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Reimagine application modernisation with the power of generative AI

CIO

In a global economy where innovators increasingly win big, too many enterprises are stymied by legacy application systems. Modernising with GenAI Modernising the application stack is therefore critical and, increasingly, businesses see GenAI as the key to success. The solutionGenAIis also the beneficiary.

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With critical thinking in decline, IT must rethink application usability

CIO

Usability in application design has historically meant delivering an intuitive interface design that makes it easy for targeted users to navigate and work effectively with a system. Together these trends should inspire CIOs and their application developers to look at application usability though a different lens.

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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machine learning. For more information on how to manage model access, see Access Amazon Bedrock foundation models.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

The workflow includes the following steps: The process begins when a user sends a message through Google Chat, either in a direct message or in a chat space where the application is installed. After it’s authenticated, the request is forwarded to another Lambda function that contains our core application logic.

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Improving the Accuracy of Generative AI Systems: A Structured Approach

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

When developing a Gen AI application, one of the most significant challenges is improving accuracy. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving. .

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

AWS Machine Learning - AI

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

AWS Machine Learning - AI

Organizations building and deploying AI applications, particularly those using large language models (LLMs) with Retrieval Augmented Generation (RAG) systems, face a significant challenge: how to evaluate AI outputs effectively throughout the application lifecycle.

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.

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The Impact of AI on the Modern Recruitment Landscape

From streamlining the job search process to efficiently navigating the influx of applications, AI-powered tools can revolutionize your recruitment efforts.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

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Generative AI Deep Dive: Advancing from Proof of Concept to Production

Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage

Save your seat and register today! 📆 June 4th 2024 at 11:00am PDT, 2:00pm EDT, 7:00pm BST

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The Essential Guide to Analytic Applications

Embedding dashboards, reports and analytics in your application presents unique opportunities and poses unique challenges. We interviewed 16 experts across business intelligence, UI/UX, security and more to find out what it takes to build an application with analytics at its core.

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Navigating the Future: Generative AI, Application Analytics, and Data

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success?

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Five years ago they may have. But today, dashboards and visualizations have become table stakes. Brought to you by Logi Analytics.

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Modernizing Workloads with the Cloud: How to Improve Performance & Reduce Costs

By modernizing and shifting legacy workloads to the cloud, organizations are able to improve the performance and reliability of their applications while reducing infrastructure cost and management.