- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Jupyter Pop-up coming to Boston on March 21 - Feb 28, 2018.
Attend a day-long exploration of Jupyter's best practices and practical use cases in business and industry.
- 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.
- 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.
- 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.
- Turbo Charge Agile Processes with Deep Learning - Feb 7, 2017.
The key to leveraging Deep Learning, or more broadly AI, in the workplace is to understand where it fits within an agile development environment.
- Top Data Scientist Daniel Tunkelang on Data Science Project Scope… and Reducing It - Oct 19, 2016.
Respected Data Scientist Daniel Tunkelang shares some insight into problems lying at the crossroads of software engineering and data science, and prescribes one major solution: reduce scope!
- Connecting Data Systems and DevOps - Jun 17, 2016.
This post will explain why anyone transforming their company into a data-driven organization should care about software development best practices, even if they don’t consider themselves a software company.
- 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.
- Apple: Software Engineering Manager, Analytics Insight - Oct 22, 2014.
Apple Analytic Insight team is responsible for mitigating fraud, waste and abuse company-wide. This role is to lead a team of software engineers who build tools to support the fraud prevention efforts.
- Virginia Tech: Faculty in AI/Machine Learning, Software Engineering, Data Analytics - Oct 16, 2014.
Virginia Tech seeks applicants for one tenure-track and two tenured faculty positions in three areas: artificial intelligence/machine learning, software engineering, and data analytics/cyber security.