Machine Learning Trends 2026: What to Expect

Machine learning has come a long way from being a “wow effect” to becoming a more widely used tool. As technology demands evolve and change, companies across various industries adopt machine learning due to its impactful applications. 

While it’s impossible to make 100% accurate predictions, we still gave it a try and listed the most promising and upcoming machine learning trends for 2026 and beyond.

Prominent machine learning trends 

Let’s consider the most prominent ML trends in today’s market.

Trend 1. Agentic AI

According to Markets and Markets, the demand for autonomous AI agents is expected to reach $93.20 billion by 2032. In the future, we will see more agentic AI or autonomous systems that consist of machine learning models to solve complex business problems. Traditional AI responds to inputs but doesn’t act independently. Agentic relies on large language models and generative AI (GenAI) to understand context, plan, make decisions itself, and execute in real time. 

First, agentic AI collects data from its environment via IoT sensors, equipment, and user interactions. Then, it processes it to extract meaningful insights. Using natural language processing and computer vision, AI detects patterns, understands the broader context, and determines appropriate actions to achieve a goal. After that, it uses reinforcement learning or self-learning to evaluate the result and collect feedback to improve future decisions.

Agentic AI solutions can be used in almost any industry to autonomously perform business tasks. For example, in retail, AI agents can analyze current stock prices and economic factors to predict customer demands and optimal stock levels. In healthcare, such systems can track patient data and provide doctors with real-time feedback via chatbots. As a result, autonomous systems can take on complex, decision-intensive tasks and improve customer interactions by delivering personalized experiences. AI agents also enhance both current and future strategies and decisions. 

Trend 2. MLOps 

As the name implies, MLOps was inspired by the DevOps methodology. MLOps is a set of practices for transparent and seamless collaboration of data scientists (“development”) and operational specialists (“operations”) to build, deploy and maintain ML models. The key stages of MLOps include:

• Problem definition
• Data collection and preparation
• Model development
• Model deployment
• Model monitoring and maintenance

Before the introduction of MLOps, machine learning development has always been associated with certain challenges, like scalability limitations, operational complexity in managing sensitive data at scale, and poor communication between the teams. MLOps is aimed to resolve these issues by introducing standard practices to ML applications deployment.

While the phases of MLOps are pretty much the same as phases of traditional ML development, MLOps brings more transparency, eliminates communication gaps, and allows better scaling due to business objective-first design. You can say that with MLOps, you pay much more attention to the process of data collection and cleaning as well as to model training and validation. Hence, enterprises with an acute need for scalability will most definitely benefit from the deployment of the MLOps approach.

Trend 3. LLMOps 

Large Language Model Operations (LLMOps) continue MLOps practices, but its primary goal is to address the unique challenges of deploying large language models, such as GPT and BERT. LLMs have a massive size and, consequently, require significant computational resources, rapid development, and continuous performance monitoring. To tackle these challenges, the LLMOps combines the following key components:

• Exploratory data analysis (EDA). This stage includes exploring, sharing, and preparing data for the machine learning lifecycle.
• Data preparation and prompt engineering. Data is cleaned, consolidated, and deduplicated to ensure its quality and availability to the team. In parallel, prompts to the model are created and refined to obtain more accurate results.
• Model fine-tuning. The model is refined using libraries such as DeepSpeed, PyTorch, and TensorFlow to improve its accuracy and adaptability. 
• Model review and governance. At this stage, versions of the model and all its components are tracked. Specialists manage and update them throughout the entire life cycle.
• Model inference and serving. The model update frequency, request processing time, and other operating parameters are monitored.
• Model monitoring with human feedback. The system records model drift and suspicious user behavior, and specialists analyze these events and make decisions.

LLMOps also requires collaboration across teams, such as data engineering, data science, and ML engineers. For companies that leverage LLMs, LLMOps offer the following key benefits:

• Better model performance. LLMOps tools and techniques are used to identify and resolve bottlenecks, fine-tuning model parameters, and implement effective deployment strategies. 
• Risk reduction. LLMOps helps companies reduce risks associated with deploying and operating LLMs by implementing monitoring systems and conducting regular security audits.
• Improved efficiency. LLMOps offers automated tools and processes that reduce manual tasks, optimize resource use, and minimize the time required to build and deploy ML models.
• Scalability. LLMOps enable organizations to easily scale ML deployments and adapt to changing demands and requirements.

