As machine learning continues to evolve, its impact on As machine learning continues to evolve, its impact on various sectors—healthcare, finance, education, and more—becomes increasingly significant. This technology has been shown to enhance efficiency and enable intelligent automation in many extremely involved tasks. It also opens new avenues for innovation and problem-solving. 

Using machine learning solutions to build precise models empowers organizations to analyze potentially hundreds of thousands of data points to deliver faster, more accurate results at scale. With ML, businesses can gain detailed insight into opportunities, risks, and customer needs. And while this often means improved returns, the true possibilities of machine learning may be almost limitless. 

Machine learning is one of the foundational technologies of the AI era. As such it sometimes gets grouped together with similar advancements and the specifics of the terminology become blurred. To better understand the differences between AI, ML, and deep learning (DL), consider the following: 

Artificial intelligence

AI is a discipline that is used throughout information technologies to describe any attempt to replicate human or near-human intelligence in machines. AI encompasses both machine learning and deep learning. 

Machine learning

The term ‘machine learning’ is typically used to refer to classic data-based algorithms that identify patterns and perform tasks like classification, regression, and clustering. The more information it has, the stronger it will perform. 

A model is specified by several parameters. The concept of training an ML model means it moves to optimize the parameters while trying to minimize discrepancies between predictions and the actual truth values that appear in the data. 

Deep Learning

Deep learning is a younger field of AI that is based on neural networks. It is a subset of machine learning, using and structuring parameters in connected layers to create artificial approximations of human neural networks.

Training a neural network requires substantial amounts of data and computational resources, but the resulting DL models are often much more powerful than those obtained with classic ML algorithms.

What is data mining vs. machine learning?

Machine learning results in computer algorithms used to transform data into intelligent interpretations and actions. Data mining hunts for actionable intelligence in existing, available data. 

Data mining falls more under the umbrella of business analytics. It focuses on teaching computers how to identify unknown patterns, anomalies, or relationships in a large data set. Humans can then solve problems using this data. The process is more manual and usually requires human intervention for decision making. 

Machine learning is more directly related to AI and focuses on teaching a computer how to learn to analyze large datasets and their patterns. After initial programming, ML is capable of growing and improving without the need for human intervention. In a way, the computer becomes more intelligent and progresses on its own rather than being reactive and simply analyzing data it is given. 

There are several approaches to machine learning, each with their own advantages. These include:

Supervised learning

Supervised learning describes a machine learning technique in which an algorithm applies to new data what has been learnt from data that has been labeled or classified, in order to then predict future events. The system provides targets for outputs after being sufficiently trained. It can also compare output to the correct, intended output to identify errors and modify the model as needed.

Unsupervised learning

Unsupervised learning is used when the information for training is not classified or labeled. It studies how systems infer functions to describe hidden structures and solutions from unlabeled data. It does not necessarily provide the right output, but it is used to explore data and draw different inferences from datasets to identify any hidden structures or interesting relationships.

Semi-supervised learning

His method falls between supervised and unsupervised learning, as it uses both labeled and unlabeled data. It’s typical to use a smaller amount of labeled data and a larger amount of unlabeled data. The systems that apply this method considerably improve learning accuracy. Semi-supervised learning is usually chosen when labeling data requires skilled and relevant resources for training/learning.

Reinforcement learning

A method that interacts with the environment by producing actions to identify errors or rewards. The key characteristics of reinforcement learning are trial and error search and delayed reward. Simple feedback is required to learn which action is best—this is the reinforcement signal. This allows software agents and machines to determine ideal behavior within a context to maximize performance.

Machine learning generally follows a specific process, outlined below:

• Gathering data
  This initial stage involves accumulating diverse and relevant data from various sources. The quality and volume of data collected directly influence the effectiveness of the ML model.
• Preparing data
  Data preparation is crucial as it involves cleaning (correcting errors, removing duplicates), normalizing, and organizing the data into a format suitable for modeling. This stage also includes splitting the data into training sets for building the model and evaluation sets for testing its accuracy.
• Choosing a model
  Depending on the problem at hand—whether it's predicting numerical values or classifying data into categories—the appropriate machine learning model is selected. The choice is crucial as it impacts the subsequent performance and suitability of the model to the task.
• Training
  In this phase, the selected model is trained using the prepared training data. This involves adjusting the model parameters to minimize errors, a process that may require several iterations to optimize the learning from the data provided.
• Evaluating
  After training, the model is tested with the evaluation dataset to assess its performance. This step is vital to ensure that the model can generalize its predictions to new, unseen data.
• Parameter tuning
  Post-evaluation, the model's parameters may be fine-tuned to enhance its accuracy and efficiency. This optimization is targeted to improve the model's performance based on specific metrics identified during evaluation.
  
• Predicting
  With training and tuning complete, the model is now capable of making predictions or decisions on new data. This step realizes the practical value of the ML model, applying it to real-world scenarios to provide insights or automated decisions.

By leveraging large datasets, machine learning provides tailored solutions and insightful analyses that can transform the way organizations in essentially every industry operate. This technological advancement is doing more than reshaping traditional business models; it’s pioneering new methods in some of the most complex and essential sectors:

Financial services

Businesses in the financial services use ML technology to identify insights in data and prevent fraud. The insights help locate opportunities for investment. Data mining and machine learning can also identify high-risk clients or utilize cybersurveillance to uncover fraud.

Government

Public safety and utilities can use machine learning, as they have many sources of data that can be mined for insights. For instance, they can analyze sensor data to identify ways to save money, detect anomalies, and increase efficiency. Machine learning also helps identify fraud to help minimize identity theft.

