Azure Machine Learning Benefits & Pitfalls

Today, allow me to write something on Machine learning and give verdict on Azure Machine Learning and its pitfalls. Let’s start with what is machine learning, it’s the construction and study of algorithms that can learn from data. There are two approaches for machine learning and they are supervised learning and unsupervised learning. Decision making in ML is done through regression , classification & clustering are the decisions taken in ML to solve problems.

Some examples for supervised and unsupervised are given below for you to understand.

In unsupervised learning, data points have no labels associated with them. Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its structure. This can mean grouping it into clusters or finding different ways of looking at complex data so that it appears simpler or more organized.

  1. To identify patterns in data – unsupervised learning
  2. Study the past – unsupervised learning
  3. Learning from the historical data to Predict / Recommend – Supervised learning

A supervised learning algorithm looks for patterns in those value labels. It can use any information that might be relevant—the day of the week, the season, the company’s financial data, the type of industry, the presence of disruptive geopolitical events—and each algorithm looks for different types of patterns. After the algorithm has found the best pattern it can, it uses that pattern to make predictions for unlabeled testing data—tomorrow’s prices.

Supervised learning is a popular and useful type of machine learning. With one exception, all the modules in Azure Machine Learning are supervised learning algorithms. There are several specific types of supervised learning that are represented within Azure Machine Learning: classification, regression, and anomaly detection.

The steps are below –

  1. Plan data storage , setup Environment and Preprocess data happens out side the ML system
  2. Setting up environment includes preparing Storage environment , pre processing environment and ML Workspace
  3. HDInsight can be used for preprocessing the data

Microsoft Azure Machine Learning, a fully-managed cloud service for building predictive analytics solutions, helps overcome the challenges most businesses have in deploying and using machine learning.

Now comes the pros and cons –

Benefits –

  1. No data limit for pulling data from Azure storages and hdfs system.
  2. Azure ML is a much friendlier set of tools, and it’s less restrictive on the quality of the training data
  3. Azure ML’s tools make it easy to import training data, and then tune the results
  4. On click publishing facilities make the data model published as web service
  5. Cost of maintenance is less compared to on premise analytics solutions
  6. Drag, drop and connect structures are available to make an experiment
  7. Built in R module , Support for python and options for custom R code for extensibility
  8. Security for Azure Ml Service relies on Azure security measures

Pitfalls –

×10 GB data limit for Flat file processing

×Predictive Model Mark-up Language is not supported, however custom R and Python code can be used to define a module

×There is no version control or Git integration for experiment graphs.

×Only Smaller amount of data can be read from systems like Amazon S3

Verdict – If you wish to run deep learning and need resources at times and not always, cloud is the fantastic option.

Cheers 🙂

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