The expectation gapWhen a team says "we're building agents," two very different pictures are usually in the room.The engineering picture is a workflow with LLM calls in it.There is a DAG.Node 3 extracts entities,node 4 classifies them,node 5 writes a summa...
Real-time model registry versioning and dynamic model adaptation driven by autonomous agents become essential in highly dynamic,mission-critical environments where data distributions shift rapidly and manual human intervention creates costly operational de...
The architectural design for a modern Machine Learning and AI application adopts a cloud-native,event-driven microservices pattern structured across six core layers to satisfy functional requirements such as automated data transformation,model versioning,a...
1.Original Meaning of "Scaffold"The term scaffold originates from physical construction and was later adapted into developmental psychology and education before being applied to computer science and artificial intelligence:Physical Construction: A scaffold...
While AI-DLC (AI Development Life Cycle) and AI-SDLC (AI Software Development Life Cycle) sound similar,they represent different scopes in the development of intelligent software solutions::AI-DLC (AI Development Life Cycle): Focuses specifically on the mo...
Your model fails at 2 a.m.on a rainy street.You have the clip.You have the logs.You even have the exact frame where confidence collapsed.Now what?.For most teams,the answer is a week of Slack threads,a half-remembered S3 prefix,and someone scrubbing throug...
Definition and Core ConceptsData lineage is the process of tracking,documenting,and visualizing data as it flows from its point of origin through various transformations,storage systems,and consuming applications to its final destination.It provides a hist...
Designing a robust system infrastructure for allocating GPU and LLM clusters requires an optimized task management approach to address severe resource limitations.Because these computational clusters operate under strict hardware constraints,the infrastruc...
Bridging the gap between Machine Learning (ML) theory and infrastructure often comes down to shift in perspective: ML is the intelligence (the brain),and Infrastructure is the system that keeps it alive,fed,and useful to the world (the body and environment...
The LLM-Powered Multi-Agent Orchestration System is designed as a decoupled,event-driven architecture that transforms complex unstructured tasks into structured outcomes through autonomous agent collaboration.The execution pipeline begins with asynchronous...
The end-to-end architectural design for the machine learning training pipeline begins at the Vehicle,where telemetry and sensor data are collected and routed into the Data Ingestion system.This ingested data is stored centrally in a Data Lake and subsequen...
Just like Microsoft’s AutoGen or CrewAI,Amazon Web Services (AWS) provides powerful multi-agent frameworks and fully managed features that enable multiple AI agents to collaborate and solve complex tasks.Broadly,AWS offers two main paths for implementing ...
Microsoft AutoGen is an open-source framework developed by Microsoft that simplifies the orchestration,optimization,and automation of complex large language model workflows by enabling multiple cooperative agents to converse with one another to solve tasks...
Here is an in-depth investigation into Semantic Kernel (SK),its key features,proposed integration architectures,and a detailed comparison with Microsoft AutoGen. 1.What is Semantic Kernel?Semantic Kernel (SK) is an open-source,lightweight SDK developed by ...
The paradigm shift in building applications with Large Language Models (LLMs) has moved from simple,single-prompt interactions to complex,multi-agent systems that require memory,decision-making loops,and collaboration.To address this,the creators of LangCh...
Microsoft AutoGen is an open-source framework designed to simplify the development of advanced Large Language Model (LLM) applications.Its core paradigm relies on multi-agent conversation,where multiple specialized agents communicate,collaborate,and orches...
The core mission is to develop kernel-level implementations for a variety of Deep Neural Network (DNN) models,specifically targeting Arm architectures.The primary goals are to optimize these implementations for maximum power efficiency and performance,cruc...
While Large Language Models (LLMs) have undeniably transformed AI capabilities across numerous tasks,they do not diminish the critical need for direct deep learning implementation using frameworks such as PyTorch,TensorFlow,or JAX.LLMs are powerful general...
Every evening when you open Netflix,your home screen greets you with personalized movie thumbnails and tailored content suggestions.Netflix delivers hyper-customized recommendations to over 200 million users in a matter of seconds.From a data scientist's p...
1.Initial Retrieval Prioritizes Speed Over PrecisionIn standard RAG systems,the first-stage retrieval (using dense embeddings or sparse keyword matching) is designed to scan vast databases rapidly.It focuses on speed and breadth,fetching a broad list of th...
Below is from Aurimas Griciūnas showing hierarchical layers for AI Agents.Sometimes 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 might seem simple from the outside.They are not 👇There are many moving pieces that production ready Agentic Systems rely on. I like to split the 𝗛𝗶𝗲𝗿...
MLOps (Machine Learning Operations)MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.It bridges the gap between machine learning development (ML Dev) and operations (Ops),similar to ...
A single ad exchange processing 100k requests per second generates terabytes of logs per day: impression logs (one per auction),error logs (DSP failures,timeouts),financial logs (pacing,budget,spend),and audit logs (campaign changes,user access).This torre...
Monolith is ByteDance’s deep-learning recommendation system (described in a 2022 paper) that powers TikTok’s “For You” feed.It is a large-scale,two-stage (retrieval → ranking) deep-learning engine,purpose-built for the dynamic,sparse,and constantly sh...
Almost every large-scale recommendation service — TikTok,YouTube,Amazon,Netflix,Spotify — is built on the same architectural skeleton,regardless of which specific model it uses.Understanding this skeleton is more valuable than memorizing any single algor...