Trends and Challenges in Cloud Computing with Deep Learning

Artificial intelligence is ubiquitous. From daily transactional tasks like online shopping to bank transactions to robotics every field is affected by it. Deep learning a part of machine learning has made its presence felt in the machine learning world. Major players like Facebook, Microsoft and Google are all using it. However, for deep learning to be effective it requires huge amounts of data. Deep learning architecture ensures many layers of the neural network. “Deep” will be useful when the depth i.e. number of layers are more in number. This requires more storage for this large amount of data needed for training.

The power requirements also increase as the tasks become computationally intensive. So the traditional computers may not work very effectively. Also this leads to more capital investments by the company. So an easier and effective way would be the use of the services provided by the cloud for performing deep learning.

Cloud Computing with deep learning

Cloud computing is Internet-based computing. Clouds are distributed technology platforms that leverage technology innovations to provide highly scalable and resilient environments. Shared resources, software and information are provided to computers and other devices on-demand, like the electricity grid. Hardware, systems software, applications are delivered as a service over the internet. The cloud provides service in the form of IaaS, PaaS and SaaS.

Cloud computing is the apt platform for deep learning analytics as the architecture provides support for Scalability, Virtualization, Storage for huge amounts of data-structured & unstructured and unlimited resources on demand. Scalability is the ability of a system, network, or process to handle a growing amount of work in a capable manner. It is the most important factor for analysis of huge datasets.

The traditional models spend a large amount of time in designing scale-up and scale-out solutions. Significant investments are made on the hardware platform. Cloud Computing removes this overhead for the Architect/Organization by providing on-demand (elastic) computing resources on the fly. Role of the architect is then reduced to finding right cloud vendor.

The cloud model offers database scalability. It is able to handle very large amounts of data and provide the input/output operations per second (IOPS) necessary to deliver data to analytics tools. It offers storage for both structured and unstructured data. Thus database scalability, distributed computing and virtualization ensure there is never shortage of storage space. The Cloud Computing model provides unlimited resources on demand.

Big data environments need clumps of servers to support the devices that process huge volumes, varied formats and high volumes of data. Clouds are set up on pools of networking resources and server storage. Thus they offer economical means to support big data technologies. Use of cloud computing for big data implementation lowers the internal processing power commitment by altering the data processing to the cloud. Provide sufficient assets to a small to medium sized companies.

Infrastructure as a service

Iaas involves taking the physical hardware and going completely virtual. e.g. all servers, networks, storage, and system management all existing in the cloud. This is the equivalent to infrastructure and hardware in the non-cloud computing method running in the cloud. This will mitigate the need for a data center, heating, cooling, and maintaining hardware at the local level.

This service model is the one which can be used for big data storage. IaaS technology ups processing capabilities by rapidly deploying additional computing nodes. IaaS enables you to allocate or buy time on shared server resources. These are virtualized, to handle the computing and storage needs for big data analytics. Cloud operating systems manage high-performance servers, network, and storage resources.

Flexibility In cloud Computing

Flexibility is allowing resources to be deployed rapidly and only as needed. Cloud computing puts big data within the reach of companies that would otherwise not be able to afford the high costs or invest the time associated with buying sufficient hardware capacity to store and analyze large data sets. Cloud computing hosts a pool of shared resources: provided to consumers. Resources such as compute, memory, network and disk (storage) are allocated to consumers of the service from a shared pool.

Rapid elasticity provided by allowing rapidly provisioning and release of resources as demand for the cloud service increases and decreases. This is done automatically. The rapid elasticity capability works economically on network, compute, memory and storage resources. This is due to these resources are highly scaled and are released when required. When projects start, the resources are allocated to consumers of the cloud service, and when the project comes to an end, these resources are released back into the cloud infrastructure resource pool.

Conclusion

The combination of advanced analytics software and the availability of cheap processing power makes the cloud a perfect place to perform analytics using deep learning. Machine Learning Is Everywhere and deep learning is the phrase from the day. The Cloud’s power is inescapable. Analysis, computation and statistics are made easier on the cloud and the workloads are highly variable. Deep learning requires heavy computing resources. It is cost prohibitive to build the infrastructure yourself and power it locally. Deep learning in the cloud can utilize the massive infrastructure available online thus the combination of these two will be feasible.