Always happy to see new MLOps books! The DevOps for Data Science is a new book by Alex K Gold. As the name implies, the book focuses on topics related to DevOps for data scientists. This includes the following:
✅ Command line
✅ Working with Linux systems
✅ Docker
✅ Scaling resources
✅ Network, domains, DNS, SSL, etc.
✅ Authentication
(2/5) 𝐒𝐥𝐢𝐦 𝐢𝐦𝐚𝐠𝐞
Typically, I use the Python official image as the baseline for setting up a dockerized Python environment. The official Python image offers multiple images for different Linux flavors and CPU architectures. The default image (e.g., 𝘱𝘺𝘵𝘩𝘰𝘯:𝘭𝘢𝘵𝘦𝘴𝘵) has comprehensive supporting tools that impact the image size - 1 GB.
A simple way to reduce the image size is to replace the default image with a slim version that is 150 MB (compared to 1GB) 🚀.
(1/2) I am excited to present at the useR!2024 conference on July 2nd!
I am going to run a virtual workshop about deployment and monitoring data and ML pipelines using free and open-source tools. This includes setting pipelines using GitHub Actions, Docker 🐳, R, and Quarto 🚀.
(2/2) The book covers the following topics:
✅ Mathematical foundations of machine learning and NLP
✅ Data preprocessing techniques for text data
✅ Machine learning applications for NLP and text classification
✅ Deep learning methods for NLP and text applications
✅ Theory and design of Large Language Models
✅ Applications of LLM models
✅ LLM applications with Langchain
The book is for folks who are interested in getting started with NLP and those who wish to delve into LLM applications.
I am excited to present at the Dev AI conference in Paris on June 19!
I am going to run a workshop about the deployment and monitoring of ML pipelines with free and open-source tools. This includes using tools such as GitHub Actions and Pages, Docker, Python, Quarto, etc.
I was looking for examples of interactive data visualization for a gradient descent algorithm, and I found this app by Lili Jiang. This desktop app is based on C++ and enables simulation and visualization of different gradient descent algorithms, such as momentum, AdaGrad, RMSProp, and Adam. The app enables to compare different methods simultaneously.
Open your calendar, NumPy 2.0 is going to be out on June 16th 🚀
This is the first major release since 2006. The release includes breaking changes in the library API, and therefore, if you are planing to adopt it, some code refactoring may required.
The release includes new features, performance improvement 🏎️, improvements on the C API, and more.
(1/2) Shiny Apps for demystifying statistical models and methods 🚀
This is a cool website that explains different statistical concepts with the use of interactive Shiny Apps. Ben Prytherch made this website from the Department of Statistics at Colorado State University.
(2/2) It covers the following topics:
✅ Factorial ANOVA
✅ Mixed effect ANOVA
✅ Mixed effect with random slopes
✅ Logistic regression
✅ ANCOVA
✅ One-way ANOVA
✅ Odds ratio vs relative risk
✅ Correlation coefficient vs slope
✅ Sampling
Building robust data pipelines with dbt, Airflow, and Great Expectations 🚀
I started to dive into great expectations - a Python library for data quality checks, and I found this great talk by Sam Bail about building data pipelines with dbt, Airflow, and great expectations.
The Machine Learning for Beginners by Microsoft Developer is an introductory course for classical machine learning. This crash course mainly focuses on regression analysis with Python 🐍, and it covers topics such as:
✅ General setup
✅ Cleaning data
✅ Data visualization
✅ Regression models
✅ Polynomial regression
✅ Logistic regression
Here is a short e-book with a sequence of tutorials on the scientific Python ecosystem for beginners. This includes topics such as:
✅ Working with numerical data using NumPy
✅ Data visualization with Matplotlib
✅ Scientific computing with SciPy
✅ Statistics with Python
✅ Machine learning with scikit-learn