David Martin, Author at Towards Data Science https://towardsdatascience.com Publish AI, ML & data-science insights to a global community of data professionals. Mon, 14 Jul 2025 22:31:13 +0000 en-US hourly 1 https://wordpress.org/?v=6.8.1 https://towardsdatascience.com/wp-content/uploads/2025/02/cropped-Favicon-32x32.png David Martin, Author at Towards Data Science https://towardsdatascience.com 32 32 There and Back Again: An AI Career Journey https://towardsdatascience.com/there-and-back-again-an-ai-career-journey/ Mon, 14 Jul 2025 22:31:01 +0000 https://towardsdatascience.com/?p=606579 A full circle moment 30 years in the making

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]]> Learnings from a Machine Learning Engineer — Part 6: The Human Side https://towardsdatascience.com/learnings-from-a-machine-learning-engineer-part-6-the-human-side/ Fri, 11 Apr 2025 18:44:39 +0000 https://towardsdatascience.com/?p=605720 Practical advice for the humans involved with machine learning

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]]> Learnings from a Machine Learning Engineer — Part 5: The Training https://towardsdatascience.com/learnings-from-a-machine-learning-engineer-part-5-the-training/ Thu, 13 Feb 2025 21:04:32 +0000 https://towardsdatascience.com/?p=597833 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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]]> Learnings from a Machine Learning Engineer — Part 3: The Evaluation https://towardsdatascience.com/learnings-from-a-machine-learning-engineer-part-3-the-evaluation/ Thu, 13 Feb 2025 21:00:06 +0000 https://towardsdatascience.com/?p=597857 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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]]> Learnings from a Machine Learning Engineer — Part 1: The Data https://towardsdatascience.com/learnings-from-a-machine-learning-engineer-part-1-the-data/ Thu, 13 Feb 2025 20:55:53 +0000 https://towardsdatascience.com/?p=597818 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 […]

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]]> Learnings from a Machine Learning Engineer — Part 4: The Model https://towardsdatascience.com/learnings-from-a-machine-learning-engineer-part-4-the-model/ Thu, 13 Feb 2025 20:53:42 +0000 https://towardsdatascience.com/?p=597858 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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]]> Learnings from a Machine Learning Engineer — Part 2: The Data Sets https://towardsdatascience.com/learnings-from-a-machine-learning-engineer-part-2-the-data-sets/ Thu, 13 Feb 2025 20:29:39 +0000 https://towardsdatascience.com/?p=597856 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 […]

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