Viktoria Semaan

How does Elemental Cognition harness the power of #apachekafka and Confluent Cloud to build trustworthy #GenAI solutions? Find out in The New Stack and check out the 5 best practices for building reliable #GenAI apps. ⬇

4

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

192

6 Comments

Google Cloud Storage customers are increasingly running workloads like analytics and AI/ML which expect filesystem-like semantics and structure. We are excited to announce the preview release of Hierarchical Namespace for Cloud Storage, a new bucket creation option that organizes your bucket to look and act more like a filesystem. This provides better performance, consistency, and manageability for file-oriented workloads on object storage. We would love for you to try it out and give us your feedback! https://lnkd.in/g59_Q_N8

74

2 Comments

What makes success stories like Observe.AI so exciting for me is witnessing MongoDB's evolution into an AI-accelerating data platform. For an AI-centric company like Observe.AI, choosing the right data architecture was the star. And they recognised MongoDB's distinct strengths to flex across databases, analytics, search, mobile data management and now vector databases for generative AI workloads. Our powerful indexing and querying capabilities allow Observe.AI's data scientists to quickly access and analyse the data powering their AI models. Read the blog to know how MongoDB is a core enabler of their innovative AI stack. #MongoDB #artificialintelligence #dataanalytics

28

Have your cake and eat it too? You can with Vyond. And go ahead and add a dollop of ice cream. ✅Instant AI video making at scale ✅ Tight as a drum secure. CISO’s love it ✅ Low cost (aka awesome ROI) AWS AI Vyond #Learninganddevelopment #ChiefLearningOfficer #Instructionaldesigner #AIVideo

8

Whoa! Apart from the obvious SAP relevance, this post instantly reminded me of the moment I bought my first PC. I had gone wildly large: two (2) floppy disk drives (translation for 2024AD: each was 360KB). The salesman noticed the extravaganza and suggested I get a 20MB HD, which I found insane. It was a time when businesses of all sizes depended on COBOL (not that they don't now) and faced one critical problem: hard disk failures between backups, resulting in data loss. I used that PC to write a program in 80x86 Assembly that would read the data from a broken HD and recreate the database on another. Working with such constraints taught me invaluable lessons about being resourceful, frugal by necessity, and creative with what you have. These are skills that have carried over into my current work with large-scale, mission-critical AWS projects. Embracing new technologies like the U7i high memory instances with up to 32 TiB of DDR5 memory and 896 vCPUs is exciting. Yet, the foundation of understanding how to maximize and innovate with available resources remains just as crucial. In a world where offshoring is increasingly common, it's essential to recognize the value of deep, hands-on expertise and experience. #TechNostalgia #AWS #CloudComputing #Innovation #Resourcefulness #ProblemSolving #FinOps #CloudManagement #SAP

1

As companies go multi-cloud in their deployments, we need stand alone MLOps/LLMOps solutions that can deployed across vs being dependent on any one platform. As developers use multiple code generators, we will need security solutions that go across all of them to support developers. As businesses use multiple LLMs/AI models/APIs across the enterprise, we will need stand alone solutions to help identify, implement, and secure data and use cases. These are great opportunities to build next gen independent large software companies.

48

6 Comments

Setup a RAG application on AWS Customers with existing data and documents currently managed on #AWS can now easily set up a #RAG application using SearchBlox. Setting up a RAG solution for documents stored within #S3 buckets or filesystems, etc., requires developers to go through multiple steps: Data crawling and ingestion, processing the suitable chunks for embedding and storing within a vector database, converting the user query into a vector search against stored chunks and then retrieving the most relevant chunks of information for augmenting a personalized response with a large language model (LLM). https://lnkd.in/esjaGgWA

2

In another galaxy IBM acquired Broadcom 😀 , but in MilkyWay Galaxy IBM will acquire Hashi for $6.4 billion 🤑 well, Terraform has a new home, will this be terrifying or justifying for Hashi Customers? we can have some delays but hope Apache V2.O will be the future of Terraform or we can see a drift detection of Terraform with Crossplane. Looking forward to an insightful conversation in the comments, well I believe with this move IBM is a Redbull of Platform Engineering Formula 1. I'll be up in the move to cover some grounds and will get back with some more insights while talking to subject matter expert guests and kind of a good space to get in touch with experts for a podcast, don't be terrified of IBM terraforming the terraform. Read more: https://lnkd.in/dwj_2kBJ #hashicorp #ibm #terraform #opensource

5

With fine-tuning functionality for its Command R #LLMs, Cohere helps customers (like Oracle) realize high performance #ai processing at lower costs. Kieron Allen puts the new functionality in context in his analysis on Acceleration Economy: https://lnkd.in/eGwJvWCK

