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Category: Python

PyPI and Malicious Code

Steven Vaughan-Nichols gives us the story:

The Python Package Index (PyPI), is the most popular Python programming language software repository. It’s also a mess. Earlier this year, the FortiGuard team discovered zero-day malware in three PyPI packages called “colorslib,” “httpslib,” and “libhttps.”  Before that, 2022 closed with  PyTorch-nightly on Linux being poisoned with a fake dependency. More recently, PyPI had to stop new user registrations and project creations because of a flood of malicious users. PyPI isn’t the only one to notice the user trouble. The Python Software Foundation (PSF) received three subpoenas for PyPI user data. What is going on here!?

Read on to learn more about what’s happening with the most popular Python repository.

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Diffify Updates

Myles Mitchell celebrates a year of diffify:

We’ve just passed an important milestone for diffify: our app for tracking Python and R package releases has just turned 1 year old! To mark this exciting occasion we are delighted to announce an “anniversary update” featuring numerous quality of life improvements. This post will outline the latest changes and tease at some exciting developments in the works…

Check out these recent changes and a little bit of what’s on the horizon.

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Creating Your First PySpark Application

Dustin Vannoy gives us a primer on Apache Spark:

Get hands on with Python and PySpark to build your first data pipeline. In this video I walk you through how to read, transform, and write the NYC Taxi dataset which can be found on Databricks, Azure Synapse, or downloaded from the web to wherever you run Apache Spark. Once you have watched and followed along with this tutorial, go find a free dataset and try to write your own PySpark application. Pro tip: Search for the Spark equivalent of functions you use in other programming languages (including SQL). Many will exist in the pyspark.sql.functions module.

In addition to the code listing, Dustin has a video walking us through the process.

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Hybrid ML and Rules-Based Fraud Detection

Ayodeji Ogunlami mixes approaches:

In developing this hybrid system, sets of rules are required as well as a machine learning model. I would be making use of a vehicle insurance dataset from Kaggle in this demonstration.

The dataset can be downloaded from this link:

The ML model would be built using a random forest classifier on Azure Databricks using Pyspark.

This seems to be the most sensible approach, especially given how rare actual fraud incidents are and what that imbalance does to classification algorithms.

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Common Challenges Implementing PySpark Code

Amlan Patnaik looks at some common implementation problems:

Pyspark has become one of the most popular tools for data processing and data engineering applications. It is a fast and efficient tool that can handle large volumes of data and provide scalable data processing capabilities. However, Pyspark applications also come with their own set of challenges that data engineers face on a day-to-day basis. In this article, we will discuss some of the common challenges faced by data engineers in Pyspark applications and the possible solutions to overcome these challenges.

Read on for five such challenges.

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Improving the Robustness of ML Model Deployment

Alexander Billington shares a few tools and tips:

Machine learning (ML) model deployment is a critical part of the MLOps lifecycle, and it can be a challenging process. In the previous blog, we explored how Azure Functions can simplify the deployment process. However, there are many other factors to consider when deploying ML models to production environments. In this blog, we’ll delve deeper into some of the essential hints and tips for more robust model deployments. We’ll look at topics such as proper model versioning and packaging, data validation, and performative code optimisations. By implementing these practices, data scientists and ML engineers can ensure their models are deployed efficiently, accurately, and with minimal downtime.

MLflow is definitely a good recommendation, as is Pydantic (which is on my to-learn list…one of these days).

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Generating Artificial Data in Databricks

Ben Hazan needs some fake data:

While attending the SQLBits 2023, I took part in André Kamman’s session about “Generate test data quick, easy and lots of it with the Databricks Labs Data Generator”.

In this blog, I will share with you my insights about the DataBricks Data Generator library and I’ll give an example.

Synthetic data is a valuable resource for data scientists, engineers, and analysts who need to test, benchmark, or demonstrate their solutions without compromising sensitive or confidential information. However, generating realistic and relevant synthetic data can be challenging and time-consuming.

That’s why Databricks Labs has developed a Python library called dbldatagen that can help you create large-scale synthetic data sets using Spark.

Click through to learn more about the library and see how you can use to to generate arbitrary amounts of artificial data following certain constraints.

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Data Validation with Great Expectations and Azure Functions

Eduard van Valkenburg does a bit of data validation:

Great Expectations (Great Expectations Home Page • Great Expectations) is a popular Python-based OSS tool to validate data that comes into your data estate. And for me, validating incoming data is best done file by file, as the files arrive! On Azure there is no better platform for that then Azure Functions. I love the serverless nature of Functions and with the triggers available for arriving blobs, as well as HTTP, event grid events, queues and others. There are some great patterns that allow you to build event-driven data architectures. We also now have the Python v2 framework for Azure Functions available, which makes the developer experience even better. So, let’s go through how to get it running.

This looks really interesting and tying it in to Azure Functions is a good idea assuming that the checks don’t run for too long.

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Working with Kafka from Python

Dave Shook has a new course for us:

If you’re a Python developer, our free Apache Kafka for Python Developers course will show you how to harness the power of Kafka in your applications. You will learn how to build Kafka producer and consumer applications, how to work with event schemas and take advantage of Confluent Schema Registry, and more. Follow along in each module as Dave Klein, Senior Developer Advocate at Confluent, covers all of these topics in detail. Hands-on exercises occur throughout the course to solidify concepts as they are presented. At its end, you will have the knowledge you need to begin developing Python applications that stream data to and from Kafka clusters.

Read on to learn more about it and give it a try.

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Visualizing PyTorch Models

Adrian Tam describes a model:

PyTorch is a deep learning library. You can build very sophisticated deep learning models with PyTorch. However, there are times you want to have a graphical representation of your model architecture. In this post, you will learn:

  • How to save your PyTorch model in an exchange format
  • How to use Netron to create a graphical representation.

Click through for the article, which is mostly about training the PyTorch model. Visualizing it turns out to be pretty easy with the right tool.

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