バッチ推論とは何か、そしてなぜ重要なのか？

In machine learning, building a model is only the first step. Its real value comes when it is used to make predictions that support business decisions. This stage is called inference, and it usually happens in two ways: real-time inference for instant results, or batch inference for large-scale scheduled processing.

Batch inference is widely used to process large volumes of data efficiently and at lower cost when immediate responses are not required. It plays a key role in many data-heavy AI systems.

This guide explains what batch inference is, why it matters, how it compares with real-time inference, and more.

What is Batch Inference?

Batch inference, also known as offline inference, is the process of using a machine learning model to generate predictions for a large set of data at one time. Instead of scoring each record as it arrives, data is collected over a defined period and processed on a scheduled basis, such as hourly, daily, or weekly.

The main objective of batch inference is high throughput rather than low latency. It is built to handle large volumes of data efficiently, making it ideal for use cases where immediate predictions are unnecessary and results can be generated in advance, stored, and used when needed.

Key characteristics of Batch Inference

The core methodology of batch inference is defined by several distinct characteristics that set it apart from other deployment strategies.

• High-Volume Data Processing: It is specifically designed to handle large datasets, from thousands to billions of data points in a single run.
• Scheduled Execution: Inference jobs are not triggered by individual user requests but are run automatically at predetermined intervals using schedulers like cron jobs or orchestration tools like Apache Airflow.
• Asynchronous Operation: The system that requests the predictions does not wait for an immediate answer. The results are stored in a database, data lake, or file system to be accessed when needed.
• Throughput over Latency: The key performance metric is the volume of data processed over a period (throughput), not the speed of a single prediction (latency).

Why is Batch Inference important?

Batch inference is important because it provides a scalable and cost-effective way to run machine learning models on large datasets when instant predictions are not needed.

• Cost Efficiency and Resource Optimization: Batch jobs can run during off-peak hours or on lower-cost infrastructure. Processing data in groups also improves the use of GPUs, TPUs, and CPUs, helping reduce overall compute costs.
• High Throughput for Large-Scale Datasets: It is well suited for scoring millions of records, large product catalogs, or historical datasets. Batch systems are built to process high data volumes quickly and reliably.
• Simplified Infrastructure and Scheduling: Compared with real-time inference APIs, batch systems are often easier to build, schedule, and maintain, with less operational complexity.

Improved Hardware Utilization (GPUs, TPUs, and CPUs): Modern accelerators perform best when handling many tasks in parallel. Batch inference takes advantage of this by processing multiple inputs at once for faster and more efficient predictions.

Batch Inference vs. Real-time Inference

The main difference between batch inference and real-time inference is when predictions are generated and how quickly results are needed. Choosing between them depends on the requirements of the application.

Batch inference processes large volumes of data on a scheduled basis, such as hourly, daily, or overnight. Predictions are generated in bulk and stored for later use. It is built for high throughput and efficiency rather than instant response times. 

For example, an e-commerce company may run a nightly job to generate product recommendations for all users before the next day.

Real-time inference, also known as online inference, generates predictions the moment new data arrives. It usually operates through an API and returns results within milliseconds. This makes it ideal for applications that require immediate decisions. 

For example, a fraud detection system must evaluate a transaction instantly before approving or blocking it.

In simple terms, use batch inference when predictions can be prepared ahead of time, and use real-time inference when results are needed immediately.

What about the middle ground? Streaming and micro-batch inference

In practice, many production systems don't fit neatly into either category. A third pattern, sometimes called streaming inference or micro-batch inference, sits between the two extremes. Instead of waiting for a large batch to accumulate, micro-batching processes small groups of records in near-real-time using short rolling time windows (e.g., every few seconds or minutes).

This approach is common in LLM serving, where tools like vLLM use continuous batching to group incoming requests dynamically as they arrive, rather than waiting for a full batch or processing them one by one. Ray Serve and Apache Flink are also commonly used to build streaming inference pipelines.

Micro-batching is a good fit when you need lower latency than traditional batch jobs but cannot justify the infrastructure cost of a fully real-time system.

Also read: What Is AI Model Deployment

When should you use Batch Inference?

