Bio-IT World Conference and Expo 2021: Evidence-based Knowledge Discovery at Speed & Scale with Hybrid NL

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Data is critical for biopharma organizations, but this data often comes in the form of language – unstructured and varied across sources – making it difficult to process and gain insights at scale. This session will dive into how you can automate human-like understanding of language data, allowing for large quantities of scientific literature, clinical studies, and medical reports to be analyzed.  Reduce the hours spent reading documentation to empower your organization with accelerated drug discovery and development efforts and cutting-edge medical innovations.
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OTHER ON-DEMAND WEBINARS

Let’s Chat! : Why Conversational AI is the Future for Surveys

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10 Must-Have Capabilities of an Enterprise AI Platform

Data-driven organizations use AI and ML, either natively within applications or infused into applications, to gain enhanced decision intelligence as well as automate processes for greater efficiency. But the proliferation of AI projects, ML models, APIs, and data sets to enable these processes present serious challenges that stand in the way of successful AI and ML deployments.
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5 Linux Security and Management Best Practices for Microsoft Cloud Implementations

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While Linux’s lightweight operating system tremendously improves performance, its benefits often come at the expense of easier management and comprehensive security. Virtualization and cloud administrators – especially those with limited scripting know-how – are frequently challenged by Linux’s command-line-only interface. Microsoft Azure and Hyper-V fully support Linux instances, but don’t provide the tools, training or support required to manage and secure Linux VM lifecycles.
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Machine Learning Foundations for Business-Driven AI

This webinar session is geared towards business analysts and general data enthusiasts to learn the foundations around AI and Machine Learning. We’ll discuss how to approach AI/ML for business transformation, applications for predictive analytics, and best practices across the AI/ML lifecycle.
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