Fri.Mar 29, 2024

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Salesforce BLIP: Revolutionizing Image Captioning

Analytics Vidhya

Introduction Image captioning is another exciting innovation in artificial intelligence and its contribution to computer vision. Salesforce’s new tool, BLIP, is a great leap. This image captioning AI model provides a great deal of interpretation through its working process. Bootstrapping Language-image Pretraining (BLIP) is a technology that generates captions from images with a high level […] The post Salesforce BLIP: Revolutionizing Image Captioning appeared first on Analytics Vidhya.

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Rocket Mortgage lays foundation for generative AI success

CIO Business Intelligence

To succeed in the mortgage industry, efficiency and accuracy are paramount. So too is keeping your options open. That’s why Rocket Mortgage has been a vigorous implementor of machine learning and AI technologies — and why CIO Brian Woodring emphasizes a “human in the loop” AI strategy that will not be pinned down to any one generative AI model. The Detroit-headquartered retail mortgage lender has been deploying machine learning and AI for more than a decade and is among the few pioneers that hav

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X-CLIP: Advancing Video Recognition with Language-Image Pretraining

Analytics Vidhya

Introduction Video recognition is a cornerstone of modern computer vision, enabling machines to understand and interpret visual content in videos. With the rapid evolution of convolutional neural networks (CNNs) and transformers, significant strides have been made in enhancing the accuracy and efficiency of video recognition systems. However, traditional approaches are often constrained by closed-set learning […] The post X-CLIP: Advancing Video Recognition with Language-Image Pretraining

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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight

AWS Big Data

In Part 2 of this series, we discussed how to enable AWS Glue job observability metrics and integrate them with Grafana for real-time monitoring. Grafana provides powerful customizable dashboards to view pipeline health. However, to analyze trends over time, aggregate from different dimensions, and share insights across the organization, a purpose-built business intelligence (BI) tool like Amazon QuickSight may be more effective for your business.

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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

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10 GitHub Repositories to Master MLOps

KDnuggets

Begin your MLOps journey with these comprehensive free resources available on GitHub.

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Nexthink scales to trillions of events per day with Amazon MSK

AWS Big Data

Real-time data streaming and event processing present scalability and management challenges. AWS offers a broad selection of managed real-time data streaming services to effortlessly run these workloads at any scale. In this post, Nexthink shares how Amazon Managed Streaming for Apache Kafka (Amazon MSK) empowered them to achieve massive scale in event processing.

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Direct Lake and DirectQuery in the Age of Fabric

Paul Turley

I just returned from the Microsoft Fabric Community Conference in Las Vegas. Over 4,000 attendees saw a lot of demos showing how to effortlessly build a modern data platform with petabytes of data in One Lake, and then ask CoPilot to generate beautiful Power BI reports from semantic models that magically appear from data in a Fabric Lakehouse. Is Direct Lake the silver bullet solution that will finally deliver incredibly fast analytic reporting over huge volumes of data in any form, in real time

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OpenTelemetry vs. Prometheus: You can’t fix what you can’t see

IBM Big Data Hub

Monitoring and optimizing application performance is important for software developers and enterprises at large. The more applications that an enterprise deploys, the more data that exists for collecting and analyzing. Yet, this data isn’t worth much without the right tools for monitoring, optimizing, storing and—crucially—putting the data into context.

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Protected: Using GraphDB’s Natural Language Interface to Talk with Your Content

Ontotext

Password Protected To view this protected post, enter the password below: Password: Submit The post Protected: Using GraphDB’s Natural Language Interface to Talk with Your Content appeared first on Ontotext.

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GraphQL vs. REST API: What’s the difference?

IBM Big Data Hub

As the conduits through which software components interact and data flows across the internet, APIs are the lifeblood of contemporary web services. API technologies like SOAP (a web services messaging protocol), REST (an architectural style) and GraphQL (a programming language and tool) simplify software development by enabling third-party data and services integration.

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8 Steps to Transformation at Speed & Scale – Your Guide to Deploying StratOps

📌Is your Data & AI transformation struggling to really impact the business? Discover the game-changing StratOps approach that: Bridges the Gap : Connect your Data & AI strategy to your operating model, to ensure alignment at every level. Prioritizes Outcomes : Focuses on concrete business outcomes from day one, rather than capabilities in isolation.

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Holistic asset management for utility network companies

IBM Big Data Hub

Addressing challenges of the energy transition with grid asset management The energy transition is gearing up to full speed as renewable energy sources replace fossil-based systems of energy production. The grid itself must green to operate within the environmental, social and governance (ESG) objectives and become carbon neutral by 2050. This shift requires energy utility companies to plan their grid asset management holistically as they find a new balance between strategic objectives.

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Create a lasting customer retention strategy

IBM Big Data Hub

Customer retention must be a top priority for leaders of any company wanting to remain competitive. An effective customer retention strategy should support the company to maintain a healthy stable of loyal customers and bring in new customers. Generating repeat business is critical: McKinsey’s report on customer acquisition states (link resides outside of ibm.com) that companies need to acquire three new customers to make up the business value of losing one existing customer.

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Empowering career growth amidst global challenges 

IBM Big Data Hub

Finding the right recipe for career growth can challenge global clients. At Conagra, a leading food manufacturer, prioritizing employee growth and career mobility became paramount, especially amidst the recent pandemic. The company recognized the need for a strong skills foundation, empowering its workforce to develop talents and explore new career opportunities within the organization.