Distributed Machine Learning

Train and deploy machine learning models across a distributed compute fleet, optimizing performance, reducing data movement, and enabling large-scale parallelism.

Overview

Scaling machine learning workloads across multiple regions and compute environments is a complex task. Traditional approaches require transferring large datasets to a central location for processing, leading to high costs and inefficiencies. Bacalhau simplifies distributed machine learning by allowing model training, inference, and data processing to occur where the data resides, leveraging distributed compute resources to improve efficiency and scalability.

Key Capabilities

1. Distributed Training & Parallel Execution

Bacalhau enables ML workloads to be split across multiple nodes, allowing for faster and more efficient training by parallelizing computations.

• Distribute training across multiple machines to handle larger models and datasets.

• Process subsets of data locally and aggregate results, reducing the need for excessive data transfer.

• Enable federated learning approaches where training occurs on edge nodes without centralizing data.

2. Optimized Data Movement & Local Processing

Moving large datasets for ML training is expensive and slow. Bacalhau optimizes data locality by running computations closer to the data source.

• Perform feature extraction and preprocessing on data where it resides.

• Train models across multiple data centers without requiring full dataset replication.

• Minimize network costs by aggregating model updates instead of raw data.

3. Scalable Model Deployment & Inference

Bacalhau simplifies deploying ML models across a distributed fleet for real-time or batch inference.

• Deploy inference jobs across multiple regions to serve predictions with low latency.

• Run model evaluation and A/B testing across different compute clusters.

• Dynamically allocate compute resources based on demand.

4. Federated Learning & Privacy-Preserving ML

For privacy-sensitive applications, Bacalhau supports federated learning by keeping data decentralized while training global models.

• Train models on distributed data sources without exposing raw data.

• Aggregate model updates instead of datasets, preserving data privacy.

• Improve compliance with data sovereignty regulations while enabling large-scale ML training.

Example Use Cases

• Training a deep learning model across multiple GPUs in different regions.

• Running distributed hyperparameter tuning without moving raw datasets.

• Deploying inference jobs near users for low-latency predictions.

• Aggregating model updates from different locations in federated learning scenarios.

• Processing large-scale unstructured data (e.g., video, images, logs) directly where it is stored.

Next Steps

To get started with distributed ML using Bacalhau:

1. Deploy compute nodes where training and inference workloads need to run.

2. Schedule distributed training jobs to parallelize computation and optimize data movement.

3. Deploy trained models across a distributed fleet for scalable, low-latency inference.

By leveraging Bacalhau’s distributed execution model, machine learning workloads can scale efficiently, reduce infrastructure costs, and enable privacy-aware training while optimizing resource usage across multiple regions.