Health care

There is a growing trend towards the use of machine learning with the help of wearable devices and sensors that use data to assess the health of a patient in real time or extract the most essential information pertaining to patients’ health. This technology can help medical experts analyze and identify trends in data or any issues that can lead to improved treatment and diagnosis.

Retail

Websites have the ability, using machine learning, to recommend items that customers may want based on previous purchases and the purchases of others. Retailers capture data, analyze it, and personalize the shopping history, especially with marketing campaigns, price optimization, supply planning, inventory management, and customer insights.

Oil and gas

Machine learning is used to find new energy sources, analyze minerals in the ground, streamline distribution, predict refinery and sensor failure, and other cost-effective movements.

Transportation

Transportation benefits from routes being more efficient; by analyzing data to identify patterns and trends and predict potential issues, profitability can be increased. Data analysis and modeling aspects of machine learning are crucial for delivery companies and public transportation.

With so many industries adapting to incorporate ML solutions, it is important to recognize specifically how this new technology is optimizing operations and helping organizations reach their objectives. The following are only a few of the ways that ML is changing the face of business:

Digital assistants and chatbots

Machine learning can be applied to chatbots and digital assistants, allowing them to evolve and learn from inputs, and working to maintain natural language processing while gathering and storing relevant information.

Customer service

Machine learning improves customer service by powering chatbots and virtual assistants that can handle routine inquiries without human intervention. This also aids in analyzing customer feedback and behavior, empowering businesses to better tailor their services and responses to individual buyers.

Recommendation engines

Recommendation engines power the suggestions for products, movies, and content on platforms like Amazon and Netflix. Machine learning models analyze user behavior, preferences, and similar profiles to suggest items that users are likely to enjoy, enhancing user experience and engagement.

Contextual online advertising

Consumers want to see ads that are relevant to them. ML technology helps populate relevant keywords that are trending in content, while also helping marketers take advantage of brand-building content.

Cybersecurity

An essential aspect of AI security, machine learning helps make cybersecurity simpler, less expensive, more effective, and more proactive. AIOps and SecOps use machine learning to analyze patterns to help predict and prevent attacks, both similar and new, while adapting to changing behavior.

Computer vision

Computer vision applications, powered by machine learning, enable computers to interpret and make decisions based on visual data. This technology is used in various sectors including healthcare for diagnostic imaging, in retail for check-out processes, and in automotive for autonomous driving.

Speech recognition

Speech recognition systems use machine learning to convert spoken language into text. These systems are fundamental in voice-activated assistants, automated transcription services, and real-time communication tools for the hearing impaired, as well as playing essential roles in ML-enhanced translation services.

Robotic process automation

Robotic process automation (RPA) integrates machine learning to automate routine, rule-based tasks by mimicking human interactions with digital systems. ML enhances RPA capabilities by enabling the automation of more complex tasks that require adaptive responses.

Fraud detection

Machine learning enhances fraud detection systems by analyzing patterns and anomalies in transaction data that might indicate fraudulent activity. These systems learn over time to detect ever-more sophisticated fraud tactics, thereby protecting users and reducing losses for businesses.

As organizations look to harness the power of machine learning, it's recommended that they approach its implementation strategically by carefully considering how ML fits into existing operations and established goals. Here are some best practices for incorporating machine learning into a business:

• Identify specific business needs
  Begin with a clear business problem that machine learning can address, rather than adopting ML just for its technological appeal.
• Evaluate relevance and feasibility
  Assess whether machine learning is a suitable and cost-effective solution for the identified problems.
• Start small
  Pilot with small-scale projects to gauge the impact and understand the dynamics of integrating ML into existing processes.
• Build cross-disciplinary teams
  Combine expertise from data scientists, industry specialists, and business managers to ensure that ML solutions are practical and aligned with company objectives.
• Invest in data infrastructure
  Ensure that all data acquisition, processing, and management systems are capable of supporting ML applications.
• Focus on continuous learning
  Encourage ongoing learning and adaptation, both in terms of the technology and the team's understanding of ML capabilities.
• Develop a scalable strategy
  Plan for a gradual scale-up of ML applications as initial projects demonstrate success and value.
• Leverage pre-built ML solutions
  Rather than building machine learning models in-house, businesses can gain faster value by adopting enterprise platforms with built-in ML capabilities. Invest in a platform that delivers AI-driven intelligence out of the box to scale ML without the complexities of custom development.

Simply put, machine learning has the capacity to revolutionize business, enabling organizations to operate more efficiently, predict outcomes more accurately, and deliver enhanced customer experiences at scale. 

The ServiceNow Platform®, our AI platform for business transformation is taking ML even further. It leverages machine learning to automate routine tasks, optimize workflows, and provide highly actionable predictive insights, empowering organizations to make data-driven decisions more swiftly than ever before. Organizations leverage one platform to put AI to work in every corner of their business. Only ServiceNow unites AI, data, and workflows on a single, enterprise-grade platform designed for scale, trust, and efficiency. 

ServiceNow Platform ML use cases

With its comprehensive suite of ML tools, the ServiceNow Platform helps businesses to innovate and streamline operations across a wide range of functions: 

• Enhancing IT operations
  Use predictive intelligence to automate incident categorization, recommend resolutions, and proactively prevent service disruptions.
• Optimizing HR service delivery
  Apply intelligent automation to streamline employee requests, accelerate case resolution, and improve overall workforce experiences. 
• Transforming customer service
  Leverage AI-powered virtual agents and case routing to deliver faster support, reduce resolution times, and enhance customer satisfaction. 
• Boosting developer productivity 
  Enable developers to work more efficiently with intelligent coding suggestions, automated test creation, and AI-assisted debugging.

Support your business with AI-powered solutions built on advanced machine learning. Demo ServiceNow today!