- 85% of data science projects fail – here’s how to avoid it - Sep 13, 2021.
Here are a few common traps that data scientists can avoid to NOT be one of the 85% of data science projects that fail.
- Why Do Machine Learning Projects Fail? - Feb 24, 2021.
At the beginning of any data science project, many challenges could arise that lead to its eventual collapse. Making sure you look ahead -- early in the planning -- toward putting your resulting model into production can help increase the chance of delivering long-term value with your developed machine learning system.
- 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.
- Predicting Heart Disease Using Machine Learning? Don’t! - Nov 10, 2020.
I believe the “Predicting Heart Disease using Machine Learning” is a classic example of how not to apply machine learning to a problem, especially where a lot of domain experience is required.
- When good data analyses fail to deliver the results you expect - Nov 3, 2020.
To all those Data Scientists out there who thrive on discovering actionable insights from your data (all of you, right?), take heed from this cautionary tale of a data analysis, a dashboard, and a huge waste of resources.
- Why BERT Fails in Commercial Environments - Mar 24, 2020.
The deployment of large transformer-based models in dynamic commercial environments often yields poor results. This is because commercial environments are usually dynamic, and contain continuous domain shifts between inference and training data.
- Why organizations fail in scaling AI and Machine Learning - May 29, 2019.
We explain why AI needs to understand business processes and how the business processes need to be able to change to bring insight from AI into the process.
- The Best and Worst Data Visualizations of 2018 - Feb 8, 2019.
We reflect on some of the best examples of Data Visualization throughout 2018, before focussing on some of the not-so-good and how these can be improved.
- 6 Data Visualization Disasters – How to Avoid Them - Feb 5, 2019.
If you intend to use data visualizations in a presentation or publication, be certain that your audience will understand and trust the information. Here are six mistakes you will want to avoid.
- 5 reasons data analytics are falling short - Jul 30, 2018.
When it comes to big data, possession is not enough. Comprehensive intelligence is the key. But traditional data analytics paradigms simply cannot deliver on the promise of data-driven insights. Here’s why.
- 9 Reasons why your machine learning project will fail - Jul 25, 2018.
This article explains in detail some of the issues that you may face during your machine learning project.
- Data Science: 4 Reasons Why Most Are Failing to Deliver - May 24, 2018.
Data Science: Some see billions in returns, but most are failing to deliver. This article explores some of the reasons why this is the case.
- AI is not set and forget - May 3, 2018.
Just like a car, AI-based system can tick along in decent shape for a while. But neglect it too long and you’re in trouble. Unfortunately, failing to maintain your AI will destroy the project.
- Data science through the lens of research design - May 16, 2017.
Data science projects may often fail due to a lack of clear definition of the business goal and not because data scientists technical abilities. We examine the connection between data science and research design to help address this issue.
- How to Fail with Artificial Intelligence: 9 creative ways to make your AI startup fail - May 4, 2017.
This post summarizes nine creative ways to condemn almost any AI startup to bankruptcy. I focus on data science and machine learning startups, but the lessons on what to avoid can easily be applied to other industries.
- Top Reasons Why Big Data, Data Science, Analytics Initiatives Fail - Dec 1, 2016.
We examine the main reasons for failure in Big Data, Data Science, and Analytics projects which include lack of clear mandate, resistance to change, and not asking the right questions, and what can be done to address these problems.
- Trump, The Statistics of Polling, and Forecasting Home Prices - Nov 12, 2016.
Why polling has failed in US Presidential election? The home price index offers an apt comparison inasmuch as sample selection is problematic, equally snagging both election predictions and home price futures.
- Trump, Failure of Prediction, and Lessons for Data Scientists - Nov 9, 2016.
The shocking and unexpected win of Donald Trump of presidency of the United States has once again showed the limits of Data Science and prediction when dealing with human behavior.
- Top KDnuggets tweets, May 4-10: Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages - May 11, 2016.
Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages; Why AI development is going to get even faster; Why Implement #MachineLearning Algorithms From Scratch?
- Don’t be afraid to Fail – Start Now with Data Science - Mar 30, 2016.
An argument for why aspiring data scientists should stop waiting for permission and start doing data science.
- When Big Data Means Bad Analytics - Mar 21, 2016.
When analytics delivers disappointing results, it is often because there is not enough analytic expertise, and/or lack of understanding of a business objectives for using Big Data in the first place. To avoid failure, insist on high standards.
- Big Data + Wrong Method = Big Fail - Oct 19, 2015.
Big data is hyped as a gold mine, but Big Data applications are risky. Understand how to start with a minimum viable application and iterate to minimize the risk of failure.
- Top KDnuggets tweets, Jun 4-5: “Practical Data Science with R” stands out; Top 5 cities for #BigData jobs - Jun 6, 2014.
How does "Practical Data Science with R" book stand out ? Top 5 cities for #BigData jobs: San Francisco, McLean, Boston, St. Louis, and Toronto; Big jump in #BigData applications, code built with Apache Spark ; 76 Startup Failure Post-Mortems.