4

Every day, I speak with businesses that are keen to understand how to use Google's #AI to solve real-life problems. During this year's @Google #CloudNext event in Las Vegas we showcased 101 incredible real-world use cases. Here are some of my favourites! Which inspire you? 🎨 Canva is using Vertex AI to help users bypass time-consuming editing tasks and create shareable and engaging videos in just seconds. 📺 Paramount is using Google's VertexAI to video summaries used across its Paramount+ platform more efficiently, boosting productivity. 📈 WPP unveiled plans to integrate Google Cloud’s gen AI capabilities into its intelligent marketing operating system, called WPP Open, which empowers its people and clients to deliver new levels of personalization, creativity, and efficiency 🚘 Uber is using new tools that help customer service representatives by summarising communications with users and surfacing context from previous interactions, so front-line staff can be more helpful and effective. These are just some of the examples of how industry leaders are using Google Cloud AI technologies to boost productivity and enhance the customer experience. To learn more about possible AI applications check out our blog - https://lnkd.in/euimk33d #Google #GoogleCloud #CloudNext #AI #GenerativeAI

108

The rise of Kubernetes in AI deployment marks a pivotal moment in tech evolution. 🚀💻 As Kubernetes celebrates its 10th birthday, it's already powering enterprise AI at scale, shaping the future of cloud-native computing. ☁️🧠 Makes you wonder what groundbreaking innovations will emerge at the intersection of Kubernetes and AI in the next decade? #Kubernetes #AI #CloudNative #TechEvolution 🎉🤖 https://lnkd.in/dUVZsqZe

1

Microsoft has unveiled a novel, compact #AI language model called Phi-3-mini that offers comparable capabilities to large language models (#LLMs) like GPT-4, but with a significantly smaller size and reduced operational costs. Phi-3-mini has only 3.8 billion parameters, compared to the hundreds of billions or even trillions of parameters in the largest LLMs. This makes it suitable for running locally on consumer hardware like smartphones and laptops, without requiring an internet connection or powerful data center GPUs. According to Microsoft, Phi-3-mini's performance "rivals that of models such as Mistral 8x7B and GPT-3.5," which power the free version of ChatGPT. The innovation lies in the carefully curated, high-quality training data used to develop the model, which includes textbooks and synthetic data. The introduction of Phi-3-mini represents a step towards reducing the environmental impact of AI, as smaller models require less energy and computational resources than their larger counterparts. This could lead to more accessible and sustainable AI solutions for everyday tasks. https://lnkd.in/egHGu_MA

16

Google's Workload Optimized Infrastructure at Next '24 Discover how Google Cloud is advancing workload-optimized infrastructure with innovations like Axion CPU & AI Hypercomputer, storage, & more. https://lnkd.in/g9AXr9CH #GoogleCloudNext

1

OCI is leading the pack in cloud infrastructure performance with its ability to support HPC, Exadata, Autonomous Database, and GPU-powered AI and ML applications. What sets OCI apart is its scalable, low-latency network that supports remote direct memory access (RDMA) for lightning-fast performance. Check out this article for more details on how OCI is building a high-performance network in the public cloud. #OCI #cloudinfrastructure #HPC #AI #machinelearning #Exadata #AutonomousDatabase #GPU #publiccloud #oracle https://lnkd.in/gY4SkF-t

8

ONNX is an open source machine learning (ML) framework that provides interoperability across a wide range of frameworks, operating systems, and hardware platforms. This new blog post shows you how to run ONNX Runtime inference on #AWS Graviton3 and how to configure to use optimized GEMM kernels. With these optimizations, you can see up to 65% better performance for fp32 inference and up to 30% better performance for int8 quantized inference for several natural language processing (NLP) models on Graviton3: https://lnkd.in/giWYk6Hq

116

4 Comments

Congratulations #UBIXAI team! As a #UBIXLabs Advisory Board Member, and advocate for customers and partners of #MicrosoftCloud #AzureAI, will #MicrosoftCloud customers and partners have opportunity to benefit from this OpenSource alternative now available to #GoogleCloud customers? Why This Matters? This is a force-multiplier for customers and partners looking to accelerate the journey to cloud. In particular, when their is a dependency on Data Science & Data Engineering resources to successfully transition from legacy environments like those offered by #SASInstitute to intelligently scaling platforms like UBIX that embrace open-source innovations. The Impact? This shift not only reduces costs, it fosters acceleration of your "AI Innovation Agenda", especially as open-source solutions offer greater adaptability and community-driven enhancements. Moreover, UBIX accelerates a customers cloud-migration journey, in addition to unlocking opportunity to simplify, build-out and optimize workflows leveraging the UBIX no-code analytics workflows. The Force-Multiplier AI/ML Resources: For specific algorithms or bespoke code that you need to migrate, UBIX can translate that code as an accelerator into Python, creating a reusable function (Fn) within the UBIX Platform, enabling consumption by any workflow and powering the analytics journey for existing and new innovations including MLOps enablement. The UBIX platform integrates with existing compute infrastructures and transactional systems. The UBIX UI was designed with the business user in mind, abstracting complexity and presenting a consistent interface to power your analytics workflows and AI/ML pipelines! https://lnkd.in/gz6G5ypm

4

1 Comment

At #GoogleCloudNext ‘24, top companies, governments, researchers, and startups showcased how they're already using Google's AI solutions to enhance their work. See how 101 of these leading organizations are developing AI agents in six key areas: customer service; employee empowerment; creative ideation and production; data analysis; code creation; and cybersecurity. #GoogleCloud #GENAI #AI #VertexAI #Google

51

5 Comments