Source: analyticsinsight.net
How to add value to businesses with the help of an AI Factory?
Like a physical factory creates physical products reliably at scale and speed, an artificial intelligence (AI) factory delivers AI solutions for businesses at scale and speed. An AI factory combines data, people, process, product, and platform to move beyond science experiments and deliver AI that drives business value. The AI factory builds on the principles of the AI Ladder, which describes the importance of creating solid information architecture for sustained AI success. It combines DataOps, ModelOps, and MLOps to stimulate AI innovations to market.
How an AI Factory Works
Quality data obtained from internal and external sources train ML algorithms to make predictions on specific tasks. In cases like diagnosis and treatment of diseases, these predictions can help human experts with their decisions. In content recommendation cases, ML algorithms can automate tasks with little or no human intervention.
The algorithm and data-driven model of the AI factory allows companies to test new hypotheses and make a change that improves their system. It could be new features added to an existing product or new products built on top of what the company already owns. In turn, these changes enable the company to obtain new data, improve AI algorithms, and again find new ways to increase performance, create new services and products, grow, and move across markets.
How AI Factories add Value to Businesses
In many ways, building a successful AI company is as much a product management challenge as an engineering one. Many successful companies have figured out building the right culture and processes on long-existing AI technology instead of fitting the latest developments in deep learning into an infrastructure that doesn’t work. Let’s see how an AI factory helps businesses to grow at scale.
The AI Factory begins with Centralised Governance
The idea is to pool and coordinate investment and steering efforts. Only a small number of companies’ highest-value projects will be examined by those sponsors most engaged in their success. The selection of these use cases must be extremely rigorous. No project specifically should see the light if it doesn’t respect the simple law of 10X (offer a 10:1 return on investment). The success and impact of each use case should be measurable as per a simple and understandable KPI. And the systematic improvement of this KPI the most crucial reason for the teams.
Lean AI
Lean AI is a methodology that reduces the uncertainty of efficiency and applicability of AI solutions. Models are never perfect and must be examined in real-world situations. The method contains a continuous improvement loop of short cycles which include the formulation of hypotheses, the identification of pertinent data, the construction and testing of one or more models, followed by deployment on a test perimeter, and collection of user feedback.
The cycle is repeated with the formulation of new hypotheses, new data, etc. This technique enables testing in real situations, then the improvement of cases not explored, until reaching a level of satisfaction considered acceptable by the organization to begin production.
Important Ethical Challenge
The recent example of Alexa and the unpleasant surprise of her listening have been noticed. Regulations will always lag behind technology. It is important that those enterprises that employ AI understand the ethical challenges of these solutions. Seven guiding ethical principles that were published by the Committee of Independent Experts mandated by the European Commission, includes AI at the service of humanity, trustworthiness, which respects private data, transparent, non-discriminatory, dedicated to the improvement of the common good, and finally, with a clearly defined human responsibility.
People lead to the Success of an AI Factory
An AI factory requires a team of people with a variety of skills, roles, and responsibilities to be successful, just like a physical factory. AI development traditionally involves cross-functional or full-stack technical teams. It is essential to consider the AI factory not just as a technical shop but as a market-driven business. In designing an AI factory, all jobs of AI and IT stakeholders, data scientists, data journalists, IT support, business analysts, marketing, and sales need to be done. Assigning people with clear ownership, roles, and responsibilities will add value to a business.