AI Notes

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Author: Admin

  • Apache Spark

    Let’s illustrate Apache Spark with a classic “word count” example using PySpark (the Python API for Spark). This example demonstrates the fundamental concepts of distributed data processing with Spark. Scenario: You have a large text file (or multiple files) and you want to count the occurrences of each unique word in the file(s). Steps: from… Read more

  • Inner workings of Apache Spark

    Here’s a breakdown of key internal aspects of the inner workings of Apache Spark. : 1. Architecture: 2. Execution Model: 3. Data Partitioning: 4. Shuffle Operations: 5. Memory Management: In essence, Spark’s internal workings involve: Understanding these internal mechanisms is key to writing efficient and scalable Spark applications. Read more

  • MLOps pipeline

    While a full-fledged MLOps pipeline involves integrating various tools and platforms, here are some illustrative code snippets demonstrating key MLOps concepts using popular Python libraries and tools. These examples focus on individual stages and can be combined to build a more comprehensive pipeline. 1. Data Versioning with DVC (Data Version Control): This isn’t Python code,… Read more

  • Workflow of MLOps

    The workflow of MLOps is an iterative and cyclical process that encompasses the entire lifecycle of a machine learning model, from initial ideation to ongoing monitoring and maintenance in production. While specific implementations can vary, here’s a common and comprehensive workflow: Phase 1: Business Understanding & Problem Definition Phase 2: Data Engineering & Preparation Phase… Read more

  • Developing and training machine learning models within an MLOps framework

    The “MLOps training workflow” specifically focuses on the steps involved in developing and training machine learning models within an MLOps framework. It’s a subset of the broader MLOps lifecycle but emphasizes the automation, reproducibility, and tracking aspects crucial for effective model building. Here’s a typical MLOps training workflow: Phase 1: Data Preparation (MLOps Perspective) Phase… Read more

  • Output of machine learning (ML) model

    The output of a machine learning (ML) training process is a trained model. This model is an artifact that has learned patterns and relationships from the training data. The specific form of this output depends on the type of ML algorithm used. Here’s a breakdown of what constitutes the output of ML training: 1. The… Read more

  • Using .h5 model directly for Retrieval-Augmented Generation

    Using a .h5 model directly for Retrieval-Augmented Generation (RAG) is not the typical or most efficient approach. Here’s why and how you would generally integrate a .h5 model into a RAG pipeline: Why Direct Use is Uncommon: How a .h5 Model Fits into a RAG Pipeline (Indirectly): A .h5 model can play a role in… Read more

  • What is a Tensor

    In the realm of computer science, especially within the fields of machine learning and deep learning, a tensor is a fundamental data structure. Think of it as a generalization of vectors and matrices to potentially higher dimensions. Here’s a breakdown of how to understand tensors: Key Properties of Tensors: Why are Tensors Important in Machine… Read more

  • Tensor

    PyTorch’s fundamental data structure is the Tensor. It’s the central object for numerical computation in PyTorch, analogous to NumPy’s ndarray but with added capabilities for GPU acceleration and automatic differentiation (crucial for deep learning). Here’s a breakdown of PyTorch’s data structure landscape, with the Tensor at the core: 1. Tensors (torch.Tensor) 2. NumPy Arrays (numpy.ndarray)… Read more

  • Google BigQuery

    Google BigQuery is a fully managed, serverless, and cost-effective data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure. It’s designed for analyzing massive datasets1 (petabytes and beyond) with high performance and scalability. Here’s a breakdown of its key features and concepts: Core Concepts: Key Features: Use Cases: In summary, Google… Read more