Published inTotalEnergies Digital Factory·3 days agoSome best practices in LLM-based solution development using MLflowHow to track and compare LLM artifacts — LLM is undoubtedly powerful, fun and exciting. However, during development of LLM-based solution, due to the iterative and empirical nature of this process, one can be quickly drown in a swarm of models, configurations, datasets, etc. It is thus important to have a system to keep track of the development…Llm9 min readLlm9 min read
Published inTotalEnergies Digital Factory·Feb 12Multiple shades of No-ETLAn updated view for No ETL trends — Late 2022, AWS announced the preview of a brand-new No-ETL integration from Aurora RDMS database to Redshift warehouse during their tech event re:Invent in Las Vegas. While being attracted by this news, I can not help to feel enthusiastically skeptical. The term No-ETL seems too good to be true. As…Data8 min readData8 min read
Published inTotalEnergies Digital Factory·Sep 26, 2022When lazy is good: lazy importSimple tips to boost your data science python package — You have a CLI python application, but even with a simple --help command, it would need to wait a few tens seconds. Sounds familiar? What if by changing one line of code, we can speedup 100X the startup time? Might want to learn more? …Machine Learning7 min readMachine Learning7 min read
Published inTotalEnergies Digital Factory·Jun 27, 2022Imbalanced Data ML: SMOTE and its variantsAn intuitive explanation of the SMOTE and its common variants for imbalanced data — This is the first part of an upcoming series on the effective modelling of imbalanced data for AI-based solution. In real life usecases, we often deal with imbalanced data — when numbers of instances per class are significantly different: banking fraud detection, industrial detection of failure, disease diagnosis, etc…. When…Imbalanced Dataset8 min readImbalanced Dataset8 min read
Published inTowards Data Science·Jan 31, 2021Speed up your Pandas-based analysisUnderstand the core principles through a hand-on experience — As a pythonist-datascientist, you are undoubtedly familiar to data analysis with pandas, a python package for dealing with table-like data type, similar to the canonical tools like Excel , SAS, SQL or data.table, dplyr in R. To carry out an analysis in pandas, there are more than one way to…Python8 min readPython8 min read
Published inTowards Data Science·Jan 24, 2021Boost your Numpy-Based Analysis Easily — In the right wayUnderstand and make use of Numba — a doped version of Numpy — I’ve recently come cross Numba , an open source just-in-time (JIT) compiler for python that can translate a subset of python and Numpy functions into optimized machine code. In principle, JIT with low-level-virtual-machine (LLVM) compiling would make a python code faster, as shown on the numba official website. …Python8 min readPython8 min read
Published inPython in Plain English·Jan 21, 2021How to Handle a Very Common Warning for Python Data ScientistsWhy and how to effectively deal with SettingWithCopyWarning — I crossed by this apparently harmless and annoying warning message SettingWithCopyWarning countless times. As many, I chose an easy way to ignore or just hide the message with unease. …Python4 min readPython4 min read
Published inThe Startup·Jan 16, 2021Quick Backup Your Python SessionPython for datascientist: How to pickup from yesterday. Many pythonist-datascientist fellows, if happened to cross by Matlab on the way, might miss a handy feature there for saving workspace. You did some analysis, had an bunch of intermediate results that you want to temporally backup to reuse later on without…Python5 min readPython5 min read
