What Is Real-Time Machine Learning and Why It Matters for Modern AI

As businesses collect ever-increasing volumes of data, the challenge of making sense of that data quickly can impact their bottom line. For example, imagine a retail business that uses machine learning to optimize its inventory. If the model is slow to score incoming data about changing customer preferences, the company risks stocking the wrong items, and sales will suffer. Real-time Machine Learning addresses this challenge by enabling machine learning models to score and react to incoming data quickly. Machine Learning Optimization plays a crucial role in ensuring these models perform efficiently and accurately. This article will explore how this approach can help you build intelligent systems that respond instantly to new data, enabling faster, more adaptive, and impactful AI-driven decisions.

AI inference APIs from Inference offer an effective way to achieve your goals. These tools help you deploy your machine learning models for real-time performance so your applications can make instant predictions and decisions as new data arrives.

What is Real-Time Machine Learning?

Real-time machine learning is when an app uses a machine learning model to autonomously and continuously make decisions that impact the business in real time. A great example is when you’re using a credit card to make a purchase, the credit card company has a bunch of data at its disposal, like your shopping history and average transaction amount, to immediately figure out whether it’s you making the purchase or whether it should be flagged as fraud.

However, the decision must be made in real time, and milliseconds matter because a user waits for their transaction to be approved on the other side. Other examples include recommendation systems, dynamic pricing for tickets to a sporting event, and loan application approvals. These mission-critical applications run online in production on a company’s operational stack.

Understanding Analytical Machine Learning and Its Applications

Analytical ML lives offline and is ML’s older sibling in real time. Analytical ML applications are designed for human-in-the-loop decision making. They help a business user make better decisions with machine learning, sit in a company’s analytical stack, and typically feed directly into reports, dashboards, and business intelligence tools. They’re much easier to deploy because they operate at human timescales.

If an application goes down, the end user isn’t directly impacted, and the human decision-maker will just have to wait a little longer to get their analytical report. Some examples you’ll see in everyday life are churn predictions, customer segmentation, and sales forecasting tools. Analytical and real-time ML are necessary in an organization, serve different functions, and are implemented differently. The table below gives a high-level overview of the difference between the two.

Real-Time Machine Learning in the Real World

Let’s look at a real-world real-time machine learning example from Uber Eats. When you open the app, it has a list of recommended restaurants and delivery time estimates.

What looks simple and easy in the app doesn’t tell the whole story; what goes on behind the scenes is complicated and involves many moving parts. For example, to recommend “Otto’s Tacos” and provide a 20-30 minute wait time in the app, Uber’s ML platform needs to pull a wide array of data from several different raw data sources, such as:

• How many drivers are near the restaurant at the moment? Are they in the middle of delivering an order, or are they available to pick up and deliver a new order?
• How busy is the restaurant’s kitchen? A slower kitchen with few orders means the restaurant can start working on a new order faster, and vice versa for a busy kitchen.
• What are the customer’s past restaurant ratings? (This will affect what the app shows as recommended restaurants, for example.)
• What cuisine is the user searching for right now? What is the user’s current location or set delivery location? The Michelangelo feature platform converts all this data into machine learning features, aka signals, that a machine learning model is trained on. The model then uses that information to make real-time predictions.

For example, ‘num_orders_last_30_min‘ is an input feature that predicts the delivery time, which will appear in your mobile app. The steps above, turning raw data from various data sources into features and features into predictions, are common across all real-time ML use cases. It doesn’t matter if a system tries to predict a car loan applicant’s interest rate, detect credit card fraud, or recommend what to watch next; the technical challenges remain the same.

The Trends Enabling Real-Time Machine Learning

Uber was positioned to take full advantage of real-time ML because it built its entire tech stack on a modern data architecture and principles. Over the years, we’ve also seen similar modernization outside the tech world.

Historical data can now be preserved forever. Data storage costs have dropped precipitously. Companies can now collect, buy, and store information about every touchpoint with customers. This data is crucial for ML because training a good, accurate model requires much historical data. Without data, machine learning wouldn’t exist. Data silos are being broken up. From its first day, Uber centralized nearly all of its data in its Hive-based distributed file system.

