8-Chems

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) ...