Improve Quality of Your Data by Applying These Checks in Your Journey — Imagine you have just delivered a fancy new Data Lake with some cool Data Pipelines that bring data from all over your organisation. Now imagine your dismay when your business teams realise the data in the Lake is all garbage. Remember the adage: garbage in, garbage out. This isn't necessarily…

Label your data programmatically — Table of Contents: · Exploratory Data Analysis · Keyword Labeling Functions · Heuristic Labeling Functions · Labeling Functions with spaCy · Combining Labeling Function Outputs · Training a Classifier · Wrapping Up There was a radical idea to entirely eliminate hand-labeling any training data in machine learning projects. It birthed…

In recent years, trust and consideration of ethical rules have become an essential part of the AI landscape. Early abuses have led legislators to regulate the industry after the fact. This article discusses the ethical issues of conversational assistants considering these new regulations. — On September 28, 2018, the Governor of California approves Chapter 6 ordering that Bots must make it clear that they are just software.

Understanding how the "reverse" convolution works — Introduction Transposed convolutions are like the "ugly duckling" of the convolutional family. They are quirky and weird, but there's more to a transposed convolution than it meets the eye. You will often find layers of transposed convolutions in the decoder part of AutoEncoders, or in the generator part of GANs. Their…

Final insights (with lecture notes) from the Machine Learning Engineering for Production (MLOps) Course by DeepLearning.AI & Andrew Ng — Realizing the potential of machine learning (ML) in the real world goes beyond model training. By leveraging the best practices of MLOps, teams can better operationalize and manage the end-to-end lifecycles of ML models in a sustainable manner. In this final article of the 4-part MLOps Specialization series, I summarize…