Systems | Development | Analytics | API | Testing

Technology

5 Best Practices for Streaming Analytics with S3 in the AWS Cloud

Streaming analytics is an invaluable capability for organizations seeking to extract real-time insights from the log data they continuously generate through applications and cloud services. To help our community get started with streaming analytics on AWS, we published a piece last year called An Overview of Streaming Analytics in AWS for Logging Applications, where we covered all the basics.

Beyond the Buzz: Braze Equips Modern Marketers with Powerful AI Tools

A lot of the buzz around AI focuses on its future potential. And we get it — we’re talking about a transformative technology that presents seemingly limitless possibilities. But an important aspect of this world-changing tech story that gets lost in the hype is understanding exactly what AI solutions are available for you and your team to employ right now, today.

Gen AI Reshaping E-Commerce: Impact on Product Descriptions, Customer Experiences, and Content

By 2030, the value of the Generative AI (Gen AI) sector is expected to grow to USD 110.8 billion. GenAI is predicted to be responsible for 10% of all data generation by 2025, a stark increase from under 1% in 2021 – Gartner.

Technical Deep-dive - Unlock the Power of Data with AI, Machine Learning & Automation - Part 2

We delve into Generative AI capabilities, seamless application automation integration, and robust machine learning using AutoML. The webinar aims to unravel the behind-the-scenes magic that powers the application. Attendees can anticipate gaining valuable insights into the methodologies and technologies that contribute to enhanced predictability and data-driven decision-making.

Kotlin Unit Testing Guide for Android Developers: Best Practices & Techniques

Unit testing is one of the most powerful features of Android app development, saving us crucial time and reducing overall project cost and allowing developers to embrace test driven development With unit testing, we take an individual slice of code and test it to our requirements. If it passes, then the slice of code is pushed to the repository to merge with the existing code. If it fails, the developers fix the error and retest until it passes.

Implementing Gen AI for Financial Services

Gen AI is quickly reshaping industries, and the pace of innovation is incredible to witness. The introduction of ChatGPT, Microsoft Copilot, Midjourney, Stable Diffusion and many more incredible tools have opened up new possibilities we couldn’t have imagined 18 months ago. While building gen AI application pilots is fairly straightforward, scaling them to production-ready, customer-facing implementations is a novel challenge for enterprises, and especially for the financial services sector.