The Critical Role of Centralized Data Access in Machine Learning

Centralized data storage (or centralized access to decentralized data stores) is essential because data scientists training ML models know what data is available, where to find it, and how to access it. Even today, years after the launch of Michelangelo, many enterprises haven’t yet centralized all of their data. However, architectural trends like the modern data stack are moving the data scientist’s dream of democratizing access to data closer to reality.

The Rise of Real-Time Data and Its Importance for Machine Learning

Real-time data is now available with streaming (and no, we’re not talking about video streaming). You can’t detect fraud in real time if you only know what happened 24 hours ago, but not 30 seconds ago.

Data warehouses like Snowflake and data lakes like Databricks’ Delta Lake are purpose-built for long-term historical data storage. Over the past few years, more companies have adopted the streaming infrastructure crucial for real-time ML, like Kafka or Kinesis, to provide applications with real-time data.

The Necessity of Real-Time ML for Handling High-Velocity Data

Humans can’t keep up with the volume and speed of data. Analytical ML isn’t enough for many of today’s use cases because if you’re relying on human-in-the-loop decision-making, there’s no way you can keep up with the volumes and speed of data provided by modern infrastructure. For example, it would be impossible for humans to manually quote every Uber ride requested. Uber would need an army of employees who are just focusing on providing quotes.

This is where real-time ML comes in. Real-time ML can be used to automate decisions at much higher speed and scale than could be supported by human decision-making. Simpler, more routine decisions can be handed off to models and used to directly power applications.

MLOps = DevOps for Machine Learning

In many tech companies, engineers are empowered to own their code. They’re responsible for their work from start to finish and can make daily changes in production when needed. The process is supported by following and automating DevOps principles. Beyond the tech world, many teams bring DevOps principles and automation to their data science teams via MLOps.

Machine learning is still much more painful to get right at most companies than software. The industry is heading toward a future where a typical data scientist at a typical Fortune 500 company can iterate on a real-time ML model whenever they like, yes, even multiple times a day.

Related Reading

• How to Improve Machine Learning Models
• LLM Inference Optimization

How Does a Real-Time Machine Learning Pipeline Work?

The three components commonly associated with 'real-time' models are

• Online inference
• Low-latency serving
• Real-time features

Sometimes, it means all three; other times, it means just one. This lack of clarity isn't just a semantic issue; it leads to practical problems.

Without a deeper breakdown, it's impossible to decipher what real-time means, and it hampers our ability to tailor solutions to specific problem spaces. For instance, efforts to achieve sub-30 ms response times might compromise model accuracy, only to find later that allowing up to 500ms could have enhanced predictive quality with reduced engineering effort and been fast enough.

Low Latency Serving

Low-latency serving is often associated with the “real-time” phrase. It's about how fast a model responds to requests. But even then, it’s still unclear. Sometimes, it can mean under 10ms latency; other times, it means under 1s. The “low” in low latency is completely context-dependent.

Response speed is paramount for some use cases, like recommender systems. In others, like fraud detection, we can sometimes get away with 100ms or 1s response times.

Online Inference

A separate concept overloading the “real-time” phrase is online inference, which refers to models running on a serving platform and perpetually ready to handle requests. This contrasts with offline models, like lead-scoring systems, which activate, make inferences, and then go dormant.

The online approach implies constant readiness, but it's just one concept attributed to real-time. Online inference signifies availability, not necessarily low latency or working on up-to-date data.

Using “Real-Time” Features

The third catch-all attribute associated with "real-time" ML uses "real-time" features. Features are inputs to a model, so a "real-time" model may need "real-time" features. This pushes the confusion further down the stack, as "real-time" is still ambiguous when describing features.

‍When talking about features, “real-time” usually references either or both:

• Freshness
• Latency

The easiest way to lower feature latency is by caching pre-processed features. However, this makes the features less fresh. The importance and impact of freshness and latency are context-dependent, another reason we need to use more descriptive terminology.‍

Latency

Latency is one of the concepts overloading the “real-time” feature phrase. For some ML systems, like recommender and fraud detection systems, latency can play a pivotal role.

Latency refers to the time it takes for a model to retrieve the values of the features it needs. Many systems pre-process features into inference stores like Redis or DynamoDB to achieve lower latency. This approach ensures low-latency serving by trading off the freshness of data.

