Machine Learning (ML) is known as the high-interest credit card of technical debt. It is relatively easy to get started with a model that is good enough for a particular business problem, but to make that model work in a production environment that scales and can deal with messy, changing data semantics and relationships, and evolving schemas in an automated and reliable fashion, that is another matter altogether. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!
Related Topics
Topic | Replies | Views | Activity | |
---|---|---|---|---|
Data Engineers vs. Data Analysts vs. Data Scientists vs. ML Engineers | 0 | 288 | August 3, 2024 | |
Search Engine for AI Tools | 4 | 903 | February 3, 2023 | |
Unveiling the hidden truths of AI | 1 | 15 | November 13, 2024 | |
Why Python for Data Science? | 3 | 940 | June 3, 2020 | |
End to End Report Development process in Power BI for beginners | 0 | 1165 | December 14, 2022 | |
Data Science for Beginners by Microsoft | 1 | 1048 | June 14, 2023 | |
Machine Learning - Principal Component Analysis | 0 | 315 | September 22, 2023 | |
Free Full DevOps Engineering Course for Beginners | 0 | 1290 | October 3, 2022 | |
Create a Large Language Model from Scratch with Python - Tutorial | 0 | 267 | December 28, 2023 | |
Power BI Modeling Made Easy | 0 | 145 | September 15, 2024 |