If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
MLOps is a relatively new concept in the AI (Artificial Intelligence) world and stands for "machine learning operations." Its about how to best manage data scientists and operations people to allow for the effective development, deployment and monitoring of models. "MLOps is the natural progression of DevOps in the context of AI," said Samir Tout, who is a Professor of Cybersecurity at the Eastern Michigan University's School of Information Security & Applied Computing (SISAC). "While it leverages DevOps' focus on security, compliance, and management of IT resources, MLOps' real emphasis is on the consistent and smooth development of models and their scalability." The origins of MLOps goes back to 2015 from a paper entitled "Hidden Technical Debt in Machine Learning Systems."
Its about how to best manage data scientists and operations people to allow for the effective development, deployment and monitoring of models. "MLOps is the natural progression of DevOps in the context of AI," said Samir Tout, who is a Professor of Cybersecurity at the Eastern Michigan University's School of Information Security & Applied Computing (SISAC) .
When I was a small child, my father was used to repeat: "Anything is simple if you know how to do it". Nowadays, I'm convinced not only that he was right, but also that the meaning of his words is a little bit more complex than what can initially appear. In this period, I keep on reading posts about automated Machine Learning. They emphasize simplicity and, clearly, attract many people who don't want to invest a lot of time in learning what has already been fully engineered. From this viewpoint, this is absolutely a reasonable choice.
Totally, open-source, and made by the same development team that created the popular DVC (data version control) library, CML (Continous Machine Learning) library is a great tool that can be used to automate Machine learning workflows, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets. This brings the power of DevOps to ML or MLOps. CML is built with the objective of bringing ML projects, and MLOps practices in a way such that it should be built on top of traditional engineering tools and not as a separate stack. This could be the future of MLOps.
In 2019, organizations invested $28.5 billion into machine learning application development (Statistica). Yet, only 35% of organizations report having analytical models fully deployed in production (IDC). When you connect those two statistics, it's clear that there are a breadth of challenges that must be overcome to get your models deployed and running. The following paragraphs will give you deeper insight into these challenges and how you can overcome them. No matter what stage of machine learning development you're in, if you are working with point solutions or siloed toolsets you're creating vulnerabilities for your models and your business.
Machine learning (ML) is gaining momentum across a number of industries and scenarios as enterprises look to drive innovation, increase efficiency, and reduce costs. Microsoft Azure Machine Learning empowers developers and data scientists with enterprise-grade capabilities to accelerate the ML lifecycle. At Microsoft Build 2020, we announced several advances to Azure Machine Learning across the following areas: ML for all skills, Enterprise grade MLOps, and responsible ML. New enhancements provide ML access for all skills. Data scientists and developers can now access an enhanced notebook editor directly inside Azure Machine Learning studio.
The key to delivering consistent business value with AI is to employ operational machine learning workflows that fully integrate machine learning models into standard enterprise processes in a reliable and repeatable fashion. That's where MLOps comes in. "There are fundamentally two things enterprises can do with machine learning: One is to make processes more efficient, and the other is to launch new products and features," says Piero Cinquegrana, data scientist and co-author of O'Reilly's "Machine Learning at Enterprise Scale." These processes could be sales process, marketing measurement, operations, and tasks that are repeatable and automatable--all kinds of what Cinquegrana calls domains. "Some classic use cases are measurement, such as scoring leads for sales so that sales account executives don't have to cold call a long list of unqualified leads," he says.
Cloudera is betting that it can fuel future growth by becoming critical to deploying, managing and governing machine learning models across enterprises and industries. The company said its Cloudera Machine Learning MLOps suite is now generally available. The effort goes along with its Cloudera Data Platform (CDP) and plays into the company's plan to become more than a Hadoop distribution player. Cloudera merged with Hortonworks last year and set a strategy to manage analytics workloads. The general theory is that Cloudera can be a single pane of glass for multiple data analytics workloads using various Hadoop open source tools.
What if there was a better way? Machine Learning Operations (MLOps) will get your AI projects out of the lab and into production where they can generate value and help transform your business. In this installment of four Data Science Central Podcasts on MLOps, we explore best practices in Production Model Monitoring.
Massive investments in data science teams and machine learning platforms have yet to yield results for most companies. The last mile for AI project success is the deployment and management of models in production requiring new technology and practices. This new area is called Machine Learning Operations or MLOps.