Mario Thomas

Join this MongoDB Podcast Livestream to hear from Together AI's experts, Heejin and Hassan, as they delve into the cutting-edge RAG system, discuss the advantages of integrating AI with MongoDB, and provide a glimpse into the future of AI-powered user experiences that will redefine technology. https://lnkd.in/evjd5DJi

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Ready for Wimbledon? Check out the new "Catch Me Up" feature on their app. It uses IBM's Granite LLM to provide personalized player insights. Find out more. #Wimbledon #AI #personalization https://ibm.biz/BdmtAu

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Building accessibility solutions can be challenging, especially when additional hardware is required to coexist with existing software ecosystems. However, there's hope on the horizon! This open-source project, which utilizes #AI, shows great promise in moving the frontier further. Even at the research stage, the results are looking quite promising. Check out the details here: https://lnkd.in/eik8X8Xy

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Blocking access to #AI sites from your organisation's network is fighting against the rising tide; it won't work. There are far more effective ways to deal with AI risks, without missing out on the benefits of AI, as discussed in this article by Richard Owen. https://lnkd.in/emCFrUZA

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My newest blog post describes how we still work to improve the performance, security, and internal efficiency of the very first #AWS service: "Optimizing Amazon Simple Queue Service (SQS) for speed and scale" - https://lnkd.in/gjjk76Bn

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🚀 Anthropic’s Claude 3.5 Haiku model now available in Amazon Bedrock, and it's even better than Claud 3 Opus! Many people are interested in smaller AI models focused on specific tasks. This usually means taking a base model and fine-tuning it with your own data. While that works, it takes time and advanced skills. With Haiku 3.5, you can get amazing results without all that extra effort, and it’s as fast as smaller models. Beats earlier Claude models on coding and task handling, with lower costs. Key points per category: ▶ Coding Performance - Surpasses Claude 3.5 Sonnet and GPT-4o ▶ Processing Speed - 3x faster than comparable models ▶ Cost Efficiency - Up to 90% savings with caching ▶ Intelligence Tests - Exceeds previous generation benchmarks. Things to keep in mind, great for real time use cases (like Interactive Chatbots), data processing, content moderation, and Advance Code completion at really low cost. More details: https://lnkd.in/dZ36_82F #AWS #GenAI #AI #LLM #AmazonBedrock #Haiku #Anthropic

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Exciting new release from AWS. An important part in addition to the normal code quality and optimization practices is the following excerpt from the article. "With the Python and .NET managed runtimes, there are two types of SnapStart charges: the cost of caching a snapshot per function version that you publish with SnapStart enabled, and the cost of restoration each time a function instance is restored from a snapshot. So, delete unused function versions to reduce your SnapStart cache costs. To learn more, visit the AWS Lambda pricing page."

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We had an engaging session with GE on AI’s future and LLM frameworks.  Key point: data quality and proprietary data access become the competitive advantages. #AI #LLM

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Agree! Phi-3 together with Fabric for manufacturing is very exciting! Phi-3's lightweight AI model enhances decision-making on the shop floor, while Fabric's unified data platform streamlines processes, driving unprecedented productivity. Together, they're transforming the manufacturing landscape. #Phi3 #FabricCloud #ManufacturingInnovation #OperationalEfficiency #FrontlineEmpowerment https://lnkd.in/g2ryXz2r

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Protecting the public, reducing reoffending, & delivering swift access to justice ⚖️ are top priorities for the UK Ministry of Justice. Using #AWS analytics & machine learning, they've accelerated decision making, enhanced cross-ministry collaboration & reduced operational complexity 📊. Find out more 👉

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Exciting news for UK AI startups! 🚀 The UK government just launched a new platform to help businesses assess and mitigate the risks of AI. This is a fantastic step towards building trust and transparency in the AI ecosystem, in my opinion. At Google Cloud, we're committed to empowering startups with the compute power, talent acquisition support, and mentorship they need to succeed. By fostering a strong ecosystem of responsible AI development, we can unlock the immense potential of AI to benefit society and drive economic growth. London is the home of FinTech and this further establishes us to set the standard for responsible AI too 🤖📈

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WOW, this blog post (Part 1) by Nishant Casey, Ilan Gleiser, Karsten Schroer, and Sam Bydlon showcases how AWS Services can enable researchers on the biodiversity data already gained, provide insight into its relationships with other species to, "deduce special ecological interactions and identify misclassified species in collections." The solution utilised AWS services: AWS Glue to ingest and transform data stored in Amazon Simple Storage Service (Amazon S3). AWS Glue gives the NHM team the ability to prepare the GBIF dataset and any other datasets they require in a fast and simple manner. The team uses an AWS Glue PySpark notebook in order to filter specific data and generate load files that will hydrate an Amazon Neptune serverless database. Neptune allows the team to build a graph database that can be queried by researchers using the Gremlin query language. The Neptune bulk loader is a feature used to load the data prepared using AWS Glue into the database. Précis: Introduction The twin crises of biodiversity loss and climate change are perhaps the single greatest challenge faced by science and society. The Earth is losing species at up to 1,000 times the natural rate, and by some estimates, 30–50 percent of all species may be lost by 2050. The world’s largest museums have worked together to look at how their data can be used to tackle these challenges. The Natural History Museum in London (NHM) is a world-class visitor attraction and a leading science research center. NHM and Amazon Web Services (AWS) have worked together to transform and accelerate scientific research by bringing together a broad range of UK biodiversity and environmental data types in one place for the first time. The museum cares for a collection of more than 80 million natural science specimens that provide a historical record that can be used to understand the impacts of human activity and project future trends in biodiversity. In this post, the first in a two-part series, we provide an overview of the NHM-AWS project and the potential research benefits. #AWS #MachineLearning #AmazonNeptune #AmazonSageMaker #AWSGlue #CustomerSolution, #Nonprofit #UKPublicSector #Research #Sustainability #TechnicalHowto #NationalHistoryMuseum

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Customers are using Amazon Managed Service for Apache Flink to process data in real-time. Data streaming enables generative AI to take advantage of real-time data and provide businesses with rapid insights. This post looks at how to integrate generative AI capabilities when implementing a streaming architecture on #AWS. https://lnkd.in/g9wezHvt #Streaming #Data #StreamingData #DataStreaming #Kinesis #ApacheFlink #Flink #Bedrock

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#AWS #reinvent is fast approaching and i’m delighted to be contributing again this year. I’ll be in the AWS Modern Applications and Open Source Zone, Wednesday 4th December 4PM - 5PM doing a demo of #PostgeSQL Fast Refresh Materialized Views Fast Refresh Materialised Views (MV) functionality for PostgreSQL has often been a blocker for people wanting to migrate from Oracle to open source. Fast refresh uses materialized view logs on the underlying tables to keep track of changes, and only the changes since the last refresh are applied to the MV. In contrast, the complete refresh process refreshes all the data and can inevitably take hours for large datasets. This demo shows a project co-developed with the AWS database freedom team that both unblocks and speeds up your PostgreSQL analytics migrations.

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An insightful read: How Deputy unlocks real-time insights and operational efficiency with Amazon MSK and Amazon Redshift. https://lnkd.in/grvedxDs #msk #kafka #aws #realtime

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Redshift Serverless just became better with the GA of AI scaling and optimizations.

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HMG complex PaaS migration to AWS Containers went remarkably smoothly. I think that this was due to good simple design decisions made for each component. And a structured well planned migration sequence. With the API service https://lnkd.in/eD6etuCa migrated last. #containers #aws https://lnkd.in/eGCekJHw

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