We are thrilled to announce 📢 our latest blog post with Amazon Web Services (AWS) that reveals🔍our Fine-Tuned science and technology 🦾 within Ninja! You can read the brand-new blog post below ⬇️ and try Ninja at MyNinja.ai
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Earlier this month, I had the opportunity to attend Amazon Web Services (AWS) Gen AI event focusing on the latest updates in Amazon Bedrock. I've shared my key takeaways and insights in this blog post - check it out to learn what's new in the world of Bedrock and how Ollion can unlock new insights! #AmazonBedrock #AI #AWS #Ollion
Exploring the Latest Updates on Amazon Bedrock
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Few weeks back, I participated in an AWS organized meet-up for startup founders and business leaders regarding Gen-AI where I got insights around all services created by AWS to help companies experiment with AI. We discussed alot about AWS Bedrock and AWS Sagemaker and learnt how AWS can help my own startup with creating AI enabled solutions. Following are some things I would like to share with you as a summary... 1. Large Language Models (Generative, Recommendational, Conversational) have limitations such as Knowledge Limitations, they can be biased, they can make errors, etc. However you can overcome these by customizing them. The easiest way is Prompt Engineering 2. Writing better prompts can result into better responses. However it still cannot overcome the limitations. For e.g. if you want to know the number of moons around a planet, if you ask it to GPT, it will respond according to it's last update, which might be an year ago. However recently, some more moons were discovered for few planets of which the Model is unaware of. Hence the response will be inaccurate. In this case, we can use something called as Retrieval Augmented Generation (RAG). 3. RAG is a technique (some sources call it a Framework) in which, in simple words, you plug the model to an external source of up-to-date information, due to which, the Model has the relevant data and hence, again with Prompt Engineering, it can give you the most accurate responses. 4. Fine Tuning is another topic which was discussed. Fine Tuning gives you more control over the model. In simple words, you pick a pre-trained model and further train it on a smaller dataset (small enough compared to what LLMs are actually trained on, large enough and relevant for your organization). Fine Tuning is costly but helps in solving your company specific use-cases as the model is relevant for only your company. Further, we discussed about how Amazon Bedrock can help with RAG and Fine Tuning models. AWS provides a set of Foundation models from top companies. We can use them and customize them as per our needs and data. AWS Bedrock is a Fully Managed Service and hence everything related to hosting, provisioning and maintenance is taken care by AWS. This as well as the foundation models is the selling proposition of the service. The discussions were enough to give a start into preparing to equip AI with AWS. Following is a link to get started: #aws #bedrock #sagemaker #largelanguagemodels
What is Amazon Bedrock?
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Big milestone for AWS! Amazon Q Developer, Amazon Q Business, and Amazon Q in QuickSight, are all generally available. Don't forget to check the Amazon Code Catalyst integration with Amazon Q! #amazonwebservices #aws #amazonq #genai #awscloud
Accelerate software development and leverage your business data with generative AI assistance from Amazon Q | Amazon Web Services
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We're excited to share that Amazon Web Services (AWS) has published a comprehensive blog post, which delves into a range of groundbreaking technologies that are shaping the future of AI. From Agents for Amazon Bedrock that enable developers to configure foundational models based on data and user input, to cost-efficient Amazon Elastic Compute Cloud (EC2) P5 Instances, the article covers the cutting-edge of AWS generative AI innovations. We're particularly honored that our CEO, Charles Burden, was quoted in the section discussing the Vector Engine for Amazon OpenSearch Serverless. This technology is a game-changer, allowing developers to build machine learning-augmented experiences without the hassle of managing vector database infrastructure. As a committed AWS partner, #TensorIoT is thrilled to be at the forefront of leveraging these latest advancements. We're not just adopting these technologies; we're integrating them into solutions that solve real-world problems for our customers. So, if you're interested in how AWS is revolutionizing the AI landscape and how TensorIoT is part of that journey, this article is a must-read. https://ow.ly/cQ9Z50PIwuL
Why AWS Partners Are Excited About the Latest Innovations in Generative AI on AWS | Amazon Web Services
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There's a lot that goes into the infrastructure for generative AI. Here are a few ways AWS is focusing on this foundational layer for its customers.
