Accelerate AI and ML Workloads with Modern Infrastructure

The success of Machine Learning and Artificial Intelligence is evident from the massive amount of data. But, it is also essential to manage such huge quantity of data and also gain insights from as fast as possible.

FREMONT, CA: The generation of data from technologies like IoT, machine learning (ML) or artificial intelligence (AI), has opened up a tremendous opportunity for many corporate houses. The opportunities helped the organizations to expand their business into new markets.

However, it is also essential to protect such a massive amount of data, and industrial sectors are urgently searching for ways to do that. The problem of data management is particularly acute in the area of Artificial Intelligence (AI) and Machine Learning (ML).

Storage System Cannot Keep Up with Data

The immense growth of data is putting the IT organizations under extreme pressure to stay responsive to their businesses. As organizations have to deal with unstructured data, so many corporations are trying to scale-out storage system where they can expand the capacity by adding a new connected device.

Storage systems were created to solve the problem of handling data, but they do not have the high speed for feeding data into compute resource, nor can they scale petabytes of capacity during performance counts. Organizations also have to invest heavily on more infrastructures by increasing their expenditure to keep up with the increase in performance and capacity requirements.

The key challenges that are faced by storage and data center facilities are given below.

1. Driving Data Centre Agility

As mentioned before, the increasing size of data and the growing urgency among the organizations to gather them is creating a challenge to store, protect, and process them. The storage that is created cannot perform or manage the data available.

2. Accelerate data transformation for AI and ML workloads

The modern analytics workloads, especially machine learning and deep learning, have transformed the procedure of the usage of data needs in an organization. The new workload needs extensive data sets, faster and parallel access to the data, and algorithms for training.

Storage systems solutions cannot gain benefits of the high bandwidth networks, so organizations prefer parallel file systems that will offer high performance and support multiple data.