  Machine learning (ML) is nowadays widely used for different purposes and in
several disciplines. From self-driving cars to automated medical diagnosis,
machine learning models extensively support users' daily activities, and
software engineering tasks are no exception. Not embracing good ML practices
may lead to pitfalls that hinder the performance of an ML system and
potentially lead to unexpected results. Despite the existence of documentation
and literature about ML best practices, many non-ML experts turn towards gray
literature like blogs and Q&A systems when looking for help and guidance when
implementing ML systems. To better aid users in distilling relevant knowledge
from such sources, we propose a recommender system that recommends ML practices
based on the user's context. As a first step in creating a recommender system
for machine learning practices, we implemented Idaka. A tool that provides two
different approaches for retrieving/generating ML best practices: i) an
information retrieval (IR) engine and ii) a large language model. The IR-engine
uses BM25 as the algorithm for retrieving the practices, and a large language
model, in our case Alpaca. The platform has been designed to allow comparative
studies of best practices retrieval tools. Idaka is publicly available at
GitHub: https://bit.ly/idaka. Video: https://youtu.be/cEb-AhIPxnM.
