- Choose The Right Job in Data: 5 Signs To Look For In An Engineering Culture - Oct 8, 2021.
Software engineers seeking jobs at data companies face a new problem: choosing the right job out of all the options. Learn the 5 signs that signal an agile and innovative engineering culture.
Career Advice, Data Engineer, Data Science, Software Engineering
- Software Engineering Best Practices for Data Scientists - Mar 30, 2021.
This is a crash course on how to bridge the gap between data science and software engineering.
Data Science, Data Scientist, Programming, Python, Software Engineering
Software Engineering Tips and Best Practices for Data Science - Oct 13, 2020.
Bringing your work as a Data Scientist into the real-world means transforming your experiments, test, and detailed analysis into great code that can be deployed as efficient and effective software solutions. You must learn how to enable your machine learning algorithms to integrate with IT systems by taking them out of your notebooks and delivering them to the business by following software engineering standards.
Best Practices, Data Science, Software Engineering, Tips
- Software engineering fundamentals for Data Scientists - Jun 30, 2020.
As a data scientist writing code for your models, it's quite possible that your work will make its way into a production environment to be used by the masses. But, writing code that is deployed as software is much different than writing code for exploratory data analysis. Learn about the key approaches for making your code production-ready that will save you time and future headaches.
Advice, Best Practices, Data Science, Programming, Software Engineering
- 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.
Agile, CRISP-DM, Data Analytics, Data Management, Data Mining, Decision Management, Development, Software Engineering
- Better notebooks through CI: automatically testing documentation for graph machine learning - Apr 16, 2020.
In this article, we’ll walk through the detailed and helpful continuous integration (CI) that supports us in keeping StellarGraph’s demos current and informative.
Graphs, Integration, Jupyter, Machine Learning, Python, Software Engineering
- Software Interfaces for Machine Learning Deployment - Mar 11, 2020.
While building a machine learning model might be the fun part, it won't do much for anyone else unless it can be deployed into a production environment. How to implement machine learning deployments is a special challenge with differences from traditional software engineering, and this post examines a fundamental first step -- how to create software interfaces so you can develop deployments that are automated and repeatable.
API, Deployment, Machine Learning, MLOps, Software Engineering
- Why software engineering processes and tools don’t work for machine learning - Dec 5, 2019.
While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.
Agile, Andrew Ng, Comet.ml, Machine Learning, Software Engineering
- How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow - Oct 29, 2019.
In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.
Machine Learning, Python, scikit-learn, Software Engineering, Workflow
- How To Unit Test Machine Learning Code - Nov 28, 2017.
One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.
Machine Learning, Neural Networks, Python, Software Engineering, TensorFlow
- Big Data Architecture: A Complete and Detailed Overview - Sep 19, 2017.
Data scientists may not be as educated or experienced in computer science, programming concepts, devops, site reliability engineering, non-functional requirements, software solution infrastructure, or general software architecture as compared to well-trained or experienced software architects and engineers.
Analytics, Big Data, Big Data Architecture, Cloud, Cloud Computing, Scalability, Software, Software Engineering
- Software Engineering vs Machine Learning Concepts - Mar 6, 2017.
Not all core concepts from software engineering translate into the machine learning universe. Here are some differences I've noticed.
Machine Learning, Software Engineering
- The High Cost of Maintaining Machine Learning Systems - Jan 21, 2015.
Google researchers warn of the massive ongoing costs for maintaining machine learning systems. We examine how to minimize the technical debt.
Google, Machine Learning, Software Engineering, Technical Debt, Zachary Lipton