Generative AI Infrastructure at AWS | Amazon Web Services
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AI Engineer at StackOne 🤖 | OpenUK AI Advisory Board | AWS Community Builder | Cofounder AI Demo Days & Meetup Organiser 💫
Building demos with Large Language Models is easy... Releasing to production is difficult 😳 Needing to manage underlying infrastructure like compute, storage, load balancing and networking... before even thinking about data security and compliance 🤯 Or just use OpenAI GPT-3/4 APIs? Now there is another option 😇 but is it better? Amazon Web Services (AWS) released their new Generative AI Bedrock service into general availability 🤖 Bedrock has a bunch of key features which drastically simplify the process of integrating GenAI workloads into your applications 🚀 I wrote an article about all of these and compared whether you should switch from OpenAI 👇 🔗 Read the full article on the GenAI days blog: https://lnkd.in/eJxVBtuH #ai #genai #generativeai #awscommunitybuilders #amazonbedrock #bedrock #llm #awscommunity #aws
Empowering Enterprises with Serverless Generative AI: Amazon Bedrock | GenAI Days
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At AWS, our top priority is safeguarding the security and confidentiality of our customers’ workloads. We think about security across the three layers of our generative AI stack: Bottom layer – Provides the tools for building and training LLMs and other FMs Middle layer – Provides access to all the models along with tools you need to build and scale generative AI applications Top layer – Includes applications that use LLMs and other FMs to make work stress-free by writing and debugging code, generating content, deriving insights, and taking action. At AWS, we’re continuing that innovation as we invest in building performant and accessible capabilities to make it practical for our customers to secure their generative AI workloads across the three layers of the generative AI stack, so that you can focus on what you do best: building and extending the uses of the generative AI to more areas.
A secure approach to generative AI with AWS | Amazon Web Services
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Curious about the latest and greatest from AWS? 🙌🏼 In our latest news article, Buzzcloud’s AWS consultant Isa Sand shares her top 3 announcements from AWS re:Invent in Las Vegas earlier in December. Click and read more about: 👉🏼 Powerful new capabilities for Amazon Bedrock 👉🏿 New AWS Trainium Chip for AI Models 👉🏽 Amazon Q - Your AI Work Assistant Have you tried Amazon Q, what did you think? Or did another re:Invent announcement catch your eye? Share your thoughts below and let's discuss! 💫 🤖 #Buzzcloud #AWS #HomeOfCloudSpecialists #AWSreinvent #reinvent #ai #llm #genAI #bedrock #trainium #amazonq #generativeai
Read More | Top 3 Announcements from AWS re:Invent 2023 | Buzzcloud
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News - Sep 28: AWS announces the general availability of Amazon Bedrock and powerful new offerings to accelerate generative AI innovation There are a number of ways to get started to provide the capability to folks who are at different points in their journey or adoption. Stoked to see this announcement today. #aws #awscloud #amazon #gena #generativeai #amazonbedrock #aiml
AWS announces the general availability of Amazon Bedrock and powerful new offerings to accelerate generative AI innovation
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Amazon Web Services (AWS) is stepping up its game in the generative AI space with the launch of Custom Model Import. This new feature, currently in preview, is part of AWS' enterprise-focused suite, Bedrock, and it's a game-changer for companies looking to host and fine-tune their custom generative AI models. With Custom Model Import, organizations can now import their proprietary AI models into Bedrock, where they can access them as fully managed APIs. This means that companies can benefit from the robust infrastructure of AWS while maintaining the uniqueness of their own models. It's a significant move by AWS to address the infrastructure challenges faced by enterprises in deploying generative AI models. #AWS #GenerativeAI #CloudComputing #Innovation
Amazon wants to host companies' custom generative AI models | TechCrunch
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