Apache Flink Machine Learning – Real-Time ML Pipelines Guide
Jayanti Katariya
Last Updated: December 10, 2025
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Apache Flink Machine Learning has become one of the most powerful stream-processing engines for building real-time data and machine learning pipelines. While traditional ML frameworks focus on offline, batch-based processing, Flink Machine Learning enables continuous data ingestion, feature extraction, model serving, and monitoring—all in real time.
This article explains how ML works in Apache Flink, when to use it, and how to build real-time ML workflows with Python scripts.
What is Flink Machine Learning?
Flink Machine Learning refers to the ML capabilities built on top of Apache Flink’s streaming architecture, allowing developers to build:
- Real-time feature pipelines
- Incremental or online learning models
- Low-latency model inference
- Scalable distributed ML processing
While Flink isn’t a replacement for tools like PyTorch or TensorFlow, it excels at model deployment and real-time feature engineering, areas batch ML frameworks can’t handle efficiently.
Why Use Flink for Machine Learning?
Real-Time Feature Engineering
Flink can compute features on the fly from continuous data streams.
Low-Latency Predictions
Perfect for fraud detection, IoT analytics, churn scoring, and recommendation engines.
Scalable Pipelines
Flink’s distributed execution model makes ML pipelines highly scalable.
Online / Incremental Models
Flink supports algorithms that update in response to streaming data — essential for time-sensitive predictions.
How Flink Fits Into a Modern Machine Learning Workflow?
| Stage | Traditional ML | Apache Flink Machine Learning |
|---|---|---|
| Data ingestion | batch CSV/Parquet | streaming + batch |
| Feature engineering | offline, slow | real-time |
| Model training | offline | online or mini-batch |
| Model serving | separate infra | integrated in stream |
| Monitoring | manual | built-in metrics |
Flink is strongest at feature pipelines, real-time inference, and online training.
Python Example: Streaming Feature Engineering with Flink
Below is a simple PyFlink script that processes streaming data and extracts real-time features for ML models.
from pyflink.table import EnvironmentSettings, TableEnvironment
# Create the Flink Table environment
env_settings = EnvironmentSettings.in_streaming_mode()
table_env = TableEnvironment.create(env_settings)
# Define a Kafka source for real-time stream data
table_env.execute_sql("""
CREATE TABLE sensor_stream (
device_id STRING,
temperature DOUBLE,
timestamp TIMESTAMP(3),
WATERMARK FOR timestamp AS timestamp - INTERVAL '2' SECOND
) WITH (
'connector' = 'kafka',
'topic' = 'sensor_data',
'properties.bootstrap.servers' = 'localhost:9092',
'format' = 'json'
)
""")
# Real-time feature computation (rolling averages)
features = table_env.sql_query("""
SELECT
device_id,
AVG(temperature) OVER (
PARTITION BY device_id
ORDER BY timestamp
RANGE BETWEEN INTERVAL '10' SECOND PRECEDING AND CURRENT ROW
) AS avg_temp_10s
FROM sensor_stream
""")
table_env.execute_sql("""
CREATE TABLE feature_output (
device_id STRING,
avg_temp_10s DOUBLE
) WITH (
'connector' = 'print'
)
""")
features.execute_insert("feature_output").wait()
- Real-time sliding window features
- Perfect for feeding directly into an online ML model
Online (Incremental) Machine Learning with Flink
Flink supports algorithms such as:
- Online K-Means
- Linear models with SGD updates
- Streaming anomaly detection
- Time-series forecasting
Example: Online Prediction using a Pre-trained Model
import joblib
from pyflink.datastream import StreamExecutionEnvironment
# Load a pre-trained ML model (sklearn, XGBoost, etc.)
model = joblib.load("model.pkl")
env = StreamExecutionEnvironment.get_execution_environment()
# Dummy stream
stream = env.from_collection([5.1, 4.7, 6.3])
def predict(value):
return model.predict([[value]])[0]
stream.map(predict).print()
env.execute("online-model-inference")This enables continuous inference on incoming data.
Where Flink Machine Learning Works Best?
- Fraud detection
- Clickstream analytics
- IoT event processing
- Predictive maintenance
- Streaming recommendations
- Real-time risk scoring
When Not to Use Flink for ML?
- Heavy deep learning training
- Complex GPU-driven workloads
- Offline batch training of large models
- NLP transformer training
Use PyTorch/TensorFlow for training, and Flink for real-time data and inference.
Need Help Building Real-Time ML Pipelines?
Get expert guidance for designing, deploying, and scaling Flink-based machine learning workloads.
Conclusion
Apache Flink Machine Learning brings real-time capabilities to modern ML pipelines. While not a replacement for deep learning frameworks, Flink excels at streaming feature engineering, online training, and real-time inference.
If your application requires real-time predictions, high throughput, or continuous data processing, Apache Flink is a strong choice.
About Author
Jayanti Katariya is the CEO of BigDataCentric, a leading provider of AI, machine learning, data science, and business intelligence solutions. With 18+ years of industry experience, he has been at the forefront of helping businesses unlock growth through data-driven insights. Passionate about developing creative technology solutions from a young age, he pursued an engineering degree to further this interest. Under his leadership, BigDataCentric delivers tailored AI and analytics solutions to optimize business processes. His expertise drives innovation in data science, enabling organizations to make smarter, data-backed decisions.