Although LLMOps is an emerging field, it is already becoming a key enabler for companies that want to effectively implement large language models into their products and processes. That is why businesses implement LLMOps to ensure stable model performance and maintain high-quality responses.

Trend 4. AgentOps

Similar to DevOps/MLOps, AgentOps is a set of practices and tools that companies use to create, deploy, and manage autonomous agents throughout their lifecycle. It includes the following key stages:

• Objectives definition for the AI agent;
• AI agent design;
• AI agent development and testing;
• AI agent deployment and monitoring;
• Data analysis and AI agent refinement.

Its main goal is to ensure that AI agents are reliable, predictable, and auditable across diverse operational scenarios. Moreover, AgentOps integrates automated monitoring and error-handling checks to improve system resilience. It identifies anomalies in agent behavior, flags incorrect responses, and provides mechanisms for intervention or self-correction. 

AI systems and agent-based processes help automate and improve workflows for any organization. However, as the number of autonomous agents grows, companies need to incorporate management and control systems. This is where AgentOps comes to play and provides organizations with numerous benefits:

• Reliability and performance. AgentOps monitors the decisions and interactions of AI agents to ensure the accuracy of AI system results.
• Improved efficiency. AgentOps connects AI agents with other systems, simplifying complex workflows and boosting organizational efficiency.
• Better security and compliance. AgentOps offers security controls to prevent common threats posed by AI agents.
• Optimization. AgentOps monitors AI agents training and improves performance by collecting and analyzing logs and feedback on agent behavior.

Ignoring AgentOps is unacceptable for companies relying on AI-powered automation. Without this, AI errors will be impossible to track and verify how agents make decisions. A lack of monitoring and control of autonomous systems will reduce trust in AI, slow its adoption, and increase regulatory risks.

In 2026, AI works with humans, not replaces them

In 2026 machine learning is finally stepping out of the lab and into our daily workflows as a true partner. The focus has moved from pure computational power to context and trust. By utilizing Agentic AI and domain-specific models, we are creating systems that don’t just process data, but understand the “why” behind a task. This shift allows technology to handle the heavy lifting of coordination, returning time to humans for the work that requires empathy, intuition, and creative judgment.

True progress this year is measured by reliability over scale. Instead of massive, distant models, we are seeing the rise of specialized, “right-sized” tools that are transparent and easy to steer. This maturation proves that the most successful AI isn’t the one that replaces human expertise, but the one that amplifies it – making sophisticated technology feel like a natural extension of our own capabilities.

Head of DS & ML Department

Alexander Gedranovich

Trend 5. Smaller language models (SLMs)

Small Language Models are compact neural networks designed for various NLP tasks, such as text generation, summarization, and question answering. The examples of popular SLMs include DeepSeek-R1 and GPT-4o mini. While large language models contain hundreds of trillions of parameters, small language models typically have around 1 million or 10 billion parameters. Compared to LLMs, the main advantages of small language models are:

• Low compute requirements. SLMs require less memory and computing resources, which makes them cheaper to run and easier to deploy on edge devices or in resource-constrained environments, like laptops, mobile devices, etc.
• Effective performance. SLMs can be fine-tuned or trained for specific applications because they achieve accuracy and speed close to LLMs but without the additional complexity.
• Reduced costs. Using SMLs, organizations can save on development, infrastructure, and operational costs that would otherwise be required to run large-scale models.
• Better privacy and security controls. Because SLMs are small, they can be deployed in private cloud environments or on-premises, improving data protection and enabling more effective cyberthreat management.

When selecting language models, the choice depends on their intended use. LLMs are great for general-purpose applications if you need versatility. On the other hand, SLMs are suitable for tasks that require domain-specific knowledge, rapid processing, and lower resource usage.

Trend 6. Domain-specific models (DSLMs)

Domain-specific language models (DSLM) are AI models, often LLMs, trained on data from a particular industry or task. Large language models (LLMs), which are trained more broadly, are less likely to provide specific, deep, context-aware intelligence data. DSLM can understand industry-specific jargon, contextual nuances, minimize factual errors, and reduce model hallucinations. As a result, models become more safe, accurate, and trustworthy that are especially useful in areas like finance and healthcare.

