Check out all the on-demand sessions from the Intelligent Security Summit here. The landscape of MLops is flourishing, in a global market that was estimated to be $612 million in 2021 and is projected to reach over $6 billion by 2028. However, it is also highly fragmented, with hundreds of MLops vendors competing for end users' operational artificial intelligence (AI) ecosystems. MLops emerged as a set of best practices less than a decade ago, to address one of the primary roadblocks preventing the enterprise from putting AI into action -- the transition from development and training to production environments. This is essential because nearly one out of two AI pilots never make it into production.

Enterprise machine learning development platform startup Comet ML Inc. has raised $50 million in new funding to continue to evolve how it provides data science and machine learning teams a single platform to manage and optimize their work. The Series B round was led by OpenView. Also participating in the round were Scale Venture Partners, Trilogy Equity Partners and Two Sigma Ventures. Including the new funding, Comet has raised $69.8 million to date, according to data from Crunchbase. Founded in 2017, Comet pitches itself as doing for machine learning what GitHub did for code.

Hermes Logistics Technologies (HLT) is working with researchers at the IT University of Copenhagen (ITU), Denmark, and dnata Australia to explore new machine learning models aimed at delivering predictive business analytics. The Artificial Intelligence (AI) algorithms will run data from dnata Australia's new Hermes Digital Ecosystem, which has a full Datalake infrastructure that captures and stores all of dnata's Hermes New Generation (NG) Business Intelligence events.The machine learning models will enable dnata to make predictive business process decisions providing key insights on efficiencies, costs, and new services. "Machine learning is part of HLT's digital agenda and our datalakes are a fantastic source of events and data, which are always up to date and ready to inform and train AI models in the Hermes Cloud," said Alex Labonne, chief technology officer at HLT. He added, "Successfully trained models will form new predictive functionalities for dnata and help them refine an already competitive cargo handling offering." The ITU team, headed by Professor Philippe Bonnet and working with HLT, will create, test, and develop the predictive models over the coming months to explore the design of cloud-native enterprise machine learning solutions.

Following the release of the 2020 State of Enterprise Machine Learning report, we created an interactive data visualization so anyone can explore the survey data, conduct analysis, and see how a company's machine learning efforts compare to others like it. The State of Enterprise Machine Learning (ML) experience shares eight questions that were posed in our survey and the associated results. After exploring the data, download the full report to read our assessments and predictions about where ML development is headed. Our report shares findings from nearly 750 survey respondents whom we polled in the fall of 2019. However, if you want to see how other companies of a similar size to yours are using machine learning, the interactive experience allows you to test your own hypotheses and arrive at findings tailored to you.

An Algorithmia report released on Thursday revealed the challenges associated with increased machine learning use in 2020. Most companies will be in the early stages of machine learning developments in 2020, but to get to more advanced stages, organizations must overcome a variety of obstacles, the report found. Algorithmia's 2020 State of Enterprise Machine Learning report surveyed 745 tech professionals to determine how organizations plan on deploying machine learning in 2020, and the key issues that accompany the journey. The biggest challenges associated with machine learning deployments involved scaling, versioning, and budgeting, according to the report. "AI and machine learning is going to be the most impactful technological advance that we're going to see in our lifetime," said Diego Oppenheimer, CEO of Algorithmia. "The role of the data science is to grab a bunch of the data these companies have been collecting and make sense of it," and technological advances have caused companies to generate more data, which results in the need for more data scientists, Oppenheimer said.

Today's modern enterprises are collecting data at exponential rates, and it's no mystery that effectively making use of that data has become a top priority for many. According to a recent survey of 2000 global enterprises by McKinsey & Company, 47% of organizations have embedded at least one AI capability in their standard business processes. This is up from 20% in 2017 and it's clear that this growth has created a global race to enabling the next important evolution of business as we know it: The AI-first enterprise. But what does this actually mean? With investment in AI technologies poised to reach $9.5 billion over the next three years, the imminent opportunity involves embedding data and machine learning intelligence across the business at scale -- predicting the next best move for growth, making every product a data product, or creating entirely new data-driven revenue streams.

Interest in machine learning has grown steadily over the years, and many organizations are aware of the potential impact machine learning tools and technologies can have on their business. But the reality is we are still in the early phases of adoption, and the majority of companies have yet to deploy machine learning across their operations. In fact, since the introduction of machine learning models at scale during the dot-com boom, it's taken nearly two decades for ML models to become mainstream. To understand more about how machine learning has progressed, O'Reilly recently issued the results of a new survey that explores the state of machine learning adoption in the enterprise. The findings suggest that only 15% of the 11,000 respondents work for companies that have extensive experience using ML in production.

The results of the survey, The State of Machine Learning Adoption in the Enterprise, suggest that sophisticated companies don't shoehorn enterprise machine learning into the category of software engineering. "I think the community is starting to realize more and more that this is not quite the same as regular software development," Lorica said. Indeed, more experienced companies appear to be adjusting their culture and are experimenting with processes to meet new challenges presented by enterprise machine learning -- for good reason. "If you talk to people who do a lot of this, one of the things they'll tell you is that a lot of the work actually happens once you deploy the model to production," Lorica said. "Models degrade, they can misbehave, and so you have to monitor them; you have to know when to retrain them."

Machine learning and artificial intelligence are technologies that have evolved rapidly in the last decade. Most people today are familiar with intelligent voice assistants, streaming video platforms that recommend personalized content, and vehicle navigation systems that suggest best routes in real time to avoid traffic, all examples of artificial intelligence and machine learning in the consumer world. While consumer-facing machine learning often has a narrow focus, enterprise machine learning solutions must cater to many different types of businesses, all of which measure success differently. A consumer can expect Netflix to learn her movie preferences by tracking what she and people similar to her click on, a solution that can be built to be fairly generic. However, enterprise machine learning solutions rarely work as seamlessly right out of the box.

The darling of artificial intelligence -- the technology referred to most by vendors and media outlets -- is machine learning. In fact, the technology is so popular today that some companies use the terms machine learning and AI interchangeably. What exactly is digital transformation? You may hear the term often, but everyone seems to have a different definition. See how our experts define digitization, and how you can get started in this free guide.