Using Apache Flink® for Model Inference: A Guide for Real-Time AI Applications

As real-time data processing becomes a cornerstone of modern applications, the ability to integrate machine learning model inference with Apache Flink® offers developers a powerful tool for on-demand predictions in areas like fraud detection, customer personalization, predictive maintenance, and customer support. Flink enables developers to connect real-time data streams to external machine learning models through remote inference, where models are hosted on dedicated model servers and accessed via APIs. This approach is ideal for centralizing model operations, allowing for streamlined updates, version control, and monitoring, while Flink handles real-time data streaming, preprocessing, data curation, and postprocessing validation.

Understanding Flink AI model inference in real-time applications

In machine learning workflows, remote model inference refers to the process where real-time data streams are fed into a model hosted on an external server. Flink applications make API calls to this server, receive responses, and can act on them within milliseconds. This setup ensures that model updates, A/B testing, and monitoring are managed centrally, simplifying maintenance and scalability for high-throughput applications where latency is a trade-off for flexibility.

Remote model inference is also possible in hybrid cloud setups, where models might be hosted on a cloud-based infrastructure and accessed by edge or on-premises Flink applications. This flexibility enables businesses to scale model inference capabilities across multiple geographies or system architectures while maintaining consistency and control over the model lifecycle.

Key benefits of AI model inference with Flink on Confluent Cloud

Confluent’s Flink AI model inference integrates remote AI models into your data pipelines by calling AI models within Flink queries, managing remotely hosted AI models with SQL DDL statements and invoking remote endpoints (e.g., OpenAI, Azure ML, AWS Bedrock, AWS SageMaker, Google AI). The benefits of using data streaming for model inference include:

1. Centralized model management: With remote inference, models are managed centrally, allowing for straightforward updates and versioning. Developers can implement new model iterations without disrupting the Flink streaming application, minimizing downtime and ensuring seamless updates.

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2. Scalability and flexibility: Remote model inference can leverage Confluent Cloud’s serverless infrastructure for scalability. As demand increases, models can scale independently of the Flink applications by adding resources to the model server, making it possible to handle high volumes of concurrent inference requests without altering the streaming pipeline. In any case, model processing is isolated and decoupled from the data orchestration work done by Flink.

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3. Efficient resource allocation: By offloading model computations to a separate model server or cloud service, remote inference frees Flink’s resources to focus on data processing. This is particularly advantageous when handling complex models that require substantial computational power, allowing Flink nodes to remain lean and efficient.

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4. Seamless monitoring and optimization: Centralized model hosting allows teams to monitor model performance in real time, using analytics dashboards to track accuracy, latency, and usage metrics. Flink applications can leverage this feedback loop to adjust data processing parameters and improve the overall performance of the inference pipeline.

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The Confluent Data Streaming Platform enhances model inference capabilities, with Flink for stream processing and enabling real-time predictions with low latency.

Real-world use cases for Flink AI model inference

• Fraud detection in financial services: Banks and payment processors use remote model inference with Flink to analyze transaction data in real time. As Flink streams transaction details, it makes API calls to a centralized fraud detection model, which returns a risk score for each transaction. If the score indicates potential fraud, an alert is triggered instantly, allowing for immediate intervention.

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• Customer personalization in retail and e-commerce: In retail and e-commerce, real-time recommendations and personalization can be delivered through Flink by integrating with a recommendation model (e.g., a support vector machine) hosted on an external server. As customers interact with a website or mobile app, Flink processes their behavior data, queries the model server, and receives tailored product suggestions that are displayed in real time.

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• Condition monitoring and predictive maintenance in Industrial IoT: Manufacturing companies use Flink for real-time sensor data processing and remote inference to predict equipment failures. Flink streams telemetry from machinery sensors to a model server, where analytic models assess potential issues. Alerts are then sent back to Flink, triggering maintenance scheduling before a breakdown occurs, reducing downtime and maintenance costs.

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• Generative AI for real-time customer support: In customer service, companies can use remote model inference with Flink to deliver real-time, AI-generated responses or action commands through a centralized large language model (LLM). As Flink processes incoming customer queries, it sends requests to the LLM server, which generates contextually relevant replies instantly. This setup allows businesses to scale personalized support and automated transactional behavior across multiple channels, providing quick, consistent responses that improve customer satisfaction while keeping model management centralized and easily updatable.

