory Compliance Made Easy - Meet strict industry standards with a robust, secure solution ➒ Content Delivery, Anywhere - Reach remote audiences and underserved communities with localized caches ➓ Innovation Unleashed - Explore new frontiers in content creation with on-site data processing and analysis For more info, check out our GDC GA blog post below. #Google #GoogleCloud #AI #innovation #media #entertainment

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At the packed #generativeAI analyst summit in Seattle by #AWS. They are laying out their #genAI vision, roadmap, strategy which is differentiated from many other vendors. Most of the content is under NDA so won’t be able to post here. But one message is very clear and differentiated “𝐓𝐡𝐞𝐫𝐞 𝐢𝐬 𝐧𝐨 𝐨𝐧𝐞 𝐦𝐨𝐝𝐞𝐥 𝐭𝐨 𝐫𝐮𝐥𝐞 𝐭𝐡𝐞𝐦 𝐚𝐥𝐥.” While that is not new, they are doubling down on it! It is good to see many vendors including Amazon Web Services (AWS) discussing security, compliance, risks and other soft, but important, aspects of implementing, productionizing, and scaling up generative #AI.

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Love this new video from NVIDIA, on how OCI + NVIDIA are partnering to support enterprises and simplify generative AI adoption, to accelerate drug discovery, medical imaging, genomics and more! Enterprises can also generate up to 5x higher throughput of Llama 3 70B and connect directly to data inside Oracle database!

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As someone jumping fresh into the #genai movement I found this video from #aiengineers pretty insightful, on how much happened last year with #llms and its correlations with traditional #sdlc practices. Good watch and feel free to share other lessons in your experience if any. #builders #aws

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In 2013, Jay Kreps introduced the concept of the log as a fundamental abstraction for real-time data streaming, paving the way for platforms like #ApacheKafka. Over the past decade, the data streaming landscape has undergone a remarkable transformation, particularly with the rise of cloud-native environments and the integration of scalable object storage solutions like Amazon S3. This evolution has given rise to platforms like #ApachePulsar, each offering innovative approaches to log storage and data streaming. In our latest blog post, we explore the evolution of log storage implementations in data streaming and categorize this architectural journey into three distinct stages: 💡 Leader-Based Architecture Leader-based architectures form the foundation of most data streaming technologies used today. Traditional message queues rely on single-node brokers with primary-backup setups for high availability (HA). Kafka was the first distributed implementation, but it remains leader-based, where a designated leader manages replication. Pulsar advanced this by decoupling compute and storage, but it still follows the leader-based model. This approach works exceptionally well for scenarios requiring ultra-low latency (e.g., milliseconds). For such use cases, sending data through a leader remains unavoidable. 💡 Leaderless Architecture The emergence of S3 as a primary storage layer has triggered a shift toward leaderless architectures. Instead of treating S3 as just a tiered storage solution, it now serves as the main storage layer. In this model, brokers become leaderless, meaning any broker can handle reads and writes. Platforms like WarpStream, Confluent Freight Cluster, and StreamNative’s Ursa Engine are adopting this approach, reflecting the growing trend toward distributed and flexible architectures. 💡 Brokerless / Headless Architecture The next frontier is headless (or brokerless) architectures, where metadata and data are entirely decoupled: Data resides in S3, while metadata is managed by a separate metadata or catalog service. With a standard specification in place, operations no longer need to flow through brokers. Vendors can simply adhere to the standard and build their solutions accordingly. This approach can enable seamless scalability and interoperability. Our Ursa Engine is heading toward this exciting future. Explore the full blog post to dive deeper into these trends and the journey from leader-based to brokerless architectures: https://lnkd.in/gCcmv3rU You can also watch our keynote presentation at Data Streaming Summit: https://lnkd.in/g6a3wHrC

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Check out this new SIGMOD '24 scientific paper. Our customers love Amazon MemoryDB for its low latency performance, rich flexible API, large scale (up to 500 nodes per cluster), security, availability (4 9s SLA), and AWS's operational excellence. We designed MemoryDB for 11 9s of durability while still providing in-memory performance. My team created a nice technical scientific paper on MemoryDB's architecture. If you would like to learn about what's under the hood, check out this SIGMOD '24 paper. Yacine Taleb, Xingbo Wang, Nan Yan, Allen Samuels, Kevin McGehee, Stefan Mueller, and Jean G. - excellent work publishing this paper! https://lnkd.in/gFXPvci8 #aws #memorydb #inmemory #databases #durability #science #sigmod

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Exclusive: Just published my re:Invent Matt Garman preview for Amazon Web Services (AWS) #reInvent conference. Look for innovation up and down the stack at scale. GenAI agents will be front and center but the big story imo will be the impact of #Inference. Inference is the next core building block for developers. My fav quote from Matt: “The entire way that you build generative AI applications is going to change and be reinvented,” Garman asserted. “Inference is the next core building block. If you think about inference as part of every application, it becomes integral, just like databases.” My interviews with Swami Sivasubramanian Deepak Singh Dilip Kumar are all up and I'll share the links in comments. The business and developer value will be unprecedented from this #GenAI wave. https://lnkd.in/gsCFcpV5

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