Have you ever wondered what it feels like to manually label thousands of images for your project? You know the drill: endless hours looking at screens, second-guessing every tag, and facing the fear of mistakes that could throw off your entire analysis. It's a real headache. You spend more time guessing than making progress, whether trying to align your team's vision or ensuring every detail matches the client's expectations. Here's where image annotation tools step in.

When building your Laravel applications, you may sometimes need to use a NoSQL database to store and retrieve data. One popular choice is Amazon DynamoDB, a fully managed, serverless, and highly scalable NoSQL database service provided by Amazon Web Services (AWS). In this article, we'll take a brief look at DynamoDB. We'll then delve into how to use DynamoDB as a cache store in Laravel, and how to store Laravel models in DynamoDB using the baopham/laravel-dynamodb package.

Working with large volumes of data requires effective data management practices and tools, and two of the frequently used processes are data ingestion and ETL. Given the similarities between these two processes, non-technical people seek to understand what makes them different, often using search queries like “data ingestion vs ETL”.

If you have a legacy system with customized capabilities and valuable features but nearing end of life, refactoring the system is a potential choice for modernization. Insurance platform modernization usually involves one-to-one code migration, which can often be more costly and time consuming than expected and typically tends to miss some of the integration and data architecture modernization that is foundational for getting the full value of digitization.

We tested a product-led go-to-market motion last year. Here are the exciting results and learnings.

New legislation affirms the governance and security responsibilities of all organizations doing business in the EU.

My first attempt to understand accessibility and how to test it started back in 2016. I was working for a company where accessibility was already an important part of the process. I was asked to perform accessibility testing on the product to determine whether we could claim to be accessible. Since it was a new subject for me, I was quite confused. Where should I start? Should I be certified to provide a proper assessment? Do I need to request assessments from third-party companies?

Today’s AI may be able to pass the Turing test, but I think we can all agree that doesn’t make it human. AI is a tool—a powerful one, but a tool nonetheless. And just like any tool, it can make our work easier, but it can’t do it for us.

What is hyperautomation for insurance? It might sound like just another buzzword, but let’s peel back the layers to see how hyperautomation works. Imagine this: you go on a deep dive to evaluate your insurance company’s processes and operational models. You discover inefficiencies, lag times, and that your team manages transactions with disparate, often manual processes. It’s clear you need a new solution. And the solution you need doesn’t currently exist.

Organizations are building data lakes and bringing data together from many systems in raw format into these data lakes, hoping to process and extract differentiated value out of this data. However, if you’re trying to get value out of operational data, whether on prem or in the cloud, there are inherent risks and costs associated with moving data from one environment to another.