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Perspectives of Deep Learning and Big Data Analysis

April 09, 2018

Perspectives of Deep Learning and Big Data Analysis

This article will be discussing several data platforms in order to explore the future perspectives of deep learning and big data analysis. With development of artificial intelligence and deep learning, there should be more innovative ways to extract competitive differentiation from various data. Deep learning will be developing to the certain direction, when all current processing requirements and data sources become a fraction. If deep learning infrastructure will be changed at the time of deployment, this could influence companies’ business and put them behind other competitors. In order to have better answers and faster data, there are five crucial trends that must be considered for developing deep learning and big data analysis.

 

  1.    Load and Feed AI Platform

 

Those Deep Learning compute systems which are enabled to Graphics processing unit (GPU) will be leading, although extra storage systems will help to maximize answers per day. The correct storage platform will help GPU cycles to get quickly response from storage. Usually, AI compute systems have 4 to 8 GPUs together with high-end networking.

 

  1.    Create a Powerful Capability to Deal with Growing Scaling of Data Feeds

 

Collecting data into a central repository will become a crucial factor in building a special source where Deep learning model can run once it is ready for production.  It will help to quickly write performance or copy from large sources at huge scale. Storage systems should help to perform writing as fast as reading. Data sources improve all data gathering demands and machine learning compute platforms.

 

  1.    Having Flexible and Fast Access to Data

 

For AI storage platforms flexibility feature has many influencing factors.  Integration, transformation, splitting and handling large datasets are very important for Deep Learning in order to send those data through neuronal networks. Storage platform should support strong memory performance and fast access. It is useful to move between structured and unstructured data. Flexible data should be able to scale in many areas like performance, capacity, integration ability and responsiveness.      

 

  1.    Scale Simple and Productive

 

Scalability is measurable based on performance and functionality. In order to have a successful AI program, it should be designed with a few terabytes data and then very easily switch to petabytes without changing the whole system. It is recommended to optimize the use of storage media depending on workload. Models usually have some issues on data management, data movement, losing their architecture and can not manage data efficiently. So it’s better to start with small and then enhance your scaling strategy accordingly.  



  1.    Find a Seller Who Understands Whole Environment Including the Storage

The most important factor is successful delivery performance to the AI application. Although, how fast storage is able to push out data is also one of the key factors. When seller chooses the storage platform, he/she must understand that integration and support services can take over the whole environment and deliver results very fast. The seller or vendor must deliver high-performance solutions and work closely with you as your AI requirements developer.



Taken together all trends discussed above, you may have a complete idea about the most important platforms of deep learning and big data analysis. To have complete answers and faster data, more features will be developed in the future and companies that want to lead a tough competition should pay attention and take in account all coming trends.



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