Cloud services provide a seamless environment for analytics and machine learning projects. The cloud comes with a large repository of data and is a perfect option for ML which contains a large amount of data to function on. Here are 7 reasons for anyone working in this domain to shift their projects on the cloud.
1. Easy to get started
It is easy for even beginners to get started with the cloud, as everything is systematic. The user only needs to sign in, create an ML project and start building solutions in any of the cloud platform products. Additionally, there are no upfront costs. For example, to test out an application that you have built using the cloud is free of cost.
2. Readily Available Tools
Not only is it easy to get started with cloud services, but once you get started, there are tools available for everything that you want to do with your project. In an analytics or ML job, one does not want just one way to carry out particular tasks but also wants to know multiple ways of doing them. Manipulation across all the products available on the cloud platform is easily possible. There are command-line interface tools that work right from the command line, no matter what OS you have. Whether it is Windows or Linux or macOS, the user can script out the command line operations available to do anything and everything. The cloud platforms come with an array of algorithms needed for ML.
The entire ML product sits on top of the API calls. So if a developer wants to send out API requests, they can use these URLs. Any of the managerial administration tasks that one has to do inside the cloud platform, he can do them right through the API.
3. Data storage for every need
The cloud platform is all about building distributed storage in the cloud. One can load big data and easily do analytics on it. Using databases in the cloud. There are very specifically focused data storage solutions available so one can mix and match the data that he wants to deal with.
4. Data Security
Data security is one of the key concerns of people moving their projects to the cloud. Sensitive data can be hacked when deployed to cloud. But many cloud providers provide encrypt the data while it is transferred on the cloud. This feature is in-built in some providers. Whereas some other use dedicated clients to provide encryption to the data on the cloud.
How much of computational power is an ML project going to take completely depends on what part of the project deployment is being dealt with. For example, during training, the processing power needed is large but for running the ML models, the processing power requirement is not much. When used in the cloud, one does not have to worry about the computational power that much. If the training data resides in the cloud, there are virtual partitions available to handle these datasets. They also can turn them off when they are not being used.
6. Data Storage
All the major ML applications need a tremendous amount of data to be actually qualified as machine learning products and give us the services that they give. For this purpose, the cloud is important where a large amount of data can be stored as the data keeps on adding up as the machine learns. All the conversational AI services are deployed on the cloud.
7. Affordable Price
Pricing is obviously a big question while making a decision about moving the ML project on the cloud or not. The company or the data scientist has to make sure that he/she is limiting the costs and that it lies within the budget. Most cloud platforms come at affordable prices. They come with a pricing calculator which allows the user to choose the product and create an estimate of the monthly cost of that product. One can always build up their estimate and figure out what your costs are going to look like on a monthly basis.
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