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A simple and interpretable performance measure for a binary classifier
Binary classification tasks are the bread and butter of machine learning. However, the standard statistic for its performance is a mathematical tool that is difficult to interpret -- the ROC-AUC. Here, a performance measure is introduced that simply considers the probability of making a correct binary classification.
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Can Edge Analytics Become a Game Changer?
Edge analytics is considered to be the future of sensor handling, and this article discusses its benefits and architecture of modern edge devices, gateways, and sensors. Deep Learning for edge analytics is also considered along with a review of experiments in human and chess figure detection using edge devices.
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Prepare for a Long Battle against Deepfakes
While deepfakes threaten to destroy our perception of reality, the tech giants are throwing down the gauntlet and working to enhance the state of the art in combating doctored videos and images.
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 The Death of Data Scientists – will AutoML replace them?
Soon after tech giants Google and Microsoft introduced their AutoML services to the world, the popularity and interest in these services skyrocketed. We first review AutoML, compare the platforms available, and then test them out against real data scientists to answer the question: will AutoML replace us?
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Hand labeling is the past. The future is #NoLabel AI
Data labeling is so hot right now… but could this rapidly emerging market face disruption from a small team at Stanford and the Snorkel open source project, which enables highly efficient programmatic labeling that is 10 to 1,000x as efficient as hand labeling?
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Scaling the Wall Between Data Scientist and Data Engineer
By Matthew T. Dearing on February 17, 2020 in Advice, Data Engineer, Data Engineering, Data Scientist, Deployment, DevOps, Machine Learning Engineer, MLflow, MLOps, ProductionThe educational and research focuses of machine learning tends to highlight the model building, training, testing, and optimization aspects of the data science process. To bring these models into use requires a suite of engineering feats and organization, a standard for which does not yet exist. Learn more about a framework for operating a collaborative data science and engineering team to deploy machine learning models to end-users.
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What Does it Mean to Deploy a Machine Learning Model?
You are a Data Scientist who knows how to develop machine learning models. You might also be a Data Scientist who is too afraid to ask how to deploy your machine learning models. The answer isn't entirely straightforward, and so is a major pain point of the community. This article will help you take a step in the right direction for production deployments that are automated, reproducible, and auditable.
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 Why Did I Reject a Data Scientist Job?
Snagging that job as a Data Scientist might not be exactly what you were expecting. Consider this advice on carefully considering job titles with what the position might really be like day-to-day.
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Observability for Data Engineering
Going beyond traditional monitoring techniques and goals, understanding if a system is working as intended requires a new concept in DevOps, called Observability. Learn more about this essential approach to bring more context to your system metrics.
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AI and Machine Learning In Our Every Day Life
The curiosity and buzz around the most talked-about technology -- Artificial Intelligence -- have experts and technophiles busy decoding its exciting future applications. Of course, the use of AI and machine learning is already pervasive in our daily lives, as we review many of these popular features in this article.
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The Death of Data Scientists – will AutoML replace them?
Why Did I Reject a Data Scientist Job?