You should use batch inference when predictions are needed for large volumes of data, but not in real time. It is the best choice when efficiency, scalability, and cost savings matter more than instant responses. Here are the ideal use cases for batch inference:

• Recommendation Systems and Personalization: Generating daily or weekly product, movie, or content recommendations for an entire user base. The recommendations are computed offline and stored, ready to be served quickly when a user visits the site or app.
• Fraud Detection and Risk Scoring: While real-time inference is used to block fraudulent transactions, batch inference is used to run complex models over historical data to identify fraud rings, discover new suspicious patterns, and calculate weekly risk scores for accounts.
• Natural Language Processing and Document Classification: Classifying, summarizing, or running sentiment analysis on a large corpus of documents, articles, or customer reviews that have been collected over time.
• Image and Video Processing at Scale: Analyzing an entire library of images or videos for object detection, content moderation, or tagging. For example, processing all new video uploads to a platform each day.
• ETL Pipelines and Feature Engineering: As part of a data pipeline, batch inference can be used to generate predictive features (e.g., a customer's lifetime value score) that are then stored in a feature store for other models to use.
• Predictive Analytics and Forecasting: Generating business forecasts for sales, demand, or inventory on a weekly or monthly basis.

Where Batch Inference is not the right choice

Batch inference is not suitable when predictions must be made instantly in response to live events. In these cases, delays from scheduled processing are unacceptable.

Examples include live fraud detection during a card payment, real-time ad bidding, instant language translation, and dynamic pricing based on current market activity.

What are the key components of a Batch Inference System?

A batch inference system is a pipeline of connected components that work together to generate predictions at scale. Each layer plays a specific role in moving data, running models, and delivering results reliably. Take a look at the components of batch inference: 

• Data Ingestion & Storage Layer: This layer collects and stores the raw data used for predictions. It is typically a data lake or data warehouse such as AWS S3, Google Cloud Storage, Snowflake, or BigQuery.
• Model Registry: A model registry stores, versions, and manages trained machine learning models. It helps teams track model metadata and ensures the correct model version is used during inference. Common tools include MLflow and Vertex AI Model Registry.
• Orchestration & Scheduling Layer: This layer controls when jobs run and manages workflow dependencies. It schedules pipelines, handles retries, and monitors execution. Popular tools include Apache Airflow, Prefect, and Dagster.
• Batch Inference Engine (Compute Layer): The compute layer performs the actual predictions. It often uses distributed processing frameworks such as Apache Spark, Ray, or Dask to run inference in parallel across many machines.
• Prediction Store (Output Layer): After predictions are generated, results are stored for downstream applications and analytics teams. Common destinations include databases, data warehouses, or files in formats like Parquet or Delta Lake.
• Monitoring & Observability: This layer tracks system health, pipeline failures, runtime performance, data drift, model decay, and SLA compliance. Tools such as Prometheus, Grafana, and ML monitoring platforms are commonly used.

Also read: On-Premise LLM Deployment: Secure & Scalable AI Solutions

How does a Batch Inference work?

Batch inference works by collecting data, processing it through a machine learning model in groups, and storing the predictions for later use. The workflow is usually automated and scheduled to run at regular intervals.

Data Collection and Preprocessing

The process starts by pulling data from sources such as databases, data lakes, or warehouses. This raw data is then cleaned, transformed, and prepared so it matches the input format required by the model.

Model Loading and Batch Preparation

Next, the system loads the required version of the trained model from the model registry. The prepared data is divided into smaller batches, with batch size chosen to optimize speed and hardware performance.

Running Predictions

Each batch is sent through the model to generate predictions. Using GPUs, TPUs, or distributed systems, many records can be processed at the same time, allowing high-throughput inference.

Post-Processing and Storage

The model outputs may need additional processing, such as converting probabilities into labels, formatting results, or combining predictions with business data. The final outputs are then stored in a database, warehouse, or file system for later access.

Scheduling and Orchestration

The full pipeline is managed by orchestration tools such as Apache Airflow, Prefect, or cron jobs. These tools schedule runs, manage dependencies between tasks, and handle retries or failures automatically.

How to optimize Batch Inference performance?

