Machine learning (ML), a collection of AI techniques centered around pattern recognition and computational learning, dates back to its initial definition in 1959, envisioning computers with autonomous learning capabilities.

Previously, machine learning was cost-prohibitive for most enterprises, but today, the accessibility of machine learning services from public cloud providers has made this technology more affordable. This article aims to provide an overview of the current landscape of machine learning and its significance in addressing the evolving IT development and deployment requirements, particularly in the context of cloud environments.

What is machine learning?

Machine learning revolves around the study of algorithms capable of pattern recognition, enabling predictions based on data patterns. It offers a superior alternative to static program instructions, allowing data-driven predictions and decisions that improve autonomously over time, eliminating the need for human intervention and additional programming.

Machine learning holds significant potential for businesses. However, as it becomes more accessible through cloud platforms, concerns arise about potential misapplications. Cloud providers often emphasize the broad value of machine learning, but its true worth lies in its application to systems capable of benefiting from data-pattern-based predictions.

In summary, machine learning in the cloud has substantial value for businesses when applied appropriately. Enterprises exploring the possibilities of this technology may discover that, in certain cases, machine learning can be a transformative force for their operations.

Discovering Practical Machine Learning Applications

Machine learning applications have gained immense attention as the ultimate solution for driving enterprise value. However, the true power of machine learning lies in its selective application to specific use cases that can maximize its benefits. These applications encompass areas like fraud detection, predictive marketing, machine monitoring (IoT), and inventory management.

It's important to recognize that not all machine learning models are alike; they cater to distinct solution patterns. Major cloud providers offer support for three fundamental types of predictions. While they may carry different labels, they essentially fall into these 3 categories:

• Binary prediction
   
• Category prediction
   
• Value prediction
   

Let's delve into potential use cases for each prediction type:

1. Binary prediction
This primarily involves yes-or-no responses. They prove beneficial in scenarios like assessing order data to detect potential fraud or determining the right time to propose product upgrades to a customer, based on insights from a machine learning-driven recommendation engine.

The applications suited for binary predictions are quite extensive, as they typically deal with relatively simpler responses: Yes or No. Therefore, these machine-learning use cases are frequently integrated into business processes such as order processing, credit check systems, and recommendation engines for suggesting content, music, or other products to users based on data analysis and learned responses.

2. Category prediction
Category prediction involves classifying data into specific categories based on learned information. This proves valuable when dealing with diverse data types that require categorization to enhance comprehension and processing.

For example, insurance companies categorize claims into specific groups, drawing from historical insights. This categorization helps define the likely causes of accidents, even when explicit data may not be present, such as "likely alcohol-related," "potentially fraudulent," or "probably weather-related." These assignments are made by the machine-learning system based on past learning, considering factors like the accident's timing, location, damage type, driver's age, and more.

Category predictions find application across various domains, including finance, manufacturing, and retail, particularly when data needs additional contextualization, and the precise data correlations are not available in the existing database.

3. Value prediction
Value predictions are intricate but highly informative. They offer quantitative insights into probable outcomes derived from data analysis, utilizing learning models to identify data patterns.

Consider a scenario where you want to predict how many units of a product are likely to be sold in the coming month. This information is invaluable, as it facilitates precise manufacturing planning and potential travel cost optimization for sales teams.

The aim is to integrate these predictions into systems that can leverage this valuable data, such as planning and financial systems. Additionally, they can be incorporated into management dashboards, ensuring that decision-makers within the organization can readily access and utilize this valuable information.

Machine Learning in the Cloud

A variety of open-source and proprietary machine learning systems are available today, catering to the aforementioned prediction types, and some of these have been in existence for quite some time. Nevertheless, the expenses associated with these systems, encompassing both hardware and software, were historically prohibitive for the majority of enterprises. Furthermore, even if a business possessed the financial means, it often lacked the essential machine learning expertise necessary for designing prediction models or handling the intricacies of data science.

1. Advantages of Machine Learning Systems
Operating these systems is cost-effective, with an average hourly rate of just a few dollars, enabling you to deploy your machine-learning applications as described earlier.

Public cloud platforms also offer economical data storage solutions, allowing you to use actual databases or storage systems for input data in machine learning-enabled applications.

Moreover, these systems all offer Software Development Kits (SDKs) and Application Programming Interfaces (APIs) that enable the seamless integration of machine-learning capabilities directly into applications, supporting a wide range of programming languages. The true value of machine-learning technology emerges when it is embedded within applications. This allows for predictions that are highly operation and transaction-focused. For instance, it enables real-time assessment of the likelihood of a fraudulent loan application, providing an immediate process to address the issue, including the option for the applicant to rectify any errors and resubmit.

2. Disadvantages of Machine Learning Systems
Machine learning systems offered by specific public clouds are tightly integrated with those cloud environments. If you use a machine-learning system on a specific cloud provider, it is typically optimized for the native data storage mechanisms of that cloud. However, for enterprise databases located on-premises, integration with cloud-based storage systems is necessary.

For cloud providers, the value proposition is straightforward: customers leveraging the native machine-learning system are likely to benefit from using the native storage and database systems provided by the cloud. Moreover, applications perform more effectively on the cloud platform when they regularly interact with machine learning models, which often require seamless access to data.

However, for organizations adopting hybrid or multi-cloud deployments, a challenge emerges due to the potential separation of data from the machine-learning engine. This separation can impact performance, cost, and usability. In such cases, machine learning may serve as a loss leader intended to encourage more enterprises to embrace cloud services.

Embracing Machine Learning in the Cloud

Machine learning is often presented as a cutting-edge tool, but it's a technology that has undergone continuous evolution over several years. Recent developments in IT economics enable us to harness the potential of AI, particularly the AI variants of machine learning, to deliver tangible value to enterprises.

Several factors have paved the way for this transformation:

• The proliferation of cost-effective data storage solutions, both in the cloud and on-premises, which make vast datasets readily accessible.
   
• Enhanced data processing capabilities, encompassing raw processing power, storage, and innovative big data architectures like Hadoop.
   
• The availability of machine learning and other once-expensive services as economical cloud offerings, often comes with Pay-as-you-go pricing.
   

Despite these advancements, the successful creation and management of machine-learning systems in the cloud still rely on individuals with a deep understanding of machine learning and data-driven decision-making. The technology itself is not the limiting factor; rather, it's the scarcity of skilled professionals that poses a challenge. Addressing this skills gap will take time, but when it is overcome, we can expect machine learning to be a game-changing force for the majority of businesses.