Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
Machine learning is a branch of AI focused on building computer systems that learn from data. The breadth of ML techniques enables software applications to improve their performance over time.
ML algorithms are trained to find relationships and patterns in data. Using historical data as input, these algorithms can make predictions, classify information, cluster data points, reduce dimensionality and even generate new content. Examples of the latter, known as generative AI, include OpenAI’s ChatGPT, Anthropic’s Claude and GitHub Copilot.
Machine learning is widely applicable across many industries. For example, e-commerce, social media and news organizations use recommendation engines to suggest content based on a customer’s past behavior. In self-driving cars, ML algorithms and computer vision play a critical role in safe road navigation. In healthcare, ML can aid in diagnosis and suggest treatment plans. Other common ML use cases include fraud detection, spam filtering, malware threat detection, predictive maintenance and business process automation.
While ML is a powerful tool for solving problems, improving business operations and automating tasks, it’s also complex and resource-intensive, requiring deep expertise and significant data and infrastructure. Choosing the right algorithm for a task calls for a strong grasp of mathematics and statistics. Training ML algorithms often demands large amounts of high-quality data to produce accurate results. The results themselves, particularly those from complex algorithms such as deep neural networks, can be difficult to understand. And ML models can be costly to run and fine-tune.
Still, most organizations are embracing machine learning, either directly or through ML-infused products. According to a 2024 report from Rackspace Technology, AI spending in 2024 is expected to more than double compared with 2023, and 86% of companies surveyed reported seeing gains from AI adoption. Companies reported using the technology to enhance customer experience (53%), innovate in product design (49%) and support human resources (47%), among other applications.
TechTarget’s guide to machine learning serves as a primer on this important field, explaining what machine learning is, how to implement it and its business applications. You’ll find information on the various types of ML algorithms, challenges and best practices associated with developing and deploying ML models, and what the future holds for machine learning. Throughout the guide, there are hyperlinks to related articles that cover these topics in greater depth.
ML has played an increasingly important role in human society since its beginnings in the mid-20th century, when AI pioneers like Walter Pitts, Warren McCulloch, Alan Turing and John von Neumann laid the field’s computational groundwork. Training machines to learn from data and improve over time has enabled organizations to automate routine tasks — which, in theory, frees humans to pursue more creative and strategic work.
Machine learning has extensive and diverse practical applications. In finance, ML algorithms help banks detect fraudulent transactions by analyzing vast amounts of data in real time at a speed and accuracy humans cannot match. In healthcare, ML assists doctors in diagnosing diseases based on medical images and informs treatment plans with predictive models of patient outcomes. And in retail, many companies use ML to personalize shopping experiences, predict inventory needs and optimize supply chains.
ML also performs manual tasks that are beyond human ability to execute at scale — for example, processing the huge quantities of data generated daily by digital devices. This ability to extract patterns and insights from vast data sets has become a competitive differentiator in fields like banking and scientific discovery. Many of today’s leading companies, including Meta, Google and Uber, integrate ML into their operations to inform decision-making and improve efficiency.
Machine learning is necessary to make sense of the ever-growing volume of data generated by modern societies. The abundance of data humans create can also be used to further train and fine-tune ML models, accelerating advances in ML. This continuous learning loop underpins today’s most advanced AI systems, with profound implications.
Philosophically, the prospect of machines processing vast amounts of data challenges humans’ understanding of our intelligence and our role in interpreting and acting on complex information. Practically, it raises important ethical considerations about the decisions made by advanced ML models. Transparency and explainability in ML training and decision-making, as well as these models’ effects on employment and societal structures, are areas for ongoing oversight and discussion.
Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. The four basic types of ML are:
The choice of algorithm depends on the nature of the data. Many algorithms and techniques aren’t limited to a single type of ML; they can be adapted to multiple types depending on the problem and data set. For instance, deep learning algorithms such as convolutional and recurrent neural networks are used in supervised, unsupervised and reinforcement learning tasks, based on the specific problem and data availability.
