In our day to day life the programming concepts are changing, instead of programming a computer we can teach a computer to learn programming and work for us. ML concepts are introduced to drive unstructured data to structured manner. Once we get the data, we can easily use that data for getting useful information out of that data. So Google machine learning engine is basically a structured service that allows us to create learning models that can work on any type or size of data. We can create our models with a tensorFlow framework which powers many Google products like 'Google photo', 'Google speech' etc. This cloud machine can be deployed on any type of tensorFlow model and perform training on a cluster. It can also managing online and batch predictions. This service is managed by Google cloud dataflow for preprocessing and allow us to use data from Google cloud storage and bigquery. This machine engine allows developers to create models using cloud data lab. One can understand data, create model graphs, train their model and analyze the outcome from the model.
A system is created. We show the syatem two similar looking glass filled with different liqid and ask them to identify which glass has beer and which one is filled with wine. To answer this question that machine builds a model and this model starts a process called training. Machine learning has a goal to create an actual model and use training to answer our questions. In order to create a model we need to collect data. This data can be an image, it can be text document or music tune. In beer or wine scenario, the training data can predict whether the glass has wine or beer. The data may be like the shape of the glass, the color of drink, alcohol percentage of the liquid etc.
Services provided by Google and integrated to work together. For processing data we can use cloud data flow, for storage purpose we can use cloud storage, for model creation we can use cloud data lab. They will work together with great integration.
Google machine learning gives a scalable service by processing any type or kind of data. It do not care about the size of data. It also manages training which support CPUs and GPUs.
Machine learning engine takes few hours to build a powerful and fast performance model that can tune hyperparameter with hypertune automatically .It also create values for models quickly.
The TensorFlow SDK is an open source service. This can train a model on sample data by using Google Cloud Platform for training at big scale. We can download training engine for local or mobile integration.
This service automatically manages all the data and also monitors all resources. We just have to focus on our model development and predictions. Rest will be managed by this service.
How to launch a VM instances in Google Cloud
In this tutorial we are going to launch a Compute Egine instance.
How to install WordPress on VM instance in Google Cloud
Before you start installing WordPress on VM instance on Google Cloud. You must log in into the machine. You can launch a VM Instance on Google Cloud using this tutorial. After launch of machine log in into the machine using SSH. If you do not know how to SSH in VM instance you can follow this tutorial.
How to create a VM instance in Azure
In this Tutorial we are going to launch a Virtual Machine in azure.
How to install WordPress on VM instance in Azure
Before you start installing WordPress on VM instance on Azure. You must log in into the machine. You can launch a VM Instance in Azure using this tutorial. After launch of machine log in into the machine using SSH. If you do not know how to SSH in VM instance you can follow this tutorial.
How To Launch EC2 Machine
In this article we are going to learn how to launch a EC2 instance. For this you need to have AWS account.
How to install WordPress on ec2 machine
Before you start installing WordPress on ec2 machine. You must log in into the machine.