Other organizations can leverage business data to drive data-informed project management, allowing business leaders to more accurately determine how long certain operations may take and will cost. The fundamentals of these technologies are rooted in data-driven algorithms that enable machines to develop learned responses or predictive capabilities. As a result, with AI and machine learning comes data--big data--that requires resources to be allocated, not only specialists like programmers, but additional on-premises resources such as storage, server CPUs, networking bandwidth, and cloud-hosted storage services. As businesses look to develop their digital transformation strategies and create unique competitive advantage, AI and machine learning are increasingly considered the keys to unlocking the value of an organization's accumulated data.
For example, for personalized recommendations, we have been working with learning to rank methods that learn individual rankings over item sets. Figure 1: Typical data science workflow, starting with raw data that is turned into features and fed into learning algorithms, resulting in a model that is applied on future data. This means that this pipeline is iterated and improved many times, trying out different features, different forms of preprocessing, different learning methods, or maybe even going back to the source and trying to add more data sources. Probably the main difference between production systems and data science systems is that production systems are real-time systems that are continuously running.
In another example of disruption through AI, travel companies have begun using behavioral data and predictive analytics to customize brand experiences based on individuals' preferences and patterns. Automating IT functions alone reduces expenses by 14 to 28 percent, so companies that launch using automated services quickly establish a financial advantage over larger, legacy-burdened competitors. Some tech experts believe that the current generation of applied AI systems, such as predictive analytics, will give small businesses advantages through increased automation and efficiency. New BI platforms offer data visualization, customer relationship management programs, and other critical BI services.
As an example, mobile network operators are increasing their investment in big data analytics and machine learning technologies as they transform into digital application developers and cognitive service providers. With a long history of handling huge datasets, and with their path now led by the IT ecosystem, mobile operators will devote more than $50 billion to big data analytics and machine learning technologies through 2021, according to the latest global market study by ABI Research. Machine learning can deliver benefits across telecom provider operations with financially-oriented applications - including fraud mitigation and revenue assurance - which currently make the most compelling use cases. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization.
Whenever I read articles about data science I feel like there is some important aspect missing: evaluating the performance and quality of a machine learning model. Consequently, the first post on this blog will deal with a pretty useful evaluation technique: lift analysis. When evaluating machine learning models there is a plethora of possible metrics to assess performance.