Understanding Feature Latency Requirements for Effective ML Systems

For some ML systems, having features that are a day old is fine, and having low latency is more critical. For example, if you have a feature that was a user’s favorite genre in the last 30 days, you may want to update it via a batch job every day and not impact model performance. When you want a user’s favorite song in the last hour, you may take on the complexity of running a streaming pipeline to achieve fresher features.

When you want to check if a user’s comment is spam, you may send the comment along with the request and generate the features using an on-demand transformation. Saying that a feature needs to be real-time isn’t descriptive enough. You need to understand the latency requirements to tailor the approach to the specific needs and constraints of the application.‍

Freshness

Freshness is another concept that overloads the phrase real-time features. It refers to how long the feature was last updated at inference time.

Freshness matters because the most recent data often provides the most relevant insights, especially for features that are highly dynamic temporally. However, prioritizing freshness usually means accepting higher latency.

The Trade-off Between Feature Freshness and Processing Latency in Streaming Pipelines

This is because fresher features often require on-demand processing, which takes longer than retrieving pre-processed data from fast-access stores. Some people assume that using Kafka and Flink for streaming gives them extremely fresh features, but that’s not always true.

Data takes time to go through a streaming pipeline, even though it’ll be fresher than it would be with a scheduled batch transformation.

Balancing Feature Freshness with the Complexity of Streaming Pipelines

However, we take on much more complexity with streaming features than we would have to deal with by doing scheduled batch features. Stream jobs are always running and are often stateful. Errors require rewinding in a stream, updating the code, and running.

Updating feature logic requires backfilling and other complex data operations. The best approach is to understand how much the feature changes temporally and how different levels of feature freshness affect a model; then, we can tailor the correct pipeline for the feature with the best freshness, latency, and complexity for the use case.

Related Reading

• Model Compression
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• Inference Time
• AI Inference vs. Training

Real-Time Machine Learning Technologies

A diverse technology stack is often needed for real-time machine learning due to the multiple stages involved, each of which might require specialized tools. We’d need a separate article (or perhaps a book!) to cover these technologies. For brevity, the table below only lists some of the most popular, commonly used ones.

Key Technologies for Building Real-Time Machine Learning Systems

Streaming platforms

Useful for ingesting and handling high-velocity, high-volume data streams.
Examples: Apache Pulsar, Apache Kafka, Amazon Kinesis, Redpanda.
See how some of them compare:

• Kafka vs Kinesis
• Kafka vs Pulsar
• Kafka vs Redpanda

Stream processing solutions

Used for processing and transforming data streams, so they are suitable for analysis.
Examples: Quix, Apache Spark, Apache Flink, Faust, Bytewax.
See how some of them compare:

• Quix vs. Flink
• Quix vs. Flink vs. Spark

Machine learning libraries

Tools used to develop and train machine learning models.
Examples: TensorFlow, PyTorch, Scikit-learn.

Data storage and database systems

Tools to store the data that will be used for real-time ML.
Examples: Apache Cassandra, Cloud Bigtable, MongoDB, InfluxDB.

ML model deployment and management

Technologies that allow real-time machine learning models to be deployed and managed in production environments.
Examples: TensorFlow Serving, Kubeflow, Seldon Core, Metaflow, MLFlow.

ML model monitoring tools

Technologies that help monitor model performance.
Examples: Prometheus & Grafana, Fiddler AI, Superwise, IBM Watson OpenScale.

Feature stores & platforms

Centralized repositories where you can store, share, and retrieve features used to train ML models and generate predictions in real time.
Examples: Feast Feature Store, Databricks Feature Store, Tecton, Hopsworks Feature Store.

Managed ML platforms

End-to-end platforms that provide tools to go through multiple stages of the machine learning process: data preprocessing, feature engineering, model development, training, tuning, deployment, monitoring, etc.
Examples: AWS SageMaker, Azure Machine Learning, IBM Watson Studio, DataRobot MLOps, Vertex AI.

Real-time ML models

Algorithms that make instant predictions on live, incoming data, enabling immediate decision-making or action.
Examples: Decision trees, neural networks, gradient boosting (e.g., XGBoost), support vector machine (SVM), Large Language Models (LLMs).

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