This article was contributed by Aymane Hachcham, data scientist and contributor to neptune.ai MLOps refers to the operation of machine learning in production. It combines DevOps with lifecycle tracking, reusable infrastructure, and reproducible environments to operationalize machine learning at scale across an entire organization. The term MLOps was first coined by Google in their paper on Machine Learning Operations, although it does have roots in software operations. Google's goal with this paper was to introduce a new approach to developing AI products that is more agile, collaborative, and customer-centric.

As pandemic-led isolation triggers an epidemic of loneliness, Japanese are increasingly turning to "social robots" for solace and mental healing. At the city's Penguin Cafe, proud owners of the electronic dog Aibo gathered recently with their cyber-pups in Snuglis and fancy carryalls. From camera-embedded snouts to their sensor-packed paws, these high-tech hounds are nothing less than members of the family, despite a price tag of close to $3,000 -- mandatory cloud plan not included. It's no wonder Aibo has pawed its way into hearts and minds. Re-launched in 2017, Aibo's artificial intelligence-driven personality is minutely shaped by the whims and habits of its owner, building the kind of intense emotional attachments usually associated with kids, or beloved pets. Noriko Yamada rushed to order one, when her mother-in-law began showing signs of dementia several years ago.

In recent years, voice-based virtual assistants such as Google Assistant and Amazon Alexa have grown popular. This has presented both potential and challenges for natural language understanding (NLU) systems. These devices' production systems are often trained by supervised learning and rely significantly on annotated data. But, data annotation is costly and time-consuming. Furthermore, model updates using offline supervised learning can take long and miss trending requests.

Robots, AI and autonomous systems are increasingly being used in hospitals around the world. They help with a range of tasks, from surgical procedures and taking vital signs to helping out with security. Such "medical robots" have been shown to help increase precision in surgeries and even reduce human error in drug delivery through their automated systems. Their deployment into care homes has also shown they have the capability to help reduce loneliness. Many people will be familiar with the smiling face of the Japanese Pepper robots (billed in 2014 as the world's first robot that reads emotions).

The complete paper discusses risk reduction and increased fabric-maintenance (FM) efficiency using artificial-intelligence (AI) and machine-learning (ML) algorithms to analyze full-facility imagery for atmospheric corrosion detection and classification. With this tool, a comprehensive and objective analysis of a facility's health is achievable in a matter of weeks from the time of data collection. This application of AI and ML is a novel approach aimed at gaining a comprehensive understanding of facility-coating integrity and external corrosion threats. Atmospheric corrosion is the most-significant asset-integrity threat in the Gulf of Mexico (GOM). Offshore facilities require constant inspection and FM--and the significant financial obligation of these activities--to stay ahead of rapid equipment degradation.

No-code, low-code (horizontal) machine learning platforms are useful at scaling data science in an enterprise. Still, as many organizations are now finding out, there are so many ways that data science can go wrong in solving new problems. Zillow experienced billions of dollars in losses buying houses using a flawed data-driven home valuation model. Data-driven human resources technology, especially when based off facial recognition software, has been shown to bias hiring decisions against protected classes. While automation is a great tool to have in your arsenal, you need to consider the challenges before utilizing a horizontal ML platform.

Removing litter from oceans and seas is a costly and time-consuming process. As part of a European cooperative project, a team at the Technical University of Munich (TUM) is developing a robotic system that uses machine learning methods to locate and collect waste under water. Our seas and oceans currently contain somewhere between 26 and 66 million tons of plastic waste, most of which is lying on the seafloor. This represents an enormous threat to marine plants and animals and to the ecological balance of the seas. But removing waste from the waters is a complex and expensive process.

Ryan Owen holds an MBA from the University of South Carolina, and has rich experience in financial services, having worked with Liberty Mutual, Sun Life, and other financial firms. Ryan writes and edits AI industry trends and use-cases for Emerj's editorial and client content. Toyota came to the United States in the late 1950s, setting up its US headquarters in California. A decade later, the Japanese automaker became the third-largest import brand in the United States. In 1968, Toyota introduced the Corolla, now the world's best-selling passenger car.

Japan has started to bolster its pace in the automated driving sector with the likes of well-known brands, including Mazda, Toyota, and even Lexus implementing the technology into their vehicles. Since September, as reported via Bloomberg, Japan has slowly begun integrating self-driving cars to suit rural areas and the elderly better. By 2022, several automobile manufacturers will seek to invite level 2-based self-driving mechanics to their vehicles to assist the country's overall endeavors. There are a total of five main levels of automated driving technology for self-driving cars. At the fifth level, the automobile is fully automated and drives itself.

In 1960, J.C.R. Licklider, an MIT professor and an early pioneer of artificial intelligence, already envisioned our future world in his seminal article, "Man-Computer Symbiosis": In the anticipated symbiotic partnership, men will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations. Computing machines will do the routinizable work that must be done to prepare the way for insights and decisions in technical and scientific thinking. In today's world, such "computing machines" are known as AI assistants. However, developing AI assistants is a complex, time-consuming process, requiring deep AI expertise and sophisticated programming skills, not to mention the efforts for collecting, cleaning, and annotating large amounts of data needed to train such AI assistants. It is thus highly desirable to reuse the whole or parts of an AI assistant across different applications and domains.