In this post we’re going to compare human manual coding with AI generated code for the same problem. We’re going to use the platform Codility as the vendor of the problem. Here’s the link of the problem that we’re going to solve: https://app.codility.com/programmers/lessons/1-iterations/ 1. Human generated code // you can write to stdout for debugging purposes,...Read More
The primary service you would use for periodic, automated execution of an ML workflow within the SageMaker ecosystem is SageMaker Pipelines or, for simpler scheduling, a combination of Amazon EventBridge (the scheduler) and SageMaker Processing Jobs or Training Jobs (the executor). Here is a breakdown of the three best ways to achieve this, from simplest to...Read More
Amazon SageMaker is a powerful, fully managed service provided by Amazon Web Services (AWS) that is designed to help data scientists and developers quickly and easily build, train, and deploy machine learning (ML) models at scale. It essentially simplifies and automates many of the labor-intensive tasks throughout the entire ML lifecycle, from data preparation all the...Read More
SOAP (Simple Object Access Protocol):💡 Description: A protocol for exchanging structured information in web services. It relies on XML for message format and usually operates over HTTP or SMTP.🔦 Use Cases: Enterprise-level applications requiring high security and transactions, such as banking and payment processing.REST (Representational State Transfer):💡Description: An architectural style that uses HTTP requests to perform...Read More
Building scalable microservices requires the right set of tools to ensure seamless communication, security, and deployment. This guide covers the core technologies used in microservices architecture.1. DatabasesSQL databases like MySQL and PostgreSQL ensure structured data management, while NoSQL options like MongoDB and Cassandra offer scalability for unstructured data.2. Message BrokersKafka, RabbitMQ, and Amazon SQS enable asynchronous...Read More
