Azure Machine Learning Workflow

-


The process of creating a Machine Learning on Azure  is composed of a many pattern of workflow steps.
This workflow are designed to help users to create a new predictive alanytics in no limited time.
The main steps in the process are summarized in this figure :

  • Data : Is your input, who will be acquired, compiled , analyzed, tested and trained
  • Create the model : There is a versious algorithms of machine learning. we should create a models who is capable to make predictions, based on interfaces about the datasets.
  • Evaluate the model : This step is so important in the process because we examine the accuracy of new predictive models based on ability to predict the correct outcome, when we know in advance the input and the output values. Accuracy is measured in terms of confidence factor approching the whole number one
  • Refine and evaluate the model : After evaluating the model we refine-it by comparing, contrasting and combining alternate predictive models to find the right cobinations
  • Deploy the model : Consist to expose the predictive model as a scalable cloud web service, that is easily accessible over the internet
  • Test and use the final model : Implement the predictive model web service, in test or pré-prod environnements, later we make a manual or an automatic feedback loops for continuous improvement of the model by capturing the appropriate details when accurate or inaccurate predictions are made.


Commentaires

Enregistrer un commentaire

Posts les plus consultés de ce blog

Naive Bayes Classification Algorithm

-
Image
You are a data scientist,  an Intelligence Artificielle engineer, or a Statisticien, this post can help you to resolve your problems You are working on a classification problem and you have generated your set of hypothesis, created features and discussed the importance of variables. Within an hour, stakeholders want to see the first cut of the model. What will you do? You have hunderds of thousands of data points and quite a few variables in your training data set. In such situation, if I were at your place, I would have used ‘Naive Bayes‘, which can be extremely fast relative to other classification algorithms. It works on Bayes theorem of probability to predict the class of unknown data set. You wold like to know haw it work ? Step 1: Convert the data set into a frequency table Step 2: Create Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64. Step 3: Naive Bayesian equation to calculate the post
Lire la suite

Cross-Origin Resource Sharing and Azure Machine Learning web services

-
Image
Cross-Origin Resource Sharing (CORS) is a mechanism that allows web objects (e.g., JavaScript) from one domain to request objects (web service requests) from another domain. Cross-origin requests initiated from scripts have been subject to restrictions primarily for security reasons. CORS provides a way for web servers to support cross-site access controls, which enable secure cross-site data transfers. At the time of this writing, it should be noted that Azure Machine Learning web services do not currently support CORS requests. See the following link for more information: https://social.msdn.microsoft.com/Forums/en-US/b6ddeb77-30e1-45b2-b7c1-eb4492142c0a/azure-ml-published-web-services-cross-origin-requests?forum=MachineLearning. This CORS restriction really means that if you wish to fully exploit Azure Machine Learning web services for deployment, testing, and production for a wide variety of (web) clients, you will need to host your own server-side applications. You basically hav
Lire la suite