For example, PubMedGPT is an LLM focused on healthcare literature that has been trained on research articles and medical terminology. As a result, the model can provide clinical decision support or answer medically relevant queries with greater accuracy. The most common techniques to equip an LLM with domain-specific knowledge are:

• Prompt engineering;
• Training from scratch;
• Retrieval augmented generation (RAG);
• Fine-tuning;
• Hybrid approach.

According to Garther, more than 50% of GenAI models used by enterprises will be tailored to a particular industry or business task by 2027. Thus, the spread of domain-specific language models will not be long in coming.

Trend 7. Explainable AI (XAI)

Explainable artificial intelligence is a set of methods that enable users to understand and trust the results of machine learning models. ML algorithms can be categorized as “white-box” (results that are understandable to experts) and “black-box” (results that are hard to explain to experts). XAI algorithms follow the three principles to overcome the problems of “black-box”: 

• Transparency. The decision-making process of the ML model must be clear and traceable.
• Interpretability. The results of the ML model can be interpreted and explained in human language.
• Explainability. Each decision of the Ml model can be explained to understand why the algorithm reached such a result.

XAI methods are based on three main techniques: prediction accuracy, traceability, and decision understanding. Each technique relies on a specific method, such as Local Interpretable Model-Agnostic Explanations (LIME), Deep Learning Important FeaTures (DeepLIFT). They also include educating the team working with AI to understand how and why the AI makes decisions.

Explainable AI makes it easier to identify bias, errors, or unintended behavior in ML models. When teams understand why a model made an input, they can validate results and optimize algorithms for better outputs. It helps stakeholders trust AI outputs and feel confident using them. This is especially crucial for financial forecasts, medical diagnostics, and autonomous driving. 

Trend 8. Federated learning

Federated learning is a machine learning approach in which a shared model is trained on data from multiple decentralized edge devices or servers. The main difference between federated and traditional learning is where the data is stored during training. 

• In traditional machine learning (centralized), data is collected from various sources and consolidated in a single location, like a cloud server or data center. Then, the ML model is trained directly on this dataset. 
• In federated learning (decentralized), the ML model is sent to the data, and users train the model on their local data. Only model updates are then sent back to the central server for aggregation.

This decentralized training process can be divided into three categories: 

• Horizontal federated learning. When the central model is trained on similar datasets. For example, several hospitals are training a common model to detect pneumonia using X-ray images.
• Vertical federated learning. When the data are complementary. For example, film and book reviews are combined to predict a user’s music preferences. 
• Federated transfer learning. When a pre-trained foundation model designed to perform one task, like detecting cars, is trained on another dataset to do something else, like identify cats.

While centralized machine learning is effective, federated learning is gaining popularity for its ability to deliver data privacy. By keeping data localized on client devices, it reduces the risk of sensitive information exposure during transmission or storage. As a result, it enhances user privacy and helps organizations comply with strict data protection regulations. 

In addition, it eliminates the need to move large datasets to a central server, thereby saving bandwidth and reducing costs. Due to these benefits, federating learning is especially vital for healthcare and financial services, where sensitive data must remain on-device, and compliance requirements limit data sharing.

Trend 9. Multimodal machine learning (MML)

A multimodal machine learning model capable of processing information of multiple data types simultaneously, like images, videos, and text. The main goal is fusion, the ability to enhance AI decision-making by integrating diverse data sources that can be done in various ways: 

• Early fusion combines raw data from different sources at the start of the model pipeline. It works well when the data types are closely related, such as audio and video for emotion detection.
• In late fusion each data type is processed independently, and the results are combined at the end. For example, in self-driving cars, where data from cameras and radars is processed separately. 
• Hybrid fusion blends both early and late fusion. It is often used when different data types require varying levels of integration, as in healthcare models that combine text and image data for diagnosis.

By doing so, AI systems can generate more informed and accurate predictions. While 

multimodal models are large, they require more computational resources and are harder to train. Despite these challenges, they have enormous potential and can be used in various industries and applications. 

For example, an ML model can analyze a patient’s medical history (text) and a CT scan (image) to make a more accurate diagnosis. Multimodal ML can be used to process data from multiple sensors, including cameras, LIDAR, radar, and GPS, enabling the car to navigate roads and avoid obstacles. According to Garther, 80% of enterprise software will be multimodal by 2030 from less than 5% in 2024.

