- Top 5 Reasons Why Machine Learning Projects Fail - Jan 28, 2021.
The rise in machine learning project implementation is coming, as is the the number of failures, due to several implementation and maintenance challenges. The first step of closing this gap lies in understanding the reasons for the failure.
Data Preparation, Data Science, Failure, Implementation, Machine Learning
- CatalyzeX: A must-have browser extension for machine learning engineers and researchers - Jan 6, 2021.
CatalyzeX is a free browser extension that finds code implementations for ML/AI papers anywhere on the internet (Google, Arxiv, Twitter, Scholar, and other sites).
Implementation, Machine Learning, Programming, Research
- What’s the Best Data Strategy for Enterprises: Build, buy, partner or acquire? - Jul 22, 2019.
Every large organization is investing heavily in building data solutions and tools. They are building data solutions from scratch when they could be taking advantage of readily available tools and solutions. Many organizations are re-inventing the wheel and wasting resources.
Acquisitions, Enterprise, Implementation, Open Source, Strategy
- Why Data Scientists Must Know About Change Management - Feb 8, 2018.
Change management may be seen as an opposite to data science, but in reality both are related. Without proper implementation, a data science project fails.
Change Management, Data Science, Implementation
- Accelerating Algorithms: Considerations in Design, Algorithm Choice and Implementation - Dec 18, 2017.
If you are trying to make your algorithms run faster, you may want to consider reviewing some important points on design and implementation.
ActiveState, Algorithms, Implementation, Python
- 7 Super Simple Steps From Idea To Successful Data Science Project - Nov 8, 2017.
Ever had this great idea for a data science project or business? In the end you did not do it because you did not know how to make it a success? Today I am going to show you how to do it.
Data Science, Implementation
- 5 overriding factors for the successful implementation of AI - Oct 6, 2017.
Today AI is everywhere, from virtual assistants scheduling meetings, to facial recognition software and increasingly autonomous cars. We review 5 main factors for the successful AI implementation.
AI, Algorithms, GDPR, GPU, Humans vs Machines, Implementation, Open Data
- For AI Engineers/Data Scientists: Implementing Enterprise AI course - Nov 7, 2016.
This unique course that is focussed on AI Engineering / AI for the Enterprise. Created in partnership with H2O.ai , the course uses Open Source technology to work with AI use cases. It is offered online and also in London and Berlin, starting January 2017.
AI, Berlin, Enterprise, FutureText, H2O, Implementation, London, Online Education
- Hadoop and Big Data: The Top 6 Questions Answered - Jan 22, 2016.
6 questions surrounding Hadoop and Big Data are posed and answered, including those related to implementation, management, and practical uses. Find out where Hadoop currently sits in the world of Big Data.
Apache Spark, Big Data, Data Warehouse, Hadoop, Implementation
- The Case Against Quick Wins in Predictive Analytics Projects - Jan 6, 2016.
While “quick wins” are desirable, getting them in a predictive project can be difficult. We review 2 major obstacles to quick wins in predictive analytics projects.
Greta Roberts, Implementation, Predictive Analytics, Project Fail
