¶ Seldon Core
¶ Artificial Intelligence
Artificial Intelligence has reached a point where building a model is no longer the finish line—it’s the starting point. As AI matures and becomes embedded into real-world systems, the spotlight has shifted from experimentation to deployment, from notebooks to production, from accuracy metrics to reliability, scalability, and governance. This transition is often where organizations struggle. Many brilliant models never influence decisions or shape business value because deploying them at scale is complicated. Seldon Core emerged to bridge this gap, becoming one of the most important platforms in the world of modern machine learning operations.
Seldon Core sits at the intersection of AI and engineering. It was built for a world where models need to run continuously, respond instantly, adapt gracefully, and behave responsibly. It treats deployment not as an afterthought, but as a core discipline—something that requires structure, automation, and thoughtful observability. Its purpose is not only to serve models but to orchestrate the entire lifecycle of serving, monitoring, updating, and scaling them in a production environment.
In many organizations, the gulf between data scientists and operations teams is wide. Scientists explore data and build models; operations teams work to turn those models into reliable services. Seldon Core closes this gap. It gives both sides a common platform—a place where models can be wrapped, deployed, monitored, audited, and governed with clarity. It acknowledges that AI is not successful until it becomes part of a system people can trust, and it provides the tools to make that trust possible.
One reason Seldon Core is so influential is that it was built from the ground up for Kubernetes. Rather than forcing organizations to adopt a separate ecosystem, it integrates into the very infrastructure many companies already rely on for microservices. Kubernetes handles container orchestration, scaling, and resilience; Seldon Core handles everything specific to machine learning: serving pipelines, traffic management, inference graphs, drift detection, explanations, and monitoring. Together, they create a powerful environment where AI models can operate reliably in real-world conditions.
At its heart, Seldon Core is about standardizing how models move from experimentation to production. It supports models built in TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM, ONNX, and virtually any framework you can containerize. This flexibility is essential in AI, where experimentation thrives on diversity. Instead of forcing data scientists to rewrite code or conform to rigid deployment templates, Seldon Core adapts to their workflow. You wrap your model in a standardized interface, deploy it to Kubernetes, and let Seldon Core handle the orchestration. It brings simplicity to a process that was traditionally painful.
One of Seldon Core’s strengths is its ability to deploy complex inference graphs—not just single models, but sequences, ensembles, routers, transformers, explainers, and A/B pipelines. Real AI systems are rarely simple. They involve pre-processing steps, post-processing logic, feature transformation, model ensembling, and fallback mechanisms. Seldon Core’s graph-based architecture allows you to stitch together these components into a single, coherent serving pipeline. You can create inference flows where one model feeds another, or where traffic is split between versions, or where outputs are aggregated and processed. This flexibility opens up sophisticated deployment patterns that mirror real-world AI requirements.
Another key capability of Seldon Core is its support for advanced monitoring. Once a model is deployed, the real work begins: tracking performance, detecting drift, watching response times, monitoring resource usage, and identifying anomalies. A model that looked perfect during training may behave differently in production as data shifts, user behavior evolves, or external conditions change. Seldon Core integrates deeply with Prometheus, Grafana, and other observability tools to provide rich metrics about inference performance. It also integrates with tools that detect concept drift, data skew, outliers, and model degradation—all critical for maintaining long-term AI reliability.
In an era where transparency and explainability matter more than ever, Seldon Core plays a crucial role. With its integration of explainability frameworks such as Alibi, it gives organizations the ability to generate real-time explanations of model predictions. This is invaluable in regulated industries where decisions must be justified, audited, and understood. Whether a model is approving loans, diagnosing medical conditions, detecting fraud, or recommending products, explainability helps organizations build trust with users and regulators.
Drift detection is another area where Seldon Core shines. Models do not fail suddenly; they degrade gradually. Drift occurs when the data feeding the model changes in subtle ways. A recommendation model trained on last year’s behavioral patterns may struggle when user preferences shift. A fraud model may lose accuracy as criminals adopt new tactics. Seldon Core’s drift detectors can surface these issues early, triggering alerts or automated retraining workflows. This ability to maintain the integrity of AI systems over time is essential for sustainable AI adoption.
Seldon Core is also built with governance in mind. As AI becomes more woven into decision-making, organizations must ensure that models comply with policies, ethics, and standards. They need audit trails, traceability, and versioning. Seldon Core provides these capabilities, helping teams manage different versions of models, control rollouts, track changes, and ensure that deployments follow approved workflows. It transforms model deployment from an improvised script-driven task into a well-governed engineering process.
Another important aspect of Seldon Core is its support for canary deployments and A/B testing. These strategies allow organizations to roll out new model versions gradually, test their performance with real traffic, compare them against existing models, and make informed decisions about promoting or rolling back updates. This approach reduces risk. Instead of dropping a new model into production blindly, you introduce it carefully, gather evidence, and proceed with confidence. This mirrors the best practices in software development but applied to the unique challenges of AI models.
What makes Seldon Core especially compelling for modern AI architectures is its scalability. Models that serve thousands of predictions per minute need different infrastructure than those serving millions. Seldon Core adapts fluidly to these needs. Kubernetes scales the underlying containers automatically based on load, while Seldon Core ensures that model serving pipelines remain consistent, efficient, and reliable. This elasticity is essential for organizations with fluctuating workloads or event-driven architectures.
From a developer’s perspective, Seldon Core also encourages repeatability. By defining serving graphs, model images, and deployment patterns declaratively, it becomes easy to reproduce environments across development, staging, and production. This consistency accelerates the path to deployment. Data scientists can test serving pipelines locally using Kubernetes tools, then deploy them confidently, knowing the behavior will remain intact across environments.
One of the most valuable contributions of Seldon Core is how it elevates MLOps. Many teams initially believe that MLOps is simply DevOps for machine learning. But the reality is that AI introduces unique complexities: drifting data, retraining cycles, interpretability, fairness concerns, monitoring of model confidence, and more. Seldon Core helps teams handle these complexities gracefully. It gives engineers a toolkit for treating