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appian

Top 4 AI Use Cases in Life Sciences

Jun 4, 2024 By Victoria Ebel In Appian
The life sciences industry is rapidly embracing artificial intelligence (AI) to automate complex processes, enhance efficiency, and improve outcomes. From drug development to patient care, AI-driven automation is transforming the landscape. Here are some of the top artificial intelligence use cases for process automation in the pharmaceutical industry.
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appian

Improving Government Case Management with AI: 6 Use Cases

Jun 4, 2024 By Kyle Thomas In Appian
There are many ways AI can improve government case management processes. Examples include: AI capabilities often depend on a large language model (LLM), an advanced AI system that can understand, analyze, interpret, and generate human language. It uses deep learning techniques to predict and produce coherent text based on input prompts. A large language model trained on government agency data is capable of tasks like text extraction, translation, summarization, and conversational responses.
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bugfender

Google Play Store Policy Changes 2024

Jun 4, 2024 By Sachin Siwal In Bugfender
Google regularly updates its developer policies to make sure Google Play is a safe and trustworthy platform for everyone. While this is unquestionably necessary and essential to protect users and their data, keeping up with the latest changes to ensure applications remain safe and compliant can feel a little overwhelming for developers.
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tricentis

Introducing Tosca Copilot: Your generative AI-powered productivity assistant

Jun 3, 2024 By Simona Domazetoska In Tricentis
We’re thrilled to announce the launch of Tosca Copilot, a generative AI assistant that enhances productivity by optimizing test portfolios, explaining complex test cases, and providing actionable execution insights. Tosca Copilot is an add-on to Tosca Commander and is designed to make your testing lifecycle more efficient and effective.
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snowflake

Introducing Polaris Catalog: An Open Source Catalog for Apache Iceberg

Jun 3, 2024 By Saurin Shah In Snowflake
Open source file and table formats have garnered much interest in the data industry because of their potential for interoperability — unlocking the ability for many technologies to safely operate over a single copy of data. Greater interoperability not only reduces the complexity and costs associated with using many tools and processing engines in parallel, but it would also reduce potential risks associated with vendor lock-in.
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cloudera

Acquisition of Verta's Operational AI Platform Will Transform Cloudera's AI Vision to Reality

Jun 3, 2024 By Dipto Chakravarty In Cloudera
In an era where artificial intelligence (AI) is reshaping enterprises across the globe—be it in healthcare, finance, or manufacturing—it’s hard to overstate the transformation that AI has had on businesses, regardless of industry or size. At Cloudera, we recognize the urgent need for bold steps to harness this potential and dramatically accelerate the time to value for AI applications.
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cloudera

Cloudera Introduces AI Inference Service With NVIDIA NIM

Jun 3, 2024 By Robert Hryniewicz In Cloudera
We are excited to announce a tech preview of Cloudera AI Inference service powered by the full-stack NVIDIA accelerated computing platform, which includes NVIDIA NIM inference microservices, part of the NVIDIA AI Enterprise software platform for generative AI. Cloudera’s AI Inference service uniquely streamlines the deployment and management of large-scale AI models, delivering high performance and efficiency while maintaining strict privacy and security standards.
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Defining Asynchronous Microservice APIs for Fraud Detection | Designing Event-Driven Microservices

May 31, 2024 By Confluent In Confluent
In this video, Wade explores the process of decomposing a monolith into a series of microservices. You'll see how Tributary bank extracts a variety of API methods from an existing monolith. Tributary Bank wants to decompose its monolith into a series of microservices. They are going to start with their Fraud Detection service. However, before they can start, they first have to untangle the existing code. They will need to define a clean API that will allow them to move the functionality to an asynchronous, event-driven microservice.
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