Spark is known as a fast general-purpose cluster-computing framework for processing big data. In this post, we’re going to cover how Spark works under the hood and the things you need to know to be able to effectively perform distributing machine learning using PySpark. The post assumes basic familiarity with Python and the concepts of machine learning like regression, gradient descent, etc.
This past week some of the Scout team had the opportunity to hang out at PyCon USA in Cleveland. This was the first time the Scout APM team had attended PyCon. It was great to spend some time with an awesome swath of the Python community. With a great booth location situated across the aisle from the innovative and fun Slack booth, we had fun getting to know everyone with a solid amount of traffic heading past our booth over the exhibition days.
The 2019 edition of PyCon USA takes place over the next few days in Cleveland, Ohio. Scout is delighted to be there, sharing our APM tool with the Python community. Plus, we'll have great t-shirts and stickers for you, and we love to get geeky - one of our lead product engineers, plus two of our smart support engineers, are working the booth, ready to help you figure out your Python performance problems.
This post demonstrates a *basic* example of how to build a deep learning model with Keras, serve it as REST API with Flask, and deploy it using Docker and Kubernetes. This is NOT a robust, production example. This is a quick guide for anyone out there who has heard about Kubernetes but hasn’t tried it out yet. To that end, I use Google Cloud for every step of this process. The reason is simple — I didn’t feel like installing Docker and Kubernetes on my Windows 10 Home laptop.
Python’s built-in logging module is designed to give you critical visibility into your applications with minimal setup. Whether you’re just getting started or already using Python’s logging module, this guide will show you how to configure this module to log all the data you need, route it to your desired destinations, and centralize your logs to get deeper insights into your Python applications.
Learning to build webapps is an exciting process, but it comes with its own set of challenges. As a newer developer, deciding what programming language will bring your big idea to life is a common challenge. There are lots of terrific choices for building webapps on the market. Today, we’ll focus on two of 2019’s most popular options: Node.js vs Python.
Python is one of the most popular programming languages, and its usage is only accelerating. It was named the TIOBE language of the year in 2018 due to its growth rate. Python’s ease of use and large community have made it a popular fit for data analysis, web applications, and task automation. In this post, we’ll cover the details of garbage collection in Python. First, we’ll review the basics about memory management and why garbage collection is needed.
Serious software development calls for performance optimization. When you start optimizing application performance, you can’t escape looking at profilers. Whether monitoring production servers or tracking frequency and duration of method calls, profilers run the gamut. In this article, I’ll cover the basics of using a Python profiler, breaking down the key concepts, and introducing the various libraries and tools for each key concept in Python profiling.
Serverless is still an unexplored realm for many developers and the idea of transitioning from traditional servers to serverless may look like a daunting task. To ease this shift many communities have come up with some really good resources, whether it be articles or frameworks. In this blog, I will be introducing one such microframework for Python, developed by engineers at AWS, called Chalice. I will not go into a lot of details in this blog but highlight important parts of the microframework.
For years, data scientists have struggled to deploy their models in a timely manner before they become obsolete. Traditionally, models must be manually recoded, a time-intensive process that can take months, if not longer, to complete. Alteryx Promote solves this model deployment challenge by allowing data scientists to quickly turn complex Machine Learning models into a RESTful API from the development environment of their choice.
