👋 I’m Prakash,

In this post, I will create a small Node.js app containing the publisher and message processor, and publish it as an image to Docker Hub. Then, I will use the same image to demonstrate scaling Kubernetes Jobs using KEDA, triggered by Amazon SQS. For demonstration purposes, I will be using a Minikube local Kubernetes cluster, but in reality, this would be done in a production Kubernetes cluster Here is the architectural diagram for our workflow....

In this post, I will briefly define supervised fine-tuning of large language models (LLMs), prepare a dataset for Google Vertex AI collected from social media posts, and use Vertex AI to train Google’s LLM, Gemini to analyze the sentiment. This is a purely no-code blog post. I will demonstrate everything directly from the Google Cloud Console using Vertex AI. Introduction Fine-tuning a large language model (LLM) is the process of further training a pre-trained model to perform a specific task or work with a particular dataset....

In this post, I will define the N+1 query problem and explain how to avoid it using eager loading. Throughout my professional career, I have enhanced the response time of numerous APIs by refactoring N+1 queries. I have seen the N+1 issue in many times that use ORMs, as well as in those that use raw queries. In this blog post, I will use raw queries to demonstrate the N+1 problem and Eager Loading to solve it....

In this post, I will briefly define Large Multimodal Models (LMM), explain the data modalities of LMMs, and demonstrate Google Gemini’s multimodal capabilities using text and images. This is purely a no-code blog post. I will demonstrate everything directly from the Google Cloud Console using Vertex AI Studio and Vertex AI to showcase a simple example of using multimodal inputs (text file and image) Introduction A Large Multimodal Model (LMM) is an Artificial Intelligence (AI) model capable of processing and understanding multiple types of input data modalities simultaneously and providing outputs based on this information....

As a beginner, understanding a Kubernetes (k8s) cluster might be a little bit complicated. If you go through the k8s official documentation its overwhelming. In my personal view, the best way to learn any new tool and technology is by understanding its core concepts and architecture. In this blog post, I will try to simplify the basic concepts and common components of a Kubernetes (k8s) cluster with simple architecture diagram....

Ensuring an application runs smoothly in production is crucial. This post guides you to write and execute a simple Gatling API load test using JavaScript to ensure the stability, speed, scalability, and responsiveness of your application under a given workload. You may have written unit tests and functional tests, and the application looked good, so you shipped it to production. However, it suddenly crashes, experiences performance issues, or encounters API request timeout errors....

Logging play a very important role in software development. It helps monitoring, troubleshooting, debugging, event tracing, request tracing, security, and also can be used by Business Intelligence(BI) for reporting purpose. Logging has numerous benefits. But in this blog post, we will focus on building a real-world example to store application logs in Elasticsearch via a Node.js app by using the Winston NPM package and visualize them using Kibana. Additionally, we will dockerize the entire application....

Sometimes, you may see raw queries in your codebase, especially when dealing with very old codebases. Nowadays, people tend to use ORMs (Object-Relational Mapping). It’s possible that in the past, developers used table names, column names, or function names that are now considered keywords by newer versions of MySQL. In such cases, upgrading from an older MySQL version to a newer one (for example, from MySQL 5.7 to 8.x) can be challenging if your codebase uses raw queries without proper escaping for table names, column names, or function names in the queries....

Sometimes, we need to notify or update another system after a certain interval or upon completing background processing jobs. In such situations, we either use webhooks or polling. Each method has its own use cases, advantages, and disadvantages. This blog post will explain the differences between polling and webhooks, along with their respective use cases, pros, and cons. What is Polling? Polling, also known as API polling, is a mechanism where the client repeatedly calls the server to check for updates or changes until it receives a response from the server....

On various news portals and e-commerce platforms, you may have seen recommendations for articles and products related to the main article or product. Recommending similar products, articles, or documents involves complex algorithms, but Elasticsearch empowers us to utilize recommendation algorithms effortlessly. Mainly, recommending similar products, articles, or documents is accomplished through content similarity or by considering the user’s query history. I will provide an example of Amazon product recommendations. In the image below, “Get similar item Fast” is related to content similarity, while the second one, “Customers who viewed this item also viewed” is related to the user’s query history....