Seldon Core

Description

Seldon Core 2 is a comprehensive MLOps and LLMOps framework designed for the deployment, management, and scaling of AI systems within Kubernetes environments. It provides a standardized approach to deploying diverse model types, whether on-premises or in any cloud, ensuring production-ready applications out of the box. This framework is particularly adept at handling singular models as well as complex, modular, and data-centric applications.

The framework's capabilities extend to building composable AI applications through pipelines, which can leverage technologies like Kafka for real-time data streaming between different components. It offers robust autoscaling features for both models and application components, driven by native or custom logic. For cost efficiency, Seldon Core supports multi-model serving, allowing consolidation of multiple models onto shared inference servers, and overcommit functionality to deploy more models than available memory permits, reducing infrastructure costs for underutilized resources.

Seldon Core also facilitates experimentation through its routing capabilities, enabling A/B tests and shadow deployments for candidate models or pipelines. Users can implement custom logic, drift and outlier detection, and integrate with Large Language Models (LLMs) through plug-and-play custom components, seamlessly integrating with Seldon's broader ecosystem of ML/AI products. The framework is influenced by research into the next generation of ML model serving frameworks, aiming to address key desiderata for advanced model serving.

Installation and configuration are managed within Kubernetes, with extensive documentation available for servers, models, pipelines, experiments, and performance tuning. Seldon Core is distributed under the terms of the Business Source License, with all contributions also licensed under this term. The project is actively developed, with a strong community presence and regular updates, making it a powerful tool for organizations looking to operationalize their AI initiatives at scale.

Seldon Core's Core Features

• MLOps and LLMOps framework

• Kubernetes-native deployment

• Standardized model deployment

• Modular and data-centric AI applications

• Composable AI pipelines

• Real-time data streaming with Kafka

• Autoscaling for models and components

• Multi-model serving for cost efficiency

• Overcommit for increased model density

• A/B testing and shadow deployments

• Custom component integration (LLMs, drift detection)

• Production-ready out-of-the-box

• On-premise and cloud deployment support

How to use Seldon Core?

1. 1

   Deploy: Package and deploy your machine learning models and AI applications on Kubernetes.

2. 2

   Configure: Set up pipelines, autoscaling, and multi-model serving configurations.

3. 3

   Integrate: Incorporate custom components for advanced logic and LLMs.

4. 4

   Monitor: Utilize built-in monitoring capabilities for production systems.

5. 5

   Manage: Oversee and manage thousands of production machine learning models.

6. 6

   Optimize: Tune performance and experiment with different model routing strategies.

Seldon Core's Use Cases

• Model Deployment
• AI Application Orchestration
• Real-time Inference
• Cost Optimization
• Model Experimentation
• LLMOps
• Data-Centric AI

FAQ from Seldon Core

• What is Seldon Core?

  Seldon Core is an MLOps and LLMOps framework for packaging, deploying, monitoring, and managing thousands of production machine learning models and AI systems on Kubernetes.

• What are the key benefits of using Seldon Core?

  Key benefits include standardized deployment across clouds, modular application building, autoscaling, cost-efficient multi-model serving, and advanced experimentation capabilities.

• Can Seldon Core handle complex AI applications?

  Yes, Seldon Core supports building composable AI applications through pipelines and custom components, enabling sophisticated logic and LLM integration.

• How does Seldon Core help with cost optimization?

  It offers multi-model serving to consolidate models on shared servers and overcommit functionality to deploy more models than available memory allows, saving infrastructure costs.

• What deployment environments does Seldon Core support?

  Seldon Core supports deployment on-premise or in any cloud environment, leveraging Kubernetes for orchestration.

• What is the role of pipelines in Seldon Core?

  Pipelines allow users to deploy composable AI applications, enabling real-time data streaming between components, often utilizing Kafka.

• Does Seldon Core support custom logic and LLMs?

  Yes, it allows the implementation of custom logic, drift and outlier detection, and integration with LLMs through plug-and-play custom components.

• What kind of experimentation features are available?

  Seldon Core supports routing data between candidate models or pipelines, including A/B tests and shadow deployments.

• What is the licensing for Seldon Core?

  Seldon Core is distributed under the terms of the Business Source License.

• What programming languages are used in Seldon Core?

  The primary language used in the Seldon Core repository is Go, with significant use of Jupyter Notebooks and Kotlin.