Monday, July 15, 2024
Your daily dose of coffee
"Physical fitness is not only one of the most important keys to a healthy body, it is the basic of dynamic and creative intellectual activity."
— John F. Kennedy (1917-1963), 35th Us President

Differences between Supervised and Unsupervised Learning

by: ExpertAI — Today, let's talk about the differences between supervised and unsupervised learning in machine learning. Supervised learning is a type of machine learning where the model is trained using labeled data, meaning that the training data includes input features as well as corresponding output labels ...

Continue reading...

Artificial Intelligence

EV - Segmentation of images

May 10, 2023

Une application Flask avec une API de prédiction, déployée sur le Cloud

Open in a new tab
Cyber Security

Exploits Search Engine

Updated on Nov 07, 2023

This is a wider card with supporting text below as a natural lead-in to additional content.

Continue reading

Bullz-Eye [+]

Projects

Responsive image

Its a powerful Search Engine of Exploits ...

A Search Engine of Exploits
Responsive image

Contributed to 2 GitHub Archive Program.

Arctic Code Vault Contributor

AI - Projects

Responsive image

Artificial Intelligence has the potential to change the way humans interact and behave with the existing or the new phenomenas of the world ...

p7 - MLOps and comparison of different models
Responsive image

Une application Flask de présentation des résultats qui consomme l’API de prédiction, déployée sur le Cloud (Azure, Heroku ...

p8 - Participez à la conception d'une voiture autonome (Heroku)
Responsive image

La présentation d’exemples d’images du dataset, selon les éventuelles catégories etc., de leur comptage et de leur transformation ...

p10 - Monocular Depth Estimation (Heroku)

ExpertAI

Bolt from the blue ...

  • Irfan Toor (OpenClassrooms | Projet No. 7)
  • keywords : MLOps, MLFlow, traçage, organisation, modèles m/l, optimisation, hyper-paramètres, versioning, autolog, grid-search, reproduction, erreurs, package, partage, model simple, LSTM, BERT, transfer-learning, comparaison, embeddings, word2vec, Glove.6B, TweetEval, roBERTa, sentiment, sst-2, analysis

A B S T R A C T

"Je crois qu'on ne perdure pas si on perd les repères."
-- David de Rothschild

Artificial Intelligence has the potential to change the way humans interact and behave with the existing or the new phenomenas of the world. The automatic collection of data, it's cleaning, processing or feature engineering and the ever increasing compute-power are improving the existing models at an exponential rate. More inclined we are to this ever changing environment and it's associated dependencies, which are changing at a phenomenal rate, more we are prone to the risks associated with finding ourself in the unknown lands and the territories of the machine control, thereby falling prey to this self invented trap and to an ultimate oblivion.

AI is not a new technology, which is taking the world by storm but its the logical evolution of what we have already been creating through machine learning models. We, the humans, have understood after the experience of millenniums that keeping track of whatever we are doing is fundamental to our boundless potential, flawless evolution and behemoth success in every domain we touch. So, its quite natural that we transfer and apply the knowledge and the concepts learned in one domain to another, which could not have been possible without tracking every bit of our experiments -- be it psychology or science.

The concepts of DevOps have found their way into the M/L world, not only facilitating the versioning, logging and tracking of models but are extended into keeping track of the data, the parameters and the performance of these models as well. This concept of tracking and managing all of the life-cycle of M/L models, the associated data, their versioning, performance and parameter tracking and even staging from development to deployment is handled by Machine/Learning Operations i.e MLOps. We will discuss about MLOps, its deployment, and its usage through the comparison of three models namely: Simple, LSTM and BERT.


1. I N T R O D U C T I O N

1.1 MLOps - Experiment tracking

What is MLOps?

MLOps is to Machine Learning models, as DevOps is to development. Since the requirements for Machine Learning are more than that of the Development, the MLOps have more to manage and handle as compared to DevOps. Its not about keeping track of the code, the dependencies or staging alone, but that of the data versioning, hyper-parameters, performance metrics also.

Furthermore the life cycles of the models, their different versions, development, staging and deployment etc. are all managed by a combination of tools, protocols, policies and practices known as MLOps.

How an MLOps paradigme helps us?

