Honest thinking about technology decisions. — Page 16
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Foto Friday – Trees on Mount Scott, Wichita Mountains Wildlife Refuge
Oklahoma isn't known for 'mountains' but there are a few areas in the state that are actually quite hilly and - officially - called mountains. The Wichita
Foto Friday – Sedona Arizona Buttes
Earlier this year, I made a trip out to Sedona Arizona. I hadn't been out there before and didn't have a lot of time to spend in the field with my camera,
Machine learning risks are real. Do you know what they are?
Do you know what the big four machine learning risks are? Do you know how to mitigate these risks? If not, check out this article to learn more.
What is the cost of bad data?
How much is bad data costing you? It could be very little or a great deal. An example of what the cost of bad data really looks like.
What can you DO with Machine Learning?
What can you actually do with machine learning and AI? Examples of areas within organizations that can benefit from these technologies.
Beware the Models
Just because your machine learning or AI models look good on paper does not guarantee they will work in the real world.
Foto Friday – Sunrise colors over Sedona
A few weeks ago, I traveled to Sedona Arizona for a few days. While there, I was able to sneak out and get a few sunrise photos. Here's the first of a few
Big Data Roadmap – A roadmap for success with big data
I'm regularly asked about how to get started with big data. My response is always the same: I give them my big data roadmap for success.
Local Interpretable Model-agnostic Explanations – LIME in Python
Using LIME (Local Interpretable Model-agnostic Explanations) in Python to provide visual explanations of your classification and regression models.
Agile Marketing Based on Analytical Data Insights: Improving Scrum Tactics in Brand Outreach
Mathias Lanni highlights how to improve Scrum Marketing Management using smart data collection for better brand outreach.
Are your machine learning models good enough?
Guidance for non-technical people on how to ask the right questions to evaluate whether a machine learning model is good enough for the job.
Forecasting Time Series data with Prophet – Part 4
This is the fourth in a series of posts about using Forecasting Time Series data with Prophet. The other parts can be found here: Forecasting Time Series
2017: A year in review (and a preview of 2018)
A brief look back at 2017 and a quick preview of what 2018 looks like for me.
Deep learning – when should it be used?
When should deep learning be used? the answer isn't a simple one. The answer depends on the problem, data size and number of other factors.
When it comes to big data, think these three words: analyze; contextualize; internalize
Stop thinking about big data technologies. Think of ways to 'analyze, contextualize, internalize' your data instead.
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