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How to Monitor and Check the Performance of an API Deployed on Azure

APIs are the backbone of many modern applications, enabling them to interact seamlessly with other services and platforms. Whether you’re running a small app or a large enterprise system, monitoring and checking the performance of your API is crucial. Why? Because slow or unreliable APIs can frustrate users, harm your business reputation, and lead to lost revenue. In this article, we’ll explore how you can effectively monitor and check the performance of your API deployed on Microsoft Azure.

Understanding AI and Shift Left Testing | Shray Sharma | #generativeai #softwaretesting

In this video, Shray Sharma discusses "AI and Shift Left Testing, Advocating for a Change," exploring how the integration of AI can transform testing practices in alignment with the Shift Left approach. Shray begins by breaking down Shift Left testing, explaining its principles and benefits for improving product quality and development efficiency.

Using GraphQL API in Android

Since it was created by Facebook in 2012 and made publicly available in 2015, GarphQL has changed everything about how we fetch data from servers for our front-end apps. Most front-end clients typically use REST APIs to retrieve data from the server, this includes mobile apps for platforms like Android, iOS, and Flutter, as well as JavaScript frameworks like React, Angular, Vue, and Next. A huge advantage of GraphQL is that it enables front-end clients to request only the API they require.

15+ Mobile App Testing Statistics

As global app downloads have doubled every quarter since 2015, and users now spend more time on apps than websites, the need for thorough mobile app testing is more critical than ever. The app testing market is projected to reach $13.6 billion by 2026. This blog will cover key mobile app testing statistics and explore why it is essential for delivering high-quality user experiences and maintaining competitive advantage.

How to Implement Gen AI in Highly Regulated Environments: Financial Services and Telecommunications and More

If 2023 was the year of gen experimentation, 2024 is the year of gen AI implementation. As companies embark on their implementation journey, they need to deal with a host of challenges, like performance, GPU efficiency and LLM risks. These challenges are exacerbated in highly-regulated industries, such as financial services and telecommunication, adding further implementation complexities. Below, we discuss these challenges and present some best practices and solutions to take into consideration.

Build Scalable AI-Enabled Applications with Confluent and AWS

In this video, Confluent and AWS address enterprises' challenges in deploying generative AI and how Confluent Cloud and Amazon Bedrock empower organizations to build scalable, AI-enabled applications. We'll explore how Confluent's comprehensive data streaming platform enables you to stream, connect, and govern data at scale, creating real-time, contextualized, and trustworthy applications that differentiate generative AI.