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Many companies find that it is not enough to simply keep up with the competition in their industry. Such well-known organizations as Amazon and Microsoft continually expand their service offerings, making it hard to compete. Innovation continues to be a huge buzzword in the software industry. Tech companies need a reliable way to provide clients with what they need most, which is service wherever and whenever they need it. One industry that does this best is the SaaS (Software as a Service) space.

Feature engineering is a crucial part of any ML workflow. At Continual, we believe that it is actually the most impactful part of the ML process and the one that should have the most human intervention applied to it. However, in ML literature, the term is often overloaded among several different topics, and we wanted to provide a bit of guidance for users of Continual in navigating this concept.

By incorporating AI and machine learning into mobile testing tools, teams can become more efficient in test automation. In this article, we'll look at how the adoption of AI and machine learning will improve these tools and what the future of testing might look like.

In your iOS development learning journey, you want to understand and use the best practices while writing code. These include working with Clean Architecture, writing good tests for your native iOS app, and knowing how and what to test. This post discusses some open-source iOS Swift projects that you can take inspiration from to learn better development practices, such as: Build, test and deliver mobile apps in record time Start now

In the latest instalment of our interviews speaking to leaders throughout the world of tech, we’ve welcomed CEO of AIClub, Nisha Talagala to share her thoughts. Nisha has significant experience in introducing technologies like Artificial Intelligence to new learners.

Until recently, teams were building a small handful of Kafka streaming applications. They were usually associated with Big Data workloads (analytics, data science etc.), and data serialization would typically be in AVRO or JSON. Now a wider set of engineering teams are building entire software products with microservices decoupled through Kafka. Many teams have adopted Google Protobuf as their serialization, partly due to its use in gRPC.