Deep learning is a subfield of ML that focuses on models with multiple levels of neural networks, known as deep neural networks. These models can automatically learn and extract hierarchical features from data, making them effective for tasks such as image and speech recognition.
Supervised learning supplies algorithms with labeled training data and defines which variables the algorithm should assess for correlations. Both the input and output of the algorithm are specified. Initially, most ML algorithms used supervised learning, but unsupervised approaches are gaining popularity.
Supervised learning algorithms are used for numerous tasks, including the following:
Unsupervised learning doesn’t require labeled data. Instead, these algorithms analyze unlabeled data to identify patterns and group data points into subsets using techniques such as gradient descent. Most types of deep learning, including neural networks, are unsupervised algorithms.
Unsupervised learning is effective for various tasks, including the following:
Semisupervised learning provides an algorithm with only a small amount of labeled training data. From this data, the algorithm learns the dimensions of the data set, which it can then apply to new, unlabeled data. Note, however, that providing too little training data can lead to overfitting, where the model simply memorizes the training data rather than truly learning the underlying patterns.
Although algorithms typically perform better when they train on labeled data sets, labeling can be time-consuming and expensive. Semisupervised learning combines elements of supervised learning and unsupervised learning, striking a balance between the former’s superior performance and the latter’s efficiency.
Semisupervised learning can be used in the following areas, among others:
Reinforcement learning involves programming an algorithm with a distinct goal and a set of rules to follow in achieving that goal. The algorithm seeks positive rewards for performing actions that move it closer to its goal and avoids punishments for performing actions that move it further from the goal.
Reinforcement learning is often used for tasks such as the following:
Developing the right ML model to solve a problem requires diligence, experimentation and creativity. Although the process can be complex, it can be summarized into a seven-step plan for building an ML model.
1. Understand the business problem and define success criteria. Convert the group’s knowledge of the business problem and project objectives into a suitable ML problem definition. Consider why the project requires machine learning, the best type of algorithm for the problem, any requirements for transparency and bias reduction, and expected inputs and outputs.
2. Understand and identify data needs. Determine what data is necessary to build the model and assess its readiness for model ingestion. Consider how much data is needed, how it will be split into test and training sets, and whether a pretrained ML model can be used.
3. Collect and prepare the data for model training. Clean and label the data, including replacing incorrect or missing data, reducing noise and removing ambiguity. This stage can also include enhancing and augmenting data and anonymizing personal data, depending on the data set. Finally, split the data into training, test and validation sets.
4. Determine the model’s features and train it. Start by selecting the appropriate algorithms and techniques, including setting hyperparameters. Next, train and validate the model, then optimize it as needed by adjusting hyperparameters and weights. Depending on the business problem, algorithms might include natural language understanding capabilities, such as recurrent neural networks or transformers for natural language processing (NLP) tasks, or boosting algorithms to optimize decision tree models.
5. Evaluate the model’s performance and establish benchmarks. Perform confusion matrix calculations, determine business KPIs and ML metrics, measure model quality, and determine whether the model meets business goals.
6. Deploy the model and monitor its performance in production. This part of the process, known as operationalizing the model, is typically handled collaboratively by data scientists and machine learning engineers. Continuously measure model performance, develop benchmarks for future model iterations and iterate to improve overall performance. Deployment environments can be in the cloud, at the edge or on premises.
7. Continuously refine and adjust the model in production. Even after the ML model is in production and continuously monitored, the job continues. Changes in business needs, technology capabilities and real-world data can introduce new demands and requirements.
Learn how the following algorithms and techniques are used in training and optimizing machine learning models:
Machine learning has become integral to business software. The following are some examples of how various business applications use ML:
Enterprise adoption of ML techniques across industries is transforming business processes. Here are a few examples:
When deployed effectively, ML provides a competitive advantage to businesses by identifying trends and predicting outcomes with higher accuracy than conventional statistics or human intelligence. ML can benefit businesses in several ways:
But machine learning also entails a number of business challenges. First and foremost, it can be expensive. ML requires costly software, hardware and data management infrastructure, and ML projects are typically driven by data scientists and engineers who command high salaries.