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Implementing remote model inference with Flink on Confluent: Key steps

Step 1: Stream data into Kafka topics via connectors

Data ingestion is essential for real-time applications, and Apache Kafka® is commonly used to bring data into Flink for processing.

In this setup, connectors, Kafka clients, or APIs help stream customer interactions, sensor data, or transaction logs directly to Flink. Kafka clients in languages like Java, Python, Node.js, and C++ provide flexible ways to integrate with various applications. Additionally, a wide range of source connectors can be used, such as those for message brokers, databases with change data capture (CDC), or an HTTP connector for ingesting web events.

Step 2: Configure API calls for inference requests

Once data is ingested, Flink can preprocess the data (e.g., join, enrich, filter if needed) and then make remote API calls to the model server. Flink SQL and Table APIs were extended specifically for AI use cases with the concept of a “model” as a first-class citizen (analogous to a “function” and a “table”).

Users can register the models in Flink by providing an endpoint of their preferred model provider and an access key. Using Flink’s asynchronous I/O operators, developers can send requests to the model server and receive responses in a non-blocking manner. This minimizes processing time and ensures that the pipeline maintains high throughput, even with large volumes of inference requests.

confluent flink connection create azureopenai-connection  --cloud AZURE   --region eastus   --type azureopenai   --endpoint <YOUR_AZUREOPENAI_ENDPONT>   --api-key <YOUR_AZURE_OPENAI_ACCESSKEY>

CREATE MODEL embeddingmodel
INPUT (text STRING)
OUTPUT (response ARRAY<FLOAT>)
WITH (
'provider'='azureopenai', 
'azureopenai.connection'='azureopenai-connection',
'task'='embedding'
);

Once the model is registered, the ML_PREDICT function can be used to generate vector embeddings for retrieval-augmented generation (RAG). Here’s an example of vectorizing real-time product updates for a GenAI-powered shopping assistant:

SELECT id, text, embedding FROM product_updates, LATERAL TABLE(ML_PREDICT('embeddingmodel', text));

Step 3: Optimize network efficiency and latency

Because remote model inference involves network calls, minimizing latency is key. Techniques such as batching requests can reduce the frequency of individual network calls, cutting down on latency. Configuring Flink’s timeout settings and error-handling mechanisms also helps maintain pipeline resilience if network disruptions occur.

Step 4: Monitor and scale the model server independently

As Flink processes live data, the model server should be monitored for load, latency, and accuracy. Cloud-based model servers can be scaled horizontally to meet demand, ensuring that inference requests are processed without bottlenecks. A/B testing and shadow deployments can also be set up on the model server to optimize and validate new models without affecting the primary Flink pipeline.

To see this in action, here is a demo webinar.

Best practices for model inference with Flink 

1. Leverage asynchronous processing: Use asynchronous I/O in Flink to handle remote inference requests without slowing down the data stream, ensuring high throughput and efficient resource usage.

2. Implement robust error handling: Network calls introduce potential points of failure. Set up retries, fallbacks, and timeouts to handle cases where the model server may be temporarily unavailable.

3. Use efficient data encoding: Transmit data in compressed formats like Protocol Buffers or Avro to reduce payload size and latency in network communication, especially for high-frequency inference requests.

4. Monitor model drift: Set up monitoring on the model server to detect any shifts in model performance over time, ensuring that predictions remain accurate as incoming data changes.

5. Optimize cloud resources: For hybrid and cloud-native deployments, ensure that both the model server and the stream processing engine can scale dynamically based on request volume, using auto-scaling and load balancing to maintain cost-effectiveness without sacrificing performance.

Conclusion: Unlocking the full potential of real-time AI with Confluent

Flink AI model inference on Confluent is transforming how organizations deploy machine learning in real-time applications for predictive AI and GenAI use cases, providing a scalable, flexible, and resilient approach to making data-driven decisions. By separating the model server from the streaming application, developers can leverage powerful AI capabilities while keeping Flink applications focused on efficient data processing. This approach is also beneficial in hybrid cloud setups, allowing businesses to deploy scalable, high-performance inference across diverse environments. Apache Flink’s robust support for remote inference makes it a versatile and essential tool for building real-time, AI-driven applications that respond to data at the speed of business.

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