๐๐ป๐ฑ-๐๐ผ-๐๐ป๐ฑ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ช๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐
Understanding the full lifecycle of a machine learning project is essential for building robust and reliable models. While itโs tempting to simplify the process to importing a dataset, training a model, and making predictions, the reality is far more comprehensive.
As an ML engineer, your role involves defining the problem, collecting and exploring data, preprocessing it effectively, and splitting it for training and evaluation. You must carefully select the appropriate model, train it, and validate its assumptions before optimizing performance through hyperparameter tuning. Ultimately, the goal is not just to build a model but to deploy a solution that delivers value in real-world scenarios.
In my latest video, I walk through an ๐ฒ๐ป๐ฑ-๐๐ผ-๐ฒ๐ป๐ฑ ๐๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ using the ๐ฃ๐๐ป๐ฒ ๐๐ผ๐๐๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ๐๐ฒ๐, showcasing how to approach these critical stages to create a successful machine learning workflow.
๐ Watch here: ๐๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป | ๐๐ป๐ฑ-๐๐ผ-๐๐ป๐ฑ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ ๐ฝ๐น๐ฎ๐ถ๐ป๐ฒ๐ฑ | ๐ฃ๐๐ป๐ฒ ๐๐ผ๐๐๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ by Pritam Kudale :
๐๐ฐ๐ณ ๐ฎ๐ฐ๐ณ๐ฆ ๐ช๐ฏ๐ด๐ช๐จ๐ฉ๐ต๐ด, ๐ต๐ช๐ฑ๐ด, ๐ข๐ฏ๐ฅ ๐ถ๐ฑ๐ฅ๐ข๐ต๐ฆ๐ด ๐ช๐ฏ ๐๐, ๐ด๐ถ๐ฃ๐ด๐ค๐ณ๐ช๐ฃ๐ฆ ๐ต๐ฐ ๐๐ช๐ป๐ถ๐ข๐ณ๐ขโ๐ด ๐๐ ๐๐ฆ๐ธ๐ด๐ญ๐ฆ๐ต๐ต๐ฆ๐ณ: https://www.vizuaranewsletter.com?r=502twn
Letโs advance our skills and stay ahead in the evolving world of AI! ๐
What would this look like for building a RAG pipeline?