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.

Data preparation is the most time-consuming and hectic process in data science and machine learning, accounting for 80% of the labor. Messy data is a serious issue that costs businesses trillions of dollars every year. Model performance can be harmed by data errors (for example, mislabeled samples in the training set) and dataset-level concerns like overlapping classes. Most test set errors are ubiquitous even in gold-standard benchmark datasets. This can cause data scientists to deploy worse models. Although physically analyzing and cleaning up individual data points sounds tiresome, it frequently gives a significantly bigger payback than experimenting with advanced modeling approaches.

The more work an organisation does, the more data they generate. In addition to collecting data in their primary ITSM tool, other IT tools also generate data. Business intelligence (BI) teams and company leadership try to make informed decisions. But how do you make informed decisions when the amount of data is overwhelming and seemingly impenetrable? Increasingly, companies are turning to AIOps. AIOps combines big data and machine learning to improve the operations of IT.

Today, staying competitive means progressing with machine learning and analytics. Fortunately, the journey to success doesn't require teams have data scientists or deep analytics expertise. Register for this webinar to learn the proven processes and software technologies that make analytics accessible for every industrial organization. Cobus van Heerden is senior product manager for analytics and machine learning software for GE Digital. Cobus has 20 years of experience in developing and implementing industrial software globally.

Like Gartner, who report a 25% increase in end user inquiries on AIOps, we at BMC Software are also experiencing increased interest from customers who are challenged by increasing complexity and volumes of data which are beyond human scale to manage. The combination of big data, machine learning, analytics and automation is increasingly being recognized amongst IT leaders as having the potential to transform monitoring and event management and drive significant benefits across IT Operations processes. So, what are Gartner saying are the major changes in this updated 2019 Market Guide for AIOps?

Customers shouldn't have to explain intent. Businesses should monitor what they are up to, so that customers can expect the same personalised attention from online shopkeepers as they get when visiting their favourite farmers' market. Put simply, the online marketplace is fundamentally different to the physical marketplace. As customers, we're often alone online and need to find the products or services we'd like to purchase. If we can't find what we want, we simply navigate away without a trace. This is one reason why online marketing suffers from lacklustre conversion rates.

Making intelligent and accurate predictions is the core objective of machine learning and artificial intelligence applications. To achieve that objective, the machine learning or artificial intelligence application needs clean and well-organized information in a robust ecosystem architecture. Machine Learning (ML) is the process of a computer system making a prediction based on samples of past observations. There are various types of ML methods. One of the approaches is where the ML algorithm is trained using a labeled or unlabeled training data set to produce a model.

Digital has disintermediated content creators, distributors, and consumers, overturning traditional media business models. Strategy Analytics estimates that the total market revenue for global TV, video subscriptions and advertising will grow by nearly $70 billion from 2017 to 2022, with 90 percent of that growth coming from OTT alone. Audiences, meanwhile, will continue to expect a steady stream of high-quality content for a variety of screens and form factors. In this context, M&E firms are looking not just for cost-saving efficiencies, but for new revenue streams for their content in these new direct-to-consumer mediums. Research from IDC shows that unstructured content accounts for 90 percent of all digital information locked in a variety of formats, locations, and applications made up of separate repositories.

Rush University Medical Center is adopting machine learning and analytics technologies from two companies to process patient information, including from imaging studies and other sources, with hopes of customizing patient treatment and delivering precision medicine. The Chicago-based academic medical center is using a combination of technology from Cloudera and MetiStream, which are working together on products that providers can use to improve patient outcomes. Cloudera offers a platform for machine learning and analytics optimized for the cloud, while MetiStream develops healthcare analytics solutions. MetiStream offers an interactive analytics platform for healthcare and life science industries built on Cloudera's machine learning platform. By combining machine learning and analytics from Cloudera Enterprise and Cloudera Data Science Workbench, MetiStream contends its Ember product can deliver insights across massive volumes of handwritten clinical notes as well as genomic data.

Cloudera has expanded its footprint in machine learning and analytics with the launch of Cloudera Altus with SDX, a Platform-as-a-Service (PaaS) built with a shared data catalog that provides the business context of a user's data. Cloudera Altus supports a variety of high-value business use cases that require applying multiple data analysis capabilities and approaches together. SDX makes it possible for those analytic functions to work together to combine data from different sources into a single coherent and actionable picture. Example use cases include answering complex questions about customer "next-best-offer", IoT predictive maintenance, and advanced threat detection. Cloudera's cloud business unit general manager Vikram Makhija says Cloudera Altus with SDX enables businesses to build and manage multi-function analytics use cases in the cloud, integrating data engineering, IoT, customer, and operations analytics, with machine learning.