Cloud computing refers to on-demand computing services through the internet on a pay-as-you-go basis. The services range from applications to storage and computational power. The companies in need of these services subscribe to packages according to the need of their system architecture.
The benefit of using these services is that the company does not need to worry about the upfront cost and complexity of owning and maintaining its own IT infrastructure.
The various cloud computing services are processing power, data storage capacity, networking, web servers, database management, SDKs, CRM, business analytics, and much more. Companies in need can also get these services easily with the help of cloud computing.
Deep learning requires high computational power. The amount of dataset ingested on a deep learning model is very large. Using cloud computing for deep learning helps store these large datasets and efficiently manage the training processes. Also, cloud computing fulfills the need for deep learning models by providing high computational powers and faster training.
The multi-processing features in the cloud help models to scale efficiently. Moreover, because of the availability of larger sets of frameworks, it is flexible to train any deep learning models on cloud computing.
While deep learning applications in computer vision, natural language processing, recommendation systems, etc., are developing rapidly, cloud computing plays a vital role in their development.
Solving a Data Deficit
Data deficiency is a major problem for the large models required for deep learning. Data deficits most often occur in inaccurate systems and waste time and resources while training the models.
There are several methods to train a deep learning model. Transfer learning is one approach in deep learning where pre-trained models are used and use the weights learned by the model to build our own deep learning model on top of it.
Popular cloud services like AWS, GCS, and Azure provide GPU limits for deep learning applications. These high-end resources will help work on the pre-trained models with the amount of dataset present.
Cloud Computing provides extensive cost benefits like saving company budget and improved productivity. Some of the cost advantages of cloud computing include:
- Lower initial investment
- Decreased labor and maintenance expenses
- Higher productivity
- Lower additional charges
If a company is to maintain its private cloud, it must focus on several things like the initial investment to purchase its own servers.
Extra labor to keep an eye on and maintain the server is also required. If the systems need to scale up because of the increment in data and number of requests, additional costs might add up for buying hardware and other devices.
Moreover, the company has to maintain the server security from getting vulnerable. So, a lot of expenses might add up to the company’s budget; using cloud computing, storage and applications can be accessed by anyone who has credentials and is on a Virtual Private Network (VPN) set up by the company.
This allows remote work, which can also result in requiring less office space, lower rent, and utility payments. These are just some of the ways a cloud computing service can cut costs.
Availability of computational powers and storage are not the factors to play a primary role in improving accuracy. Factors like cleaner datasets and different model hyperparameters majorly play a role in the accuracy. Moreover, cloud computing can play a significant role in hyperparameter tuning.
As cloud services are scalable, training multiple models with several different hyperparameters or even using the ensemble models simultaneously is feasible. This allows multiple experimentations with a large set of hyperparameters; thus, the best models that justify our dataset can be chosen. In this way, the model performance can be improved.
Ultimately, cloud computing services enable businesses to experiment in a variety of learning contexts and scale up as projects are put into production and demand grows. They reduce company costs and improve productivity on an overall analysis. For this reason, due to speed, scalability, and flexibility, cloud computing services are essential for any deep learning application.
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