Optimizing batch inference performance requires a balanced approach that improves throughput, lowers costs, and maximizes the efficient use of compute resources. Take a look:

• Choose the Right Batch Size: Larger batch sizes can improve GPU and CPU utilization, but they also require more memory. Test different sizes to find the best balance for your model and hardware.
• 並列処理と分散処理を活用する：大規模なワークロードの場合、Apache SparkやRayのようなフレームワークを使用し、複数のマシンにジョブを分割して処理を高速化します。
• モデルの最適化： 量子化、プルーニング、ONNX変換などの技術は、モデルサイズを削減し、速度を向上させ、メモリ使用量を削減できます。
• データ読み込みの改善： Parquetのような効率的なストレージ形式と最適化されたデータパイプラインを使用することで、I/Oボトルネックを削減し、読み込みを高速化できます。
• パフォーマンスの監視： ジョブの実行時間、ハードウェア利用率、レイテンシなどのメトリクスを追跡し、ボトルネックを特定して、時間とともに効率を改善します。

なぜLLMはバッチ推論から恩恵を受けるのか？

大規模言語モデル（LLM） はバッチ推論から恩恵を受けます。その高い計算要件により、単一リクエストの処理は非効率的で高価になるためです。LLMにとってバッチ推論が重要である理由は以下の通りです。

• 大幅なコスト削減：LLMは強力で高価なGPUを必要とします。バッチ処理により、多くのリクエストを単一のGPUで並行して処理できるため、その利用率を最大化し、アイドル状態になる可能性のあるリアルタイムエンドポイントと比較して、予測あたりのコストを大幅に削減します。
• スループットと効率の向上：多数のドキュメントの要約やデータベースの埋め込み生成といったタスクでは、バッチ処理がはるかに効率的です。これにより、LLMは数千の入力を単一の最適化された実行で処理できます。
• インフラストラクチャの簡素化：LLMのバッチジョブを設定することは、リアルタイムトラフィックに対応するオートスケーリング、ロードバランシング、GPU管理を備えた、スケーラブルで低レイテンシのAPIを構築するよりも、多くの場合複雑ではありません。
• 信頼性の高い大規模処理：バッチ推論は、LLMで大規模なデータセットを処理するためのフォールトトレラントなフレームワークを提供します。オーケストレーションツールは、リトライと依存関係を管理し、大規模なジョブが確実に成功裏に完了するようにします。

こちらもご覧ください： LLM推論：速度、コスト、AIのスケーリングを最適化

バッチ推論実装のためのベストプラクティス

堅牢で信頼性の高いバッチ推論システムを実装するには、安定性、保守性、データ整合性を確保するためのいくつかの重要なベストプラクティスを採用する必要があります。

• べき等でリトライセーフなパイプラインの設計：パイプラインはべき等であるべきです。これは、同じ入力で複数回実行しても同じ結果が得られることを意味します。これにより、ジョブが失敗後に再実行される必要がある場合に、データの重複や破損を防ぐことができます。
• スケジューリングと依存関係管理の自動化：単純なcronジョブの代わりに、AirflowやPrefectのような専用のオーケストレーションツールを使用してください。これらのツールは、複雑なワークフローに対して、より優れた可視性、依存関係管理、エラー処理を提供します。
• データ検証と品質チェックの実装：推論を実行する前に、入力データが品質基準を満たしていることを検証してください。この「ゴミを入れればゴミが出る（Garbage In, Garbage Out）」という原則は、モデルが不良データに基づいて信頼性の低い予測を生成するのを防ぎます。
• 本番デプロイ前のテストとステージング：推論パイプラインは他のソフトウェアアプリケーションと同様に扱ってください。バグやパフォーマンスの問題を早期に発見するために、本番環境にデプロイする前に、現実的なデータを使用してステージング環境で徹底的にテストしてください。

まとめ

バッチ推論は、本番環境における機械学習の中核をなす要素であり、組織が大規模なデータセットを効率的かつ大規模に処理するのに役立ちます。リアルタイムシステムのように即座の予測は提供しませんが、スループットとコスト効率が最も重要となる場合には、より良い選択肢となります。

レコメンデーションエンジンやビジネスレポート作成から 大規模なLLMワークロードまで、バッチ推論は、保存されたデータを有用な予測と洞察に変えます。

そのアーキテクチャとベストプラクティスを理解することで、チームは真のビジネス価値を提供する、信頼性が高く、スケーラブルで、費用対効果の高いAIシステムを構築できます。

TrueFoundryは、訓練済みAIモデルで大規模なデータセットを簡単に処理できるようにすることで、チームがバッチ推論を大規模に実行するのを支援します。Jobsを使用すると、チームは並列ワークロードを効率的に実行し、バッチパイプラインを自動化し、大規模な推論タスクを確実に管理できます。その一方で AI Gateway は、複数のAIモデルへの安全で統一されたアクセスを提供します。

デモを予約する TrueFoundryの動作をご覧いただくために。