- Can Data Science Be Agile? Implementing Best Agile Practices to Your Data Science Process - Jan 18, 2021.
Agile is not reserved for software developers only -- that's a myth. While these effective strategies are not commonly used by data scientists today and some aspects of data science make Agile a bit tricky, the methodology offers plenty of benefits to data science projects that can increase the effectiveness of your process and bring more success to your outcomes.
- Software 2.0 takes shape - Oct 23, 2020.
Software developers remain in very high demand as many organizations continue to experience workloads that far exceed available talent. AI-enhanced approaches that automate more areas of the software development lifecycle are in development with interesting potentials for how machine learning and natural language processing can significantly impact how software is designed, developed, tested, and deployed in the future.
- fastcore: An Underrated Python Library - Oct 15, 2020.
A unique python library that extends the python programming language and provides utilities that enhance productivity.
- Making Python Programs Blazingly Fast - Sep 25, 2020.
Let’s look at the performance of our Python programs and see how to make them up to 30% faster!
- New Poll: What Python IDE / Editor you used the most in 2020? - Sep 22, 2020.
The latest KDnuggets polls asks which Python IDE / Editor you have used the most in 2020. Participate now, and share your experiences with the community.
- Automating Every Aspect of Your Python Project - Sep 18, 2020.
Every Python project can benefit from automation using Makefile, optimized Docker images, well configured CI/CD, Code Quality Tools and more…
- Why would you put Scikit-learn in the browser? - Jul 22, 2020.
Honestly? I don’t know. But I do think WebAssembly is a good target for ML/AI deployment (in the browser and beyond).
- A Holistic Framework for Managing Data Analytics Projects - May 22, 2020.
Agile project management for Data Science development continues to be an effective framework that enables flexibility and productivity in a field that can experience continuous changes in data and evolving stakeholder expectations. Learn more about the leading approaches for developing Data Science models, and apply them to your next project.
- Coding habits for data scientists - May 14, 2020.
While the core machine learning algorithms might only take up a few lines of code, it's the rest of your program that can get messy fast. Learn about some techniques for identifying bad coding habits in ML that add to complexity in code as well as start new habits that can help partition complexity.
- Data Science For Our Mental Development - Feb 11, 2019.
In this blog, I aim to generalize how AI can help us with mental development in the future as well as discuss some of the present-day solutions.
- Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry. - Feb 7, 2019.
The following is a method I developed, which is based on my personal experience managing a data-science-research team and was tested with multiple projects. In the next sections, I’ll review the different types of research from a time point-of-view, compare development and research workflow approaches and finally suggest my work methodology.
- DevOps 2.0: Applying Machine Learning in the CI/CD Chain - Oct 2, 2018.
Explore how ML can be implemented in your organization, so you can (for example) enable the automated assessment of test results for far more complex criteria, such as defining thresholds based on statistical significance rather than just presence/absence of specific criteria.
- Setting up your AI Dev Environment in 5 Minutes - Aug 13, 2018.
Whether you're a novice data science enthusiast setting up TensorFlow for the first time, or a seasoned AI engineer working with terabytes of data, getting your libraries, packages, and frameworks installed is always a struggle. Learn how datmo, an open source python package, helps you get started in minutes.