Machine Learning Pipelines with Spark: Introductory Guide (Part 1)

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • mleap

    MLeap: Deploy ML Pipelines to Production

    Everything is custom and will take a lot of work, but luckily, you don’t have to do all the work here. In THE second article, you will use MLeap, a library that does the heavy lifting in terms of serializing Spark ML Pipeline for real-time inference and also provides an execution engine for Spark so you can deploy pipelines on non-Spark runtimes.

  • Apache Spark

    Apache Spark - A unified analytics engine for large-scale data processing

    Apache Spark is a fast and general open-source engine for large-scale, distributed data processing. Its flexible in-memory framework allows it to handle batch and real-time analytics alongside distributed data processing.

  • Sonar

    Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.

  • scikit-learn

    scikit-learn: machine learning in Python

    The concepts are similar to the Scikit-learn project. They follow Spark’s “ease of use” characteristic giving you one more reason for adoption. You will learn more about these main concepts in this guide.

  • Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

    DataFrames are a Pandas-like, intuitive high-level API for working with data in Spark. It organizes data in a structured and tabular format in rows and columns, similar to a spreadsheet and a relational database management system. If you have worked with Pandas before, you should be familiar with DataFrames.

  • kubernetes

    Production-Grade Container Scheduling and Management

    Spark works locally on stand-alone clusters and on Hadoop YARN, Apache Mesos, Kubernetes, and other managed Hadoop platforms.

  • InfluxDB

    Build time-series-based applications quickly and at scale.. InfluxDB is the Time Series Platform where developers build real-time applications for analytics, IoT and cloud-native services. Easy to start, it is available in the cloud or on-premises.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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