These best governance practices involve "establishing the right policies and procedures and controls for the development, testing, deployment and ongoing monitoring of AI models so that it ensures the models are developed in compliance with regulatory and ethical standards," says JPMorgan Chase managing director and general manager of ModelOps, AI and ML Lifecycle Management and Governance, Stephanie Zhang. Because AI models are driven by data and environment changes, says Zhang, continuous compliance is necessary to ensure that AI deployments meet regulatory requirements and establish clear ownership and accountability. Amidst these vigilant governance efforts to safeguard AI and ML, enterprises can encourage innovation by creating well-defined metrics to monitor AI models, employing widespread education, encouraging all stakeholders' involvement in AI/ML development, and building integrated systems. "The key is to establish a culture of responsibility and accountability so that everyone involved in the process understands the importance of this responsible behavior in producing AI solutions and be held accountable for their actions," says Zhang. This episode of Business Lab is produced in association with JPMorgan Chase.

AI is fast becoming critical to business and IT applications and operations. Organizations have been investing in artificial intelligence capabilities for years to stay competitive, are hiring the best data scientist teams and are investing more and more in artificial intelligence and machine learning systems. However, implementing AI / ML models is not easy and the risk of failure is just around the corner. A solid methodology is needed to reduce this risk and enable companies to succeed. AI executives have been working toget more models in business for years now.

If you're a data scientist or you work with machine learning (ML) models, you have tools to label data, technology environments to train models, and a fundamental understanding of MLops and modelops. If you have ML models running in production, you probably use ML monitoring to identify data drift and other model risks. Data science teams use these essential ML practices and platforms to collaborate on model development, to configure infrastructure, to deploy ML models to different environments, and to maintain models at scale. Others who are seeking to increase the number of models in production, improve the quality of predictions, and reduce the costs in ML model maintenance will likely need these ML life cycle management tools, too. It's all technical jargon to leaders who want to understand the return on investment and business impact of machine learning and artificial intelligence investments and would prefer staying out of the technical and operational weeds.

MLOps, ModelOps, DataOps and AIOps are rapidly growing in importance as organizations look to leverage the power of artificial intelligence, machine learning and big data. Each approach allows organizations to build reliable systems that can effectively process large amounts of data quickly and efficiently. MLOps focuses on a continuous delivery cycle for machine learning models through automated pipelines, ModelOps is used to manage model development from conception to deployment, DataOps provides tools for developing efficient data processing pipelines, while AIOps is an AI-driven operations platform that helps automate IT processes such as incident resolution. All four approaches offer different advantages when it comes to managing the production lifecycle of AI products across multiple environments. The intersection of machine learning, model management, and data infrastructure is an essential element for any organization looking to leverage the power of artificial intelligence.

With the rise of artificial intelligence, machine learning and big data, organizations have become increasingly aware of the importance of MLOps (Machine Learning Operations), ModelOps, DataOps, and AIOps. Through this blog post, we will discuss the differences between these various approaches in order to better understand their individual roles within an organization. We then explore how Machine Learning, Model Management and Data Infrastructure intersect in MLOps. Finally, we discuss both the benefits and challenges when it comes to implementing these operations systems. MLOps, ModelOps, DataOps and AIOps are rapidly growing in importance as organizations look to leverage the power of artificial intelligence, machine learning and big data.

DataOps increases the productivity of AI practitioners by automating data analytics pipelines and speeding up the process of moving from ideas to innovations. DataOps best practices make raw data polished and useful for building AI models.

Organizations across industries are turning to machine learning (ML) to derive the greatest impact from their data, and maximize new opportunities that can set them apart from their competitors. However, businesses must develop a process that helps push their ML models into production and deployment while ensuring quality and consistent monitoring. ML model development and deployment are inherently challenging. According to a recent survey, it generally takes anywhere between about 30 to 90 days to push an individual ML model into production, and a year or more on productionizing. Even so, it's estimated that around 90 percent of all ML models fail to even make it to production.

Imagine that you are a leader at an insurance company. You know that artificial intelligence (AI) will give you a competitive edge and have decided to invest. You hired two brilliant data scientists, Juana and Yash. Juana develops an AI solution that scans digitized customer files, mines them for relevant information, and calculates accurate pay-outs. You project savings of over $1 million in the next 2 years, and 30% increased staff productivity.

Recently, businesses and organizations alike have been turning to ModelOps to solve business challenges. The framework of ModelOps works in effort to reduce manual work and increase productivity all around. Find out why ModelOps is in-demand and how SAS can help you propel in this growing area. A variation of DevOps, ModelOps is primarily focused on the management of operationalized artificial intelligence and decision models, from machine learning and optimization to linguistic and agent-based models. ModelOps is crucial for predictive analytics and allows quality analytics models to move through development to deployment as quickly as possible.

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? Artificial Intelligence represents a set of studies and techniques, typical of information technology but with significant philosophical and social implications, which has as its purpose the realization of programs and technological systems capable of solving problems and carrying out tasks normally attributable to the mind and human capabilities. Given recent progress, it is possible to identify Artificial Intelligence as the discipline that deals with creating machines (hardware and software) capable of operating autonomously. The growing attention created in this discipline is motivated by the results that can be achieved thanks to the technological maturity achieved, both in the computational calculation and in the ability to analyze in real-time and in a short time of huge amounts of data in any form [Big Data Analytics].