Naïss Haïr

Data Scientist, Student for life, opinions my own, @inphbpolytech @polytechnique @enseao alumni

[Curious Minds @CuriousMindsHub]

3 years of progress in 90 days.

[Python Programming @PythonPr]

SQL Joins Cheatsheet

[Steve Burns @SJosephBurns]

"We don't start with models. We start with data. We don't have any preconceived notions. We look for things that can be replicated thousands of times". It's all mathematics, essentially mathematics". 

- Jim Simons

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

How to build AI Agents 📚📘

[Math Files @Math_files]

Be greater than average

[JNS @_devJNS]

this is Jean-Baptiste Kempf, creator of VLC media player. he refused tens of millions of dollars in order to keep VLC ads-free and gave it to us for free. absolute legend!

[Naïss Haïr @NaissHairOff]

@lfc_lance Gonzalo Higuaín

[Naïss Haïr @NaissHairOff]

@realmadriden The only one, the Greatest of All Time @Cristiano

[𖣂JIK𖣂 @OS3992]

Life moves like the wind swift, unseen, and unpredictable. Cherish those who care for you, your family, your friends, your mother. Do not take them for granted, for the next moment is never promised. Appreciate them now, while you still can.

[Sumanth @Sumanth_077]

Google just released a python library for data extraction!

LangExtract is a python library that extracts structured information from unstructured text documents with precise source grounding and interactive visualization.

100% Open Source

[Sumanth @Sumanth_077]

Build a Large Language Model from scratch!

This repository contains the code for developing, pretraining, and finetuning a GPT-like large language model.

100% Free & Open Source

[ℏεsam @Hesamation]

This is truly the AI Engineer's TOOLKIT you need:

A list of 120+ libraries and frameworks categorized by application. When starting a new project (training, agentrs, RAG, etc.) you can just check out the available libraries and make sure you're working with the best one.

[Radio Al Bayane @radioalbayane]

Les heures de fin du Sahour et de rupture du jeûne, du 1er au 2 Ramadan 1446 Hégire

#ramadan2025 #ramadanalbayane #sahour #iftar

[OPPSCIENCE @OppScience]

Les forces de l'ordre sont confrontées à un défi majeur : passer au crible d'énormes quantités de #données pour élucider rapidement les crimes et repérer les menaces.
Comment les #technologies actuelles peuvent aider les #enquêteurs ?
@Forbes @forbes_fr @ForbesTech

[Naïss Haïr @NaissHairOff]

#MlOps

[Aurimas Griciūnas @Aurimas_Gr]

What does an 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 look like?

MLOps practices are there to improve Machine Learning Product development velocity, the biggest bottlenecks happen when Experimentation Environments and other infrastructure elements are integrated poorly.

Let’s look into the properties that an effective Experimentation Environment should have. As a MLOps engineer you should strive to provide these to your users and as a Data Scientist, you should know what you should be demanding for.

𝟭: Access to the raw data. While handling raw data is the responsibility of Data Engineering function, Data Scientists need the ability to explore and analyze available raw data and decide which of it needs to be moved upstream the Data Value Chain (2.1).

𝟮: Access to the curated data. Curated data might be available in the Data Warehouse but not exposed via a Feature Store. Such Data should not be exposed for model training in production environments. Data Scientists need the ability to explore curated data and see what needs to be pushed downstream (3.1).

𝟯: Data used for training of Machine Learning models should be sourced from a Feature Store if the ML Training pipeline is ready to be moved to the production stage.

𝟰: Data Scientists should be able to easily spin up different types of compute clusters - might it be Spark, Dask or any other technology - to allow effective Raw and Curated Data exploration.

𝟱: Data Scientists should be able to spin up a production like remote Machine Learning Training pipeline in development environment ad-hoc from the Notebook, this increases speed of iteration significantly.

𝟲: There should be an automated setup in place that would perform the testing and promotion to a higher env when a specific set of Pull Requests are created. E.g. a PR from feature/* to release/* branch could trigger a CI/CD process to test and deploy the ML Pipeline to a pre-prod environment.

𝟳: Notebooks and any additional boilerplate code for CI/CD should be part of your Git integration. Make it crystal clear where a certain type of code should live - a popular way to do this is providing repository templates with clear documentation.

𝟴: Experiment/Model Tracking System should be exposed to both local and remote pipelines.

𝟗: Notebooks have to be running in the same environment that your production code will run in. Incompatible dependencies should not cause problems when porting applications to production. It can be achieved by running Notebooks in containers.

Did I miss something? 👇

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𝗗𝗼𝗻’𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗹𝗶𝗸𝗲 💙, 𝘀𝗵𝗮𝗿𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝗲𝗻𝘁!

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[Naïss Haïr @NaissHairOff]

@ARb_sarahh 😂😂😂 Heureusement que la vie n’est pas une course, sinon 😂😂😂😂😂😂😂

[Naïss Haïr @NaissHairOff]

@SafariDjema Aïd mubarak 🎉

[Naïss Haïr @NaissHairOff]

Next goals 🦾

[Vala Afshar @ValaAfshar]

1 read books and journals
2 write blogs
3 start a podcast
4 join a community
5 publicly present ideas
6 learn to code
7 learn to invest (time and money)
8 earn a work sponsor
9 be a mentor
10 take free online courses

Do any 3 of these really well, and good things will happen.

[Naïss Haïr @NaissHairOff]

Ton ami se bat et se qualifie contre Paris et Chelsea, toi, c’est Francfort qui t’élimine en plus chez toi 😂

Toi Barcelone, quand on parle de coupe d’Europe, tu te tais 😅