Skip to main content

Getting started with Azure ml machine learning platform

Azure ml studio -getting started

Hi everybody, my name is shyambhu and I am going to provide you an introduction to Azureml and all its functionality. I will be posting details of working chances and specific details which experienced users generally do not provide. This post is normally targeted towards people who are not aware of Azureml or are just beginning to use it, but will also enrich people already working on it to find neat details and get more out of Azureml.
 First of all, we need to know what is Azureml.

What is Azure Ml?

Azure Ml , i.e. Azure machine learning studio, is a platform created by Microsoft, to provide a all around platform for machine learning without much knowledge of the original codes used behind the machine learning algorithms. In Azureml, one can get free workspace from his/her microsoft account and then can access all of its functionality.


Signing into Azure ML:

First, if you have Microsoft account its good, otherwise first open a Microsoft account using one of your existing mail account and one phone no. 
Secondly, please go to page
https://studio.azureml.net/ and on the right top corner there will be sign in option, which you have to then sign in through using your microsoft account and/or mobile no.

Homepage and basics:

Above is the picture of the first homepage. On the left there are the following options.

Experiments: 

Experiments are environments where one can execute a lot of machine learning works. There are lot of tools available, which can be dragged, dropped and connected one to another, to create a work-flow or a full-phased machine learning model. More about that will come later.

Projects: 

One or more than one experiments can be included in a project. There is a option in the bottom, named Add to project. One can select one or more experiments from the homepage of the idea-board and then add them to some existing project or a new project. This project option is important for gathering a number of experiments under one umbrella. Project can be used for long time works as well as working on a research oriented problem.

Web service:

There is the option called web service. One can create certain web services within some experiment, like prediction services and then deploy them and share them to third party via Azure Ml. These web services can be viewed and the dashboard and configuration as well as details as API keys and test versions can be seen about the web service from this option. 

Notebooks:

Whenever one uploads a data set into Azureml, one gets a bunch of functions with it. One of them is opening it in a notebook, which is currently supported in python 3, python 2 and R. The notebook are Jupyter notebook. If someone does not know about jupyter, please refer to https://jupyter.org/, which is the official documentation of the jupyter notebook. Also, one can directly go into notebook, and open a notebook without associated with any dataset. Hence one can actually work on notebooks and machine learning projects without any software downloaded on the machine.

Datasets:

One can upload dataset from local computer, internet or azure-data storage houses. These uploaded datasets get copied in the cloud-space associated for the client, and then one can see all the datasets associated to a workspace under this dataset tag. Under this dataset, there are My dataset and samples options. Azure platform comes with a lot of sample data also. One can take these sample datas and explore their features and run experiments on them. 

Trained models:

The last option on the right is trained models. All the trained models are listed under this option. One can explore a workspace and the available trained models. 

  On the top right corner there are different options available. Under the peoples' icon one can access forums. Under the smiling face sign, one can submit feedbacks. Under the questions sign one can take two actions i.e. taking a tour of Azureml platform, which includes opening a experiment, creating a model, scoring it and then at the end deploying it as a web service. Also one can visit the documentation of azureml which is created and maintained by Microsoft every year. 

+New:

This is a option to import new (1)dataset (2) notebook (3) module (4) project and (5)Experiments. These options (4) and (5) will be rather delved into details in next blogs, as modules are interesting in themselves and can lead to a lot of technical and theoretical interesting topics.

This was the intro to Azureml. In the next blog we'll explore more of experiments, the options available and the corresponding theories if necessary.

If you are more of a book person than a blog one, still follow my blog for new and exciting contents, while you can read the book below:




Labels:
Location: 28228 Co Rd F, Woodrow, CO 80757, USA

Comments

Post a Comment

Popular posts from this blog

Mastering SQL for Data Science: Top SQL Interview Questions by Experience Level

Introduction: SQL (Structured Query Language) is a cornerstone of data manipulation and querying in data science. SQL technical rounds are designed to assess a candidate’s ability to work with databases, retrieve, and manipulate data efficiently. This guide provides a comprehensive list of SQL interview questions segmented by experience level—beginner, intermediate, and experienced. For each level, you'll find key questions designed to evaluate the candidate’s proficiency in SQL and their ability to solve data-related problems. The difficulty increases as the experience level rises, and the final section will guide you on how to prepare effectively for these rounds. Beginner (0-2 Years of Experience) At this stage, candidates are expected to know the basics of SQL, common commands, and elementary data manipulation. What is SQL? Explain its importance in data science. Hint: Think about querying, relational databases, and data manipulation. What is the difference between WHERE ...
Post a Comment
Read more

What is Bort?

 Introduction: Bort, is the new and more optimized version of BERT; which came out this october from amazon science. I came to know about it today while parsing amazon science's news on facebook about bort. So Bort is the newest addition to the long list of great LM models with extra-ordinary achievements.  Why is Bort important? Bort, is a model of 5.5% effective and 16% total size of the original BERT model; and is 20x faster than BERT, while being able to surpass the BERT model in 20 out of 23 tasks; to quote the abstract of the paper,  ' it obtains performance improvements of between 0 . 3% and 31%, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks. ' So what made this achievement possible? The main idea behind creation of Bort is to go beyond the shallow depth of weight pruning, connection deletion or merely factoring the NN into different matrix factorizations and thus distilling it. While methods like know...
Post a Comment
Read more

Spacy errors and their solutions

 Introduction: There are a bunch of errors in spacy, which never makes sense until you get to the depth of it. In this post, we will analyze the attribute error E046 and why it occurs. (1) AttributeError: [E046] Can't retrieve unregistered extension attribute 'tag_name'. Did you forget to call the set_extension method? Let's first understand what the error means on superficial level. There is a tag_name extension in your code. i.e. from a doc object, probably you are calling doc._.tag_name. But spacy suggests to you that probably you forgot to call the set_extension method. So what to do from here? The problem in hand is that your extension is not created where it should have been created. Now in general this means that your pipeline is incorrect at some level.  So how should you solve it? Look into the pipeline of your spacy language object. Chances are that the pipeline component which creates the extension is not included in the pipeline. To check the pipe eleme...
Post a Comment
Read more