Working in data science, it can be hard to share insights from complex datasets using only static figures. All the facets that describe the shape and meaning of interesting data are not always captured in a handful of pre-generated figures. While we have powerful technologies available for presenting interactive figures — where a viewer can rotate, filter, […]
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As data continues to grow in importance and become more complex, the need for skilled data engineers has never been greater. But what is data engineering, and why is it so important? In this blog post, we will discuss the essential components of a functioning data engineering practice and why data engineering is becoming increasingly […]
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In this fifth part of my series, I will outline the steps for creating a Docker container for training your image classification model, evaluating performance, and preparing for deployment. AI/ML engineers would prefer to focus on model training and data engineering, but the reality is that we also need to understand the infrastructure and mechanics […]
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In this third part of my series, I will explore the evaluation process which is a critical piece that will lead to a cleaner data set and elevate your model performance. We will see the difference between evaluation of a trained model (one not yet in production), and evaluation of a deployed model (one making real-world predictions). In Part 1, […]
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It is said that in order for a machine learning model to be successful, you need to have good data. While this is true (and pretty much obvious), it is extremely difficult to define, build, and sustain good data. Let me share with you the unique processes that I have learned over several years building […]
The post Learnings from a Machine Learning Engineer — Part 1: The Data appeared first on Towards Data Science.

In this latest part of my series, I will share what I have learned on selecting a model for image classification and how to fine tune that model. I will also show how you can leverage the model to accelerate your labelling process, and finally how to justify your efforts by generating usage and performance […]
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In Part 1, we discussed the importance of collecting good image data and assigning proper labels for your image classification project to be successful. Also, we talked about classes and sub-classes of your data. These may seem pretty straight forward concepts, but it’s important to have a solid understanding going forward. So, if you haven’t, please […]
The post Learnings from a Machine Learning Engineer — Part 2: The Data Sets appeared first on Towards Data Science.

I am sure the quantum hype has reached every person in tech (and outside it, most probably). With some over-the-top claims, like “some company has proved quantum supremacy,” “the quantum revolution is here,” or my favorite, “quantum computers are here, and it will make classical computers obsolete.” I am going to be honest with you; […]
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