"While a managed training service might be the ideal solution for many ML developers [..], there are some occasions that warrant running directly on 'unmanaged' machine instances [...]."
Chaim Rand expands in his latest post on ML engineering solutions. buff.ly/47jyFi8
Towards Data Science
63.4K posts
The world's leading publication for data science and artificial intelligence professionals.
Submit an Article ✍️ contributor.insightmediagroup.io
- Courage to Learn ML: Decoding Likelihood, MLE, and MAP by Amy Ma buff.ly/3t06Fls
- Courage to Learn ML: An In-Depth Guide to the Most Common Loss Functions - MSE, Log Loss, Cross Entropy, RMSE, and the Foundational Principles of Popular Loss Functions by Amy Ma buff.ly/3RGZ5EJ
- "With the announcement of MLX, it seems that Apple wants to make a significant leap into open source deep learning." Tristan Bilot provides a helpful benchmark to assess the performance of Apple's new machine learning framework. buff.ly/3NRtdfx
- If you're a machine learning engineer in need of an accessible introduction to log loss — including the math and theory behind it — don't miss @jrobvision's thorough explainer. buff.ly/46yLKDv
- The 10 Best Data Visualizations of 2022 by Terence Shin buff.ly/3rJPfVQ
- "If all machine learning engineers want one thing, it’s faster model training — maybe after good test metrics" by @alexdremov_me buff.ly/45skkk9
- In some instances, machine learning shouldn't, in fact, be your go-to solution. Toon Beerten explains the tradeoffs of various approaches using a signature-detection case study. buff.ly/48b07zQ
- Building scalable Kubeflow ML pipelines on Vertex AI and ‘jailbreaking’ Google prebuilt containers by Kabeer Akande buff.ly/49F9b0p
- Interested in the intersection of physics and machine learning? Don't miss Shuyang Xiang's primer on building physics-informed neural networks to formulate shock waves. buff.ly/3xlTP2G
- Dealing with MRI and Deep Learning with Python - In this post, Carla Pitarch Abaigar delves into how to align our data with the model’s requirements and how to prepare the model to process our data effectively: buff.ly/3TuUDev
- 5 Python Projects to Automate Your Life: From Beginner to Advanced by @frankandradec buff.ly/3rQ8OvV

