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Exploring Agentic Workflows and Their Frameworks Introduction to Agentic Workflows Agentic workflows represent a transformative approach in artificial intelligence, where autonomous AI agents perform complex tasks that traditionally require human intervention. These agents, powered by advanced language models, can understand natural language, reason through problems, and interact with external tools to achieve specific objectives. From automating customer support to streamlining research processes, agentic workflows are reshaping how we approach efficiency and innovation. The significance of these workflows lies in their ability to reduce human error, enhance productivity, and allow individuals and organizations to focus on high-level strategic goals. By leveraging AI agents, businesses can delegate routine or intricate operations to intelligent systems, freeing up resources for creative and critical tasks....

Kubeflow Churn Prediction Pipeline Building a Churn Prediction ML Pipeline with Kubeflow This guide walks you through creating and deploying a machine learning pipeline for churn prediction using Kubeflow Pipelines . We will write our components in Python, package them in Docker containers, deploy them to a Kubernetes cluster, and run and monitor the pipeline using the command line. Step 1: Define the Project Structure project_root/ ├── components/ │ ├── preprocess/ │ │ └── preprocess.py │ ├── train/ │ │ └── train.py │ └── evaluate/ │ └── evaluate.py ├── pipeline.py └── Dockerfile (for each component) Step 2: Write Component Scripts preprocess.py def preprocess(): print("Preprocessing data...") with open("/data/processed_data.txt", "w") as f: f.write("cleaned_data") if __name__ == '__main__': preprocess() train.py def train(): print("Training model...") ...

From Transformers & Diffusion to TransFusion From Transformers & Diffusion Models to TransFusion In recent years, Transformers and Diffusion Models have each reshaped AI—from language to images. Today, they come together in TransFusion , a model designed to generate long, realistic time-series data. Let’s dive in. --- 1. Transformers: Understanding Long Sequences Transformers were introduced in 2017 by Vaswani et al. in their seminal paper “Attention Is All You Need” . They replaced RNNs by using self‑attention to directly relate every position in a sequence. The Hugging Face Transformers library makes it easy to use top models: BERT : bidirectional encoder for language understanding (BERT docs) . GPT-family : autoregressive decoder for text generation (GPT docs) . T5 : encoder‑decoder unified text-to-text model (T5 docs) . Example: creating a Transformer encoder in PyTorch: import torch.nn as nn # Create a Transformer encoder with 6...

A Journey Through the Product Lifecycle: ML, AI, and Software Engineering A Journey Through the Product Lifecycle: Building, Scaling, and Sunsetting ML, AI, and Software Products Imagine you’re an explorer, charting the untested waters of a new product—perhaps an AI-powered chatbot like Grok, a machine learning (ML) model predicting customer behavior, or a sleek software application streamlining workflows. Every product in machine learning, artificial intelligence, and software engineering embarks on a fascinating lifecycle: Introduction , Growth , Maturity , and Decline . In this tutorial, we’ll navigate each stage with a storytelling lens, weaving in practical tools, examples, and actionable steps to help you bring your tech product to life. Whether you’re a developer, data scientist, or product manager, this guide is your roadmap to success. 1. Introduction Stage: The Spark of Creation Picture a bustling lab where data scientists, engineers, a...

Building and Deploying a Recommender System on Kubeflow with KServe In this tutorial, we'll walk through the process of developing a basic recommender system and deploying it on a Kubernetes cluster using Kubeflow and KServe (formerly KFServing). Kubeflow provides an end-to-end platform for machine learning workflows, and KServe simplifies model serving with powerful features like autoscaling and multi-framework support. What You'll Learn Understanding the core components: Kubeflow, KServe. Training a simple collaborative filtering recommender model. Containerizing your model training and serving code. Defining a Kubeflow Pipeline for MLOps (optional but recommended). Deploying your trained model using KServe's InferenceService . Sending inference requests to your deployed model. Prerequisites Before you begin, e...

CrewAI vs LangGraph: A Simple Guide to Multi-Agent Frameworks 🤖 Introduction In the rapidly evolving space of autonomous AI agents, two exciting frameworks are standing out in 2025: CrewAI and LangGraph . Both are designed to help developers build multi-agent systems , but they take very different approaches. This tutorial offers a side-by-side comparison to help you choose the right tool for your use case. Whether you're building a collaborative task automation system or chaining agents in complex workflows, this guide will get you started. 🛠️ What Is CrewAI? CrewAI is an open-source Python framework built from scratch to create and manage a group (a "crew") of autonomous agents. These agents collaborate like a team to accomplish shared goals. Key Features of CrewAI Role-based agents Task delegation and orchestration Integration with tools (e.g., web search, API calling) ...