There is no doubt in saying that data is lifeline of Machine Learning and Artificial Intelligence (ML and AI). The moment these technologies get mature and proliferate apps, they produce huge amount of data. Doing so also increase the challenges of cloud storage services.

Enterprises are required to balance the cost, management ease and storage performance. It means preparing storage strategy in order to support ML and AI applications utilizing storage technologies for different kinds of data created by them. In almost every case, object storage is a major component in the data storage. Here are the reasons behind it. Take a look.

Artificial Intelligence Future Requires Unlimited Storage

Big data sets are needed to train ML and AI algorithms in order to deliver correct decisions. In turn, this drives the noteworthy storage demands. For instance, Microsoft took five years constant speech data for teaching computers talk and another renowned company Tesla is training driving to cars via 1.3 billion miles’ driving data. Data management needs storage systems which can scale limitless.

Post Artificial Intelligence algorithm is well-trained, it start producing the data. Real data set up will improve and expand via usage. In order to happen the same, data should be given framework via metadata. However, humans couldn’t manually add the context to every data, the absolute data amount will take weeks/months if a human will analyze the same. Although, AI systems can process the same within a couple of minutes. Therefore, using Artificial Intelligence for enhancing AI will boost the scalability of data storage.

Metadata

It is quite good storing data in case you can’t instantly search the required data. Search ability is empowered by metadata, this is what makes huge data volume useful.

In Machine Learning and Artificial Intelligence, metadata is a fundamental key to extract the data value. Object storage permits unlimited data description to make searching of items in the set easy. Also, it permits information regarding the unstructured data which needs to be distracted. This is needed for analytics application.

Architecture Options

Machine Learning and Artificial Intelligence learn from various data types that needs varying capabilities and performance. Systems must have correct mixture of storage skills (hybrid architecture) in order to cater immediate scalability and performance needs.

For sets of data which need to grow unlimited, parallel-access design is quite necessary. Without the same, system will cultivate bottlenecks which limit the growth of data. In addition, huge data sets sometimes need hyper scale data centers with the built-in server architectures. The deployments might benefit via pre-configured appliances’ simplicity.

Data Durability

Collecting and creating the Artificial Intelligence data sets might take years means losing the same is not a choice. However, backing up huge seta in a go could be expensive and time-consuming too. As a substitute, a few storage services come with securing capabilities which means an isolated backup is not important. Unlimited cloud storage provide customers an option in terms of protection level and enable users of striking a balance in between data protection and cost.

Cloud Integration and Data locality

Some of the ML/AI data resides within cloud; however most of them remains on in-house data centers for various reasons like cost, regulatory compliance and cost. However, in order to remain competitive, storage done on-premises should offer scalability and same cost advantages as counterpart based on cloud.

Irrespective of where information stays, public cloud integration would be a vital need for two reasons. The first is most of ML/AI innovation takes place on-premises; therefore, cloud storage services offer best flexibility. The other is everybody is more likely to witness fluid data flow to/from cloud as data is analyzed and generated. On-premises solution must have an ability of simplifying flow in between two environments in spite of limiting the same.

Cost Efficiency

Most of the storage systems are geared towards Machine Learning and Artificial Intelligence system should be affordable and scalable. These two attributes don’t co-exist always in the enterprise storage. Over all, highly-scalable systems are more costly as per the capacity/cost basis. Huge Artificial Intelligence sets of data are not possible in case they break storage budget. Often, object storage systems are made as per the basic standard of industry which results in an affordable solution.

Choices of Storage - Object Storage

All of these need means any of the working storage strategy for Machine Learning and Artificial Intelligence should include the object storage owing to the advantages of these applications. The best among these is scalability in a single namespace. In addition, object storage provides hybrid architecture and metadata abilities which natively integrates in cloud environment and offers in-built redundancy. Therefore, an isolated backup is not required for any process.

Businesses which want to stay competitive in the near future need to understand that unlimited cloud storage and ML and AI data is the largest asset to grow effectively.