Trend 10. Retrieval-augmented generation (RAG)

Due to limited pre-training, LLMs can provide false, outdated, or generic input, using unreliable sources or incorrect terminology. RAG addresses these problems by helping LLMs extract new information from external data sources, such as databases, downloaded documents, or web sources before generating answers. It allows LLMs to incorporate domain-specific and updated information that is not available in the previous training data. As a result, the LLM is able to generate more accurate, informative, and engaging answers.

Aimed at advancing artificial intelligence, the technology offers several advantages to organizations.

• Fresh data. Developers can use RAG to directly connect LLM in real time to social media feeds, news sites, or other frequently updated data sources. It allows LLM to provide users with the most up-to-date information.
• Increased user trust. By citing a source, RAG allows LLM to provide accurate information. Users can find the source documents if they need more detailed information or clarification.
• Accurate answers. The combination of search and generation reduces the risk of hallucinations. With RAG, the model relies on real, trusted documents, improving the accuracy of its answers.
• Adoptability. Developers can monitor and modify the information sources fed into the LLM to adapt the model to changing requirements across different domains without completely retraining it.

But how does it work? Retrieval-augmented generation method includes the following key stages. First, external data comes from APIs, databases, or documents and is stored as numeric vectors (embeddings) in a vector database for use by LLM. Second, the system converts a query into vectors and searches the vector database for relevant documents. Third, RAG complements the user’s query with extracted information. Using a chain of reasoning, LLM generates a precise response based on context. 

Although RAG expands the capabilities of ML models and improves data control, it does not completely eliminate all problems related to data.

Outdated Machine Learning Trends 

Machine learning is rapidly evolving, and some methods once considered best obsolete. Companies that cling to outdated trends risk wasting time, money, and resources. Here are some approaches that are no longer considered trends and the reasons why.

Training models from scratch for each task

Why: Training each model from scratch for each task is considered an outdated approach today, as it is too expensive, slow, and often produces lower-quality results than retraining existing large models. Pre-trained models and transfer learning dramatically reduce training time, allowing for faster production and pipeline reuse.

Use low-code/no-code ML platforms

Why: Low-code/no-code ML platforms are outdated because such solutions have customization, scalability, and performance concerns. They create vendor lock-in, hindering optimization. They are suitable for prototypes, but not for solid, production-ready machine learning solutions.

The logic is “the more data, the better”

Why: Today, a data-centric AI focuses on improving the quality of data, not just increasing its volume. It implies removing duplicate, noisy, and biased data rather than collecting more. Furthermore, growing data volumes significantly increase storage and processing costs without guaranteeing better model performance.

Evaluating a ML model based on accuracy only

Why: In many cases, a machine learning dataset can easily become imbalanced, meaning one class significantly outperforms the others. It occurs because algorithms typically strive to improve overall accuracy, leading their decisions to be biased toward the more frequent class. This leads to several issues, including bias in predictions, misleading accuracy estimates, and poor generalization.

Thus, traditional accuracy metrics are insufficient. Data scientists should consider precision, recall (sensitivity), F1 score, Receiver Operating Characteristic (ROC-AUC), Precision-Recall Area Under Curve (PR-AUC), and resampling techniques to balance the dataset.

What is the future of ML trends in 2026 and beyond?

According to Fortune Business Insights, the global Machine Learning (ML) market size is expected to reach $309.68 billion by 2032 with a CAGR of 30.5%. Looking ahead, we expect the ML market to continue to grow at an incredible pace, mainly due the rapid adoption of generative AI, increased business automation, and deep integration of AI into business processes.

In the coming years, we will see more machine learning trends that shift towards automation, speed, efficiency, and become increasingly specialized. Since machine learning is a valuable asset for any company, it must become more affordable and versatile to benefit businesses of all sizes and types. Organizations that implement machine learning technology can benefit from automated complex processes, increased employee productivity, improved customer service, and reduced human errors. 

At the same time, implementing ML isn’t always easy. There are common factors that hold back the adoption of ML, such as workforce skills gaps, security and privacy concerns, algorithmic accuracy, and the need for large training datasets. Most companies can avoid these and other challenges by partnering with an experienced ML provider, like SoftTeco, which can help build secure and reliable ML solutions of any complexity fast and effectively.