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How Healthcare and Life Sciences Can Unlock the Potential of Generative AI

A patient interaction turned into clinician notes in seconds, increasing patient engagement and clinical efficiency. Novel compounds designed with desired properties, accelerating drug discovery. Realistic synthetic data created at scale, expediting research in rare under-addressed disease areas.

Webinar - Building a Mobile-first Test Automation Strategy without relying on Complex Scripts

In this webinar our experts walk you through why building a mobile-centric test automation strategy is important, the hurdles in conventional framework-based mobile automation systems and how to build a mobile-first test automation strategy using Testsigma's low-code approach.#testautomation #mobile #low-code #testing.

Prevent data issues from cascading and deliver reliable insights with Kensu + Azure Data Factory

38% of data teams spend between 20% and 40% of their time fixing data pipelines¹. Combating these data failures is a costly and stressful activity for those looking to deliver reliable data to end users. Organizations using Azure Data Factory can now benefit from the integration with Kensu to expedite this process. Their data teams can now observe data within their Azure Data Factory pipelines and receive valuable insights into data lineage, schema changes, and performance metrics.

Top 5 Resources to Understand the Role of AI/ML in Embedded Analytics

Every day, more companies unlock the potential of artificial intelligence (AI) and machine learning. When AI and machine learning are utilized in embedded analytics, the results are impressive. Much of this can be seen in modern solutions that offer advanced predictive analytics. Together, predictive analytics and AI can help application teams by streamlining processes, generating personalized recommendations, and creating a more intuitive and efficient user experience.

The Best Resources to Learn Android Development

Android has surged past iOS to dominate the world’s smartphone market, but its ecosystem is growing at an exponential rate. There were more than 24,000 different Android devices at the last count, which places major strains on devs. To learn Android development, you have to learn how to optimize for hundreds of different devices. So you need a clear learning plan that can be applied to the entire Android ecosystem.

Building a global deployment platform is hard, here is why

If you ever tried to go global, you have probably faced a reality check. A whole new set of issues starts to appear when you start to operate a workload over multiple locations across the globe: So it looks like a great idea in theory, but in practice, all of this complexity multiplies the number of failure scenarios to consider!