MLOps paradigm gives us the following advantages:

  • For reproducibility of errors or the state of models
  • Organisation of different models, data, hyper-parameters and the results etc.
  • Optimisation of models/hyper-parameters
  • Automatic logging
  • Consistance between the standards of logging

Why the other classic techniques like spread-sheets are not so effective?

  • Possibility of incorporating errors due to its manual logging nature.
  • No standards have so far been established regarding the spread-sheet logging
  • Different or inconsistant logging is prone to erroneous results.
  • Problems of interpretability of different columns or values might arise over time.

1.2 MLFlow - Manage the complete life-cycle of your M/L Models

What is MLFlow?

MLFlow helps in managing the complete life cycle of an M/L Project.

In a nutshell MLFlow is the combination of tools or techniques to give the AI or M/L Engineers, the possibility to not only manage their models, and their data, but to easily fine tune these models and play with different versions and are capable to be deployed at different places with different policies or parameters.

It helps track the results by not only automatically logging the metrics, the parameters and even the models but it gives you the flexibility to compare different models on the basis of different parameters or performance, not only in the tabular form but in the graphical format as well.

It can be easily integrated into any existing models easily and provides with a web interface, where the different M/L models can be comprehensively analyzed and compared to other models. Recently, certain M/L Model integration code has also been included in mlflow, facilitating the auto-logging of a lot of regression or logistic models.

The four components offered by MLFlow:

  • MLflow Tracking -- Record and query experiments i.e the code, data, parameters and the results
  • MLflow Projects -- Packaging ML code, reproducible to share, transfer to diff stages
  • MLflow Models -- Deploying models from ML Libraries in diverse environments
  • Model Registry -- Central model repository to manage versioning, annotation and discovery

    Note : MLFlow is library-agnostic

Continue reading ...

By: Irfan TOOR

1.1 Abstract

Cet projet consiste à la recherche et développement concernant la conception d’une voiture électronique. Différente équipe sont attribué les différentes tâches dont notre équipe est attribuée la segmentation des images. Le pipeline complet consiste aux étapes suivantes :

  1. Acquisition des images en temps réel
  2. Traitement des images
  3. Segmentation des images
  4. Système de décision

1.2 Introduction

  • Concevoir un premier modèle de segmentation des images qui viennent des caméra embarquées.
  • Nombres des catégories à identifier sont limitées à 8 catégories principales et pas 32 catégories.
  • Création d’une API : basé sur Flask ou FastAPI, déployé sur cloud (ex: sur Azure, Heroku, PythonAnywahere etc.) et qui peut prendre une image et renvoie la segmentation (mask prédit par le modèle entrainé).
  • Création d’une application web (déployée sur cloud), qui va servir comme une interface pour tester l’API.
  • Il faut utiliser Keras comme une plateforme commune.

Continuer à lire ...

Billing Policy

  • You can use credit/debit cards, paypal for payments
  • We are trying to create a liason with the international gateways to make the process of all your transactions as much fast and smooth as possible.
  • Some times the transactions are not free and are charged by your banks, so for the rates kindly consult your financial institutes as wells.
  • If you are not satisfied with our services, you can initiate an immediate refund request.
  • The amount will be refund after a short verification/inquiry.

Cookies Policy

  • We do not push any cookies to the browser for general visitors.
  • For subscribers, only session cookies are pushed to the navigator.
  • We do not keep session cookies and are removed shortly after the session is logged out or expired.
  • If you observe that cookies are being pushed from our site, then probably your connection is not safe, or you are using a tracking proxy or VPN service.

Privacy Policy

This policy defines Irfan TOOR's main commitments regarding the protection of personal data, the activity of which is intended for professionals.

Continue reading ...

About

Irfan TOOR

Irfan TOOR is an Engineer (Civil + AI), and an Innovator, passionate about the Information Technology, Digital Transformation, Cyberspace and Cybersecurity.

He obtained his first degree in Civil Engineering from UET (1993), and obtained his second degree of Engineering in Artificial Intelligence (Master 2) from OC (2023).

In his own words: "I love coding, optimizing and at times re-inventing."
— Irfan TOOR

continue ...

Recent posts