Banking AI

Why you should start planning for AI scalability today

For many industries, Artificial Intelligence is increasingly becoming an unavoidable fact of life. Some organizations seek to respond to this disconcerting trend by immediate ad hoc and reactive measures. These measures often seem to address integrating automation in a certain area of operation, leaving the rest of the infrastructure unintegrated. This is where the AI adoption of organizations remains a comprehensive AI scalability planning is required to properly integration AI operations in the operational infrastructure of an organization.

Imagine if you were building a city in a simulator video game which, like the real world, would only allow you to make constructions on the terrain irreversibly. You may want to dismantle certain parts of the constructions, but those will account for heavy costs that the business cannot afford. This is how an operations infrastructure will work, which is not built with AI scalability in mind. Organizations that do not keep scalability into account often find themselves grappling with problems such as insufficient hardware expansion, limited computing resource utilization, and surging overheads. These factors make effective adoption of AI in terms of machine learning and deep learning problematic.

Build an AI-literate and adaptive culture

Organizations must build a more adaptive culture with AI-informed staff when it comes to Artificial Intelligence so that each function can offer their input in a machine learning infrastructure. Human resources in an organization do not need to be threatened by the notion of machine learning. Studies show that artificial intelligence is more likely to make the staff adapt to the operational changes as opposed to replacing them.[1]

From Silo-Centric to Platform-Centric

When it comes to manufacturing AI, most industries are not integrated in terms of machine learning across the board. This problem will be fixed when decision-makers and managers in an organization stop seeing AI devices as standalone tools attached to their existing operational infrastructure and start seeing the concepts of machine learning and deep learning as holistic methods of operation which run across organizational functions. Organizations need to shift their approach to artificial intelligence program development from silo-centric to platform-centric. Allows machines from various departments to communicate with each other, gathering, utilizing, and making use of their respective data sets.

From Staff-Driven to Data-Driven

Many of the industrial corporations still rely on experienced manpower for operational decision-making, which relies more on their gut feeling than a data-driven operation. This is where leadership in an organization needs to realize the cost-benefits of implement machine learning integrated across the operations of various functions.

The Challenge

For writing AI applications, data scientists run parts of programs in what is known as container packages built with technologies such as Docker. Open-source programs such as TensorFlows are used to develop deep learning models. However, containers and Tensorflow might help develop models but leave the data engineers at a loss when it comes to scalability. Engaging managed services to deploy these programs is a costly solution. This is where the Kubernetes comes in. Kubernetes make it possible to write various container packages as various functions or parts of complex programs on different machines to be run seamlessly as a unified program.

Kuberflow: An AI Scalability Solution of Choice

For organizations struggling to implement AI scalability, the most convenient and cost-effective solution lies in kubeflow. Kubeflowis an opensource platform that and allows the use of Kubernetes along with tools such as TensorFlow for deploying machine-learning applications. Kubeflowallows portable and deployable machine learning with the flexibility to add or remove hardware to infrastructure without dismantling the entire operation. These are a ready-made solution primarily because they resolve the problem of running ML code distributed over and can be run across various platforms such as Google Cloud and Amazon Web Services.

The Benefits

AI Scalability results in better artificial intelligence adoption. According to the Harvard Business Review, 90% of organizations with successful AI scalability spent more than half of their data analytics resources to drive adoption. It allows proactive problem solving instead of addressing customer complaints reactively and identify unhappy customers to deliver solutions which they are likely to opt for. In this way, organizations can help retain customers and help save lost businesses. Furthermore, data-driven operations can help point out problems that simply the intuition of a line manager cannot resolve, saving cost leakages that businesses did not even know existed.

If you are considering data engineering services for the AI scalability of your business infrastructure, then we at SDS could lend you a hand. If you are an industrial manufacturing corporation looking to improve the AI scalability of your operation, our data scientists and engineers can analyze your operations to efficiently formulate cost-effective solutions using tools such as microservices or SaaS.

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