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Model Operations for Secure and Reliable AI

Artificial intelligence and Machine Learning are expressing incredible potential in various application fields; however, very few companies engaged in a 4.0 transition path can successfully implement these technologies in business processes. What needs to be done to make such applications profitable?

Why is AI Important?

AI is a popular branch of computer science that concerns building “intelligent” smart machines capable of performing intelligent tasks. With rapid advancements in deep learning and machine learning, the tech industry is transforming radically.

Weak and Strong AI

Machine Learning e Deep Learning

AI vs ML vs DL | Source Dataiku

Machine Learning is a form of applied statistics, aimed at using computers to statistically estimate a complex function. It is a set of techniques (such as computational statistics, pattern recognition, artificial neural networks, adaptive filtering, the theory of dynamic systems, the processing of images, data mining, adaptive algorithms, etc.) that allow machines to ‘learn’ from data and, subsequently, make decisions or make a prediction about them.

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Machine Learning: Supervised Learning Scheme | source

Types Of Machine Learning | By Giuliano Liguori
Types Of Deep Learning | By

Deep Learning and Industrial Applications

Deep Learning is a subfield of machine learning that is concerned with algorithms inspired by the brain’s Structure & functions known as artificial neural networks. A computer model can be taught using Deep Learning to run classification actions using pictures, texts or sounds as input

Artificial Intelligence in various Sectors

Marketing and Artificial Intelligence

Artificial Intelligence in HealthCare

Cybercrime and risk management

Artificial Intelligence and Supply Chain Management

Artificial Intelligence for Public Safety

Artificial intelligence and business for value creation

Operationalization: the work practices to be developed


Model lifecycle governance | Source: Forrester

A look inside ModelOps | By Giuliano Liguori

MLOps and ModelOps | Source: ModelOp

As stated by Stu Bailey, Co-founder and Chief Enterprise AI Architect at ModelOp “Understanding and valuing the distinction between ModelOps and MLOps is important because while both are needed, only one fully addresses the operational and governance process issues that are holding back nearly two-thirds of enterprise AI programs (the 2021 State of ModelOps Report).

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ModelOps Enterprise Capability | Source: ModelOp



DataOps cycle | Source

Final thoughts