Avi Aharon 🎗️’s Post

Check out my new AWS community blog on how to create secure chatbots using Amazon Q Business with data stored on Amazon FSx for NetApp ONTAP.

Companies of all sizes and across every industry are looking to build or choose AI services for their business needs (call center operations, personalized recommendations, fraud detection, content analysis and moderation, and so much more.) Regardless of where you are in this journey, now would be a great time to assess and apply AI/ML architectural best practices across your enterprise. #machinelearning Have you looked at AWS's Well Architected Framework Review (#WAFR) - AI/ML best practices? https://lnkd.in/evBA3BZV AWS (#AWS) AI/ML best practices are organized across 6 best practices pillars, They help you understand the benefits and risks of decisions while building AI/ML workloads. Quick examples of best practices and recommendations are: - How do you define return on your investment and total cost of ownership for ML Workloads? - Do you have established ML roles and responsibilities in your organizations?  - How do you define the priorities of your AI/ML workloads? - How secure are your ML environments? Do you use pre-trained models or managed AI services?  - How do you test and deploy ML workloads? - What are your Key Performance Indicators (KPIs), when it comes to ML workloads and ML models ?  - Does using pretrained models and managed AWS AI services help you reduce cost and your carbon footprint? AWS's AI/ML best practices are available in public documentation, and can also be leveraged through the AWS Well Architected service in your AWS consoles. (And additionally, you now have Amazon Q to dive deep into any of these best practices! #amazonq ) Reach out to your AWS contacts for any additional questions or help with incorporating these best practices! 

🌟 𝗔𝗪𝗦 𝗿𝗲-𝗜𝗻𝘃𝗲𝗻𝘁 2023 𝗥𝗲𝗰𝗮𝗽 🌟 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 2023 showcased the transformative power of generative AI, revolutionizing industries by creating images, videos, stories, and code. Key advancements in data, scalable compute, and machine learning technologies, such as transformers and diffusion models, are driving this change. 𝗠𝗮𝗷𝗼𝗿 𝗔𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁𝘀 Amazon Bedrock, a managed service offering high-performing foundational models, simplifies building and scaling AI applications. Amazon CodeWhisperer, an AI coding companion, enhances developer productivity with code recommendations. 𝗢𝗽𝗲𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗔𝗪𝗦 Accessibility: How will AWS make generative AI tools more accessible to non-experts? 🤔 Data Security: What measures are in place to enhance data governance and security? 🔐 Cost Optimization: How can AWS balance model performance with cost-effectiveness? 💸 Scalability: What improvements are planned for scaling AI applications? 📈 Customization: How is AWS addressing the need for more customizable AI models? 🛠️ Ethical AI: What frameworks ensure the ethical use of AI? 🌐 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 𝗽𝗹𝗲𝗮𝘀𝗲 𝘄𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝘃𝗶𝗱𝗲𝗼 𝗜 𝗰𝗿𝗲𝗮𝘁𝗲𝗱. https://lnkd.in/gimiXbZy

New capabilities for accelerating AWS Mainframe Modernization with machine learning and generative AI assistance. Using the latest generative AI models in Amazon Bedrock and AWS Machine Learning services like Amazon Translate, AWS Blu Insights makes it simple to automatically generate code and file descriptions, transform code from mainframe languages, and query projects using natural language. https://bluinsights.aws/ #mainframemodernization #bedrock #aws #innovation #GenAI

🌟 𝗔𝗪𝗦 𝗿𝗲-𝗜𝗻𝘃𝗲𝗻𝘁 2023 𝗥𝗲𝗰𝗮𝗽 🌟 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 2023 showcased the transformative power of generative AI, revolutionizing industries by creating images, videos, stories, and code. Key advancements in data, scalable compute, and machine learning technologies, such as transformers and diffusion models, are driving this change. 𝗠𝗮𝗷𝗼𝗿 𝗔𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁𝘀 Amazon Bedrock, a managed service offering high-performing foundational models, simplifies building and scaling AI applications. Amazon CodeWhisperer, an AI coding companion, enhances developer productivity with code recommendations. 𝗢𝗽𝗲𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗔𝗪𝗦 Accessibility: How will AWS make generative AI tools more accessible to non-experts? 🤔 Data Security: What measures are in place to enhance data governance and security? 🔐 Cost Optimization: How can AWS balance model performance with cost-effectiveness? 💸 Scalability: What improvements are planned for scaling AI applications? 📈 Customization: How is AWS addressing the need for more customizable AI models? 🛠️ Ethical AI: What frameworks ensure the ethical use of AI? 🌐 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 𝗽𝗹𝗲𝗮𝘀𝗲 𝘄𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝘃𝗶𝗱𝗲𝗼 𝗜 𝗰𝗿𝗲𝗮𝘁𝗲𝗱. https://lnkd.in/gJ4vstp8

🚀 Want to supercharge your operations? Amazon Q for Business might be your secret weapon. 👇👇👇 Amazon Q for Business is a generative AI assistant tailored to your business needs. → Integrates with your data and systems to streamline tasks and accelerate decision-making. → Perfect for improving issue resolution with faster MTTI (Mean Time To Identify) and MTTR (Mean Time To Recovery). Connects to data sources like Amazon S3 and AWS documentation for in-depth problem-solving. → Customizable based on roles and permissions. → Your data remains secure—AWS doesn’t use it to train models. You need to have your data updated in order to reap the benefits The architecture diagram below showcases the use case 📽️ Demo video coming soon! 🔗 Learn more here https://lnkd.in/gcyazYte #AmazonQ #AIforBusiness #MTTR #CloudOps #AWS #GenerativeAI #AItools #OperationsEfficiency #TechInnovation

We are excited to share our latest blog post on exploring alternatives and seamlessly migrating data from Amazon Lookout for Vision. As you may know, Amazon Lookout for Vision, an AWS service that enables customized AI and ML computer vision models for quality inspection, is set to be discontinued on October 31, 2025. In this post, we delve into viable alternatives for both ready-to-use and custom-built solutions, including options from the AWS Partner Network and tools like Amazon SageMaker and Amazon Bedrock. We also provide detailed guidance on how to export your existing data, facilitating a smooth transition to another solution that best meets your needs. Whether you're looking for quick implementation or prefer developing a tailor-made solution, we have laid out the avenues available to you. To learn more about these alternatives and the migration process, please read the full article here: [Exploring alternatives and seamlessly migrating data from Amazon Lookout for Vision](https://ift.tt/kQ86rAf).

One cool feature I learned about during the preparation for recertification for the AWS Database Specialty - Integrating Aurora with ML services https://lnkd.in/ee7RfxKc

I completed the "Amazon Q Business Getting Started" certification from AWS Skillbuilder. This certification focused on Amazon Q Business, a generative artificial intelligence (generative AI) designed to answer questions, generate content, construct summaries, and complete tasks based on enterprise data. Throughout the certification, I gained insights into various aspects of Amazon Q Business, such as its functionalities, problem-solving abilities, benefits, cost structure, technical ideas, architecture, use cases, and deployment. Additionally, I learned how to set up an Amazon Q Business application in the AWS Management Console and deploy a chat application to interact with Amazon Q Business. #AWS #Skillbuilder #AWSTraining #AWSCertification

🚀 Amazon Q, the most capable generative AI-powered assistant designed for accelerating software development and leveraging company knowledge and data is now generally available on AWS. With Amazon Q, employees can get answers to questions across their business, such as company policies, product information, business results, code base, employees, and many other topics by connecting to enterprise data repositories to summarize the data logically, analyze trends, and engage in dialog about the data. https://lnkd.in/dBZBzs6p .