Another significant issue is ML bias. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models. These models can fail and, at worst, produce discriminatory outcomes. Basing core enterprise processes on biased models can cause businesses regulatory and reputational harm.
Explaining the internal workings of a specific ML model can be challenging, especially when the model is complex. As machine learning evolves, the importance of explainable, transparent models will only grow, particularly in industries with heavy compliance burdens, such as banking and insurance.
Developing ML models whose outcomes are understandable and explainable by human beings has become a priority due to rapid advances in and adoption of sophisticated ML techniques, such as generative AI. Researchers at AI labs such as Anthropic have made progress in understanding how generative AI models work, drawing on interpretability and explainability techniques.
Interpretability focuses on understanding an ML model’s inner workings in depth, whereas explainability involves describing the model’s decision-making in an understandable way. Interpretable ML techniques are typically used by data scientists and other ML practitioners, where explainability is more often intended to help non-experts understand machine learning models. A so-called black box model might still be explainable even if it is not interpretable, for example. Researchers could test different inputs and observe the subsequent changes in outputs, using methods such as Shapley additive explanations (SHAP) to see which factors most influence the output. In this way, researchers can arrive at a clear picture of how the model makes decisions (explainability), even if they do not fully understand the mechanics of the complex neural network inside (interpretability).
Interpretable ML techniques aim to make a model’s decision-making process clearer and more transparent. Examples include decision trees, which provide a visual representation of decision paths; linear regression, which explains predictions based on weighted sums of input features; and Bayesian networks, which represent dependencies among variables in a structured and interpretable way.
Explainable AI (XAI) techniques are used after the fact to make the output of more complex ML models more comprehensible to human observers. Examples include local interpretable model-agnostic explanations (LIME), which approximate the model’s behavior locally with simpler models to explain individual predictions, and SHAP values, which assign importance scores to each feature to clarify how they contribute to the model’s decision.
In some industries, data scientists must use simple ML models because it’s important for the business to explain how every decision was made. This need for transparency often results in a tradeoff between simplicity and accuracy. Although complex models can produce highly accurate predictions, explaining their outputs to a layperson — or even an expert — can be difficult.
Simpler, more interpretable models are often preferred in highly regulated industries where decisions must be justified and audited. But advances in interpretability and XAI techniques are making it increasingly feasible to deploy complex models while maintaining the transparency necessary for compliance and trust.
Building an ML team starts with defining the goals and scope of the ML project. Essential questions to ask include: What business problems does the ML team need to solve? What are the team’s objectives? What metrics will be used to assess performance?
Answering these questions is an essential part of planning a machine learning project. It helps the organization understand the project’s focus (e.g., research, product development, data analysis) and the types of ML expertise required (e.g., computer vision, NLP, predictive modeling).
Next, based on these considerations and budget constraints, organizations must decide what job roles will be necessary for the ML team. The project budget should include not just standard HR costs, such as salaries, benefits and onboarding, but also ML tools, infrastructure and training. While the specific composition of an ML team will vary, most enterprise ML teams will include a mix of technical and business professionals, each contributing an area of expertise to the project.
An ML team typically includes some non-ML roles, such as domain experts who help interpret data and ensure relevance to the project’s field, project managers who oversee the machine learning project lifecycle, product managers who plan the development of ML applications and software, and software engineers who build those applications.
In addition, several more narrowly ML-focused roles are essential for an ML team:
Once the ML team is formed, it’s important that everything runs smoothly. Ensure that team members can easily share knowledge and resources to establish consistent workflows and best practices. For example, implement tools for collaboration, version control and project management, such as Git and Jira.
Clear and thorough documentation is also important for debugging, knowledge transfer and maintainability. For ML projects, this includes documenting data sets, model runs and code, with detailed descriptions of data sources, preprocessing steps, model architectures, hyperparameters and experiment results.
A common methodology for managing ML projects is MLOps, short for machine learning operations: a set of practices for deploying, monitoring and maintaining ML models in production. It draws inspiration from DevOps but accounts for the nuances that differentiate ML from software engineering. Just as DevOps improves collaboration between software developers and IT operations, MLOps connects data scientists and ML engineers with development and operations teams.
By adopting MLOps, organizations aim to improve consistency, reproducibility and collaboration in ML workflows. This involves tracking experiments, managing model versions and keeping detailed logs of data and model changes. Keeping records of model versions, data sources and parameter settings ensures that ML project teams can easily track changes and understand how different variables affect model performance.
Similarly, standardized workflows and automation of repetitive tasks reduce the time and effort involved in moving models from development to production. This includes automating model training, testing and deployment. After deploying, continuous monitoring and logging ensure that models are always updated with the latest data and performing optimally.
The global AI market’s value is expected to reach nearly $2 trillion by 2030, and the need for skilled AI professionals is growing in kind. Check out the following articles related to ML and AI professional development:
ML development relies on a range of platforms, software frameworks, code libraries and programming languages. Here’s an overview of each category and some of the top tools in that category.
ML platforms are integrated environments that provide tools and infrastructure to support the ML model lifecycle. Key functionalities include data management; model development, training, validation and deployment; and postdeployment monitoring and management. Many platforms also include features for improving collaboration, compliance and security, as well as automated machine learning (AutoML) components that automate tasks such as model selection and parameterization.
Each of the three major cloud providers offers an ML platform designed to integrate with its cloud ecosystem: Google Vertex AI, Amazon SageMaker and Microsoft Azure ML. These unified environments offer tools for model development, training and deployment, including AutoML and MLOps capabilities and support for popular frameworks such as TensorFlow and PyTorch. The choice often comes down to which platform integrates best with an organization’s existing IT environment.
In addition to the cloud providers’ offerings, there are several third-party and open source alternatives. The following are some other popular ML platforms:
ML frameworks and libraries provide the building blocks for model development: collections of functions and algorithms that ML engineers can use to design, train and deploy ML models more quickly and efficiently.
In the real world, the terms framework and library are often used somewhat interchangeably. But strictly speaking, a framework is a comprehensive environment with high-level tools and resources for building and managing ML applications, whereas a library is a collection of reusable code for particular ML tasks.
The following are some of the most common ML frameworks and libraries:
In theory, almost any programming language can be used for ML. But in practice, most programmers choose a language for an ML project based on considerations such as the availability of ML-focused code libraries, community support and versatility.
Much of the time, this means Python, the most widely used language in machine learning. Python is simple and readable, making it easy for coding newcomers or developers familiar with other languages to pick up. Python also boasts a wide range of data science and ML libraries and frameworks, including TensorFlow, PyTorch, Keras, scikit-learn, pandas and NumPy.
Other languages used in ML include the following:
Fueled by extensive research from companies, universities and governments around the globe, machine learning continues to evolve rapidly. Breakthroughs in AI and ML occur frequently, rendering accepted practices obsolete almost as soon as they’re established. One certainty about the future of machine learning is its continued central role in the 21st century, transforming how work is done and the way we live.
Several emerging trends are shaping the future of ML:
Amid the enthusiasm, companies face challenges akin to those presented by previous cutting-edge, fast-evolving technologies. These challenges include adapting legacy infrastructure to accommodate ML systems, mitigating bias and other damaging outcomes, and optimizing the use of machine learning to generate profits while minimizing costs. Ethical considerations, data privacy and regulatory compliance are also critical issues that organizations must address as they integrate advanced AI and ML technologies into their operations.
Lev Craig covers AI and machine learning as the site editor for TechTarget Editorial’s Enterprise AI site. Craig graduated from Harvard University with a bachelor’s degree in English and has previously written about enterprise IT, software development and cybersecurity.
Linda Tucci is an executive industry editor at TechTarget Editorial. A technology writer for 20 years, she focuses on the CIO role, business transformation and AI technologies.
Ed Burns, former executive editor at TechTarget, also contributed to this article.
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