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In this blog, I explore the Software Development Life Cycle (SDLC), a systematic framework utilized by software developers and project managers to effectively design, develop, test, and deploy software applications. The SDLC encompasses key stages: P

3 ways how most AI systems are built. I’ve explained each one step-by-step. 1) 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽) 1. 𝗦𝗲𝘁 𝘁𝗮𝘀𝗸 – Decide what problem the model should solve. 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗱𝗮𝘁𝗮 – Gather lots of example

4 different ways of training LLM's. I've given a simple detailed explanation below. 1.) 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 𝗖𝘂𝗿𝗮𝘁𝗶𝗼𝗻 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽) Prepares clean, consistent, and useful data so the model learns effectively. 1. Collect text

2 frameworks powering next generation of AI apps. Here’s how LangGraph and LangChain make it happen. 𝗟𝗔𝗡𝗚𝗚𝗥𝗔𝗣𝗛 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽) LangGraph is a graph-driven framework for building dynamic, multi-agent AI workflows. 1. 𝗗𝗲𝗳𝗶𝗻𝗲

LangGraph vs LangChain, many people get confused. I've explained each in simple below. 𝗟𝗔𝗡𝗚𝗚𝗥𝗔𝗣𝗛 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽) LangGraph is a graph-driven framework for building dynamic, multi-agent AI workflows. 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗮𝗽𝗽 𝗼𝗯𝗷�

3 ways AI systems are deployed today. I’ve explained each method below in simple steps. 1.) 𝗖𝗹𝗼𝘂𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽) 𝗙𝗹𝗼𝘄: • 𝗨𝘀𝗲𝗿 𝘀𝘂𝗯𝗺𝗶𝘁𝘀 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 - User types a query or command. • 𝗖𝗹𝗼𝘂𝗱 𝗿

The fastest AI apps aren’t using the cloud. I've explained the reason for this below. Most people think the cloud is the go-to solution for AI, but when speed and real-time results are a must, other deployment options outperform it. 1.) 𝗖𝗹𝗼𝘂𝗱

“𝗟𝗲𝗮𝗿𝗻 𝘁𝗼 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝘆𝗼𝘂’𝗹𝗹 𝗮𝗹𝘄𝗮𝘆𝘀 𝗵𝗮𝘃𝗲 𝗮 𝗷𝗼𝗯.” That advice used to be gold. But times changed. Software dev job postings in the US are down 𝟕𝟎% since 2022. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝘆: •AI is quietly replacing junior rol

what I do in future currently doing job software test engineer I have skills and experience Automation Testing ETL-BI testing Manual Testing Database Testing API testing performance testing Telecom Domain Health care domain FMGC Domain