- Software Mistakes and Tradeoffs: New book by Tomasz Lelek and StackOverflow guru Jon Skeet - Dec 14, 2021.
Flexibility versus maintainability—every decision you make in software engineering involves balancing tradeoffs. Software Mistakes and Tradeoffs is available in early access from its publisher Manning. Pre-order now and start reading immediately as part of the Manning Early Access Program (MEAP).
- 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.
- 5 Must-Read Data Science Papers (and How to Use Them) - Oct 20, 2020.
Keeping ahead of the latest developments in a field is key to advancing your skills and your career. Five foundational ideas from recent data science papers are highlighted here with tips on how to leverage these advancements in your work, and keep you on top of the machine learning game.
- 5 Innovative AI Software Companies You Should Know - Jul 8, 2020.
While machine learning is impacting organizations around the world, some are driving forward the real-world applications of innovative AI. Check out these interesting companies to watch for exciting new progress this year.
- What is Machine Learning on Code? - Nov 1, 2019.
Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents.
- What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem - Jun 10, 2019.
We identify the 6 tools in the modern open-source Data Science ecosystem, examine the Python vs R question, and determine which tools are used the most with Deep Learning and Big Data.
- Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis - May 30, 2019.
Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.
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- KDnuggets™ News 19:n18, May 8: What Data Science/Machine Learning software you used – KDnuggets Poll; The Third Wave Data Scientist - May 8, 2019.
Vote in KDnuggets 20th annual poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects? Also, what skills are needed for the 3rd wave Data Scientist?; The 3 biggest mistakes in learning Data Science; What makes XGBoost so successful; The ranking of best Masters in Analytics/Data Science in US/Canada; and more.
- QCon.ai San Francisco: Applied AI Software Conference for Developers – KDnuggets Offer - Feb 8, 2019.
QCon.ai is a three-day conference focused on the major machine learning and AI software trends affecting software engineers today. Register by Feb 23 with code "KDN" and save.
- 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.
- Kanri Distance Calculator Free License Version with Demo - Oct 23, 2017.
Kanri invites you to a demo where you can receive a free version of the Kanri Distance Calculator, analytics software that takes big data and individualizes results down to individual participant.
- The Data Science Success Kit - Oct 3, 2017.
The Data Science Success Kit is designed to get you to data science success quickly. Don't delay, offer ends October 31, 2017.
- 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 development skills for data scientists - Dec 29, 2015.
Data science is not only about building the models and sharing insights, many times they have to collaborate in deploying models and sharing them with software developers, learn which things to keep in mind while doing so.
- Interview: Beena Ammanath, GE on the Industrial Internet for Data-driven Innovation - Mar 23, 2015.
We discuss the role of Analytics at GE, Industrial Internet and how it is different from consumer internet, and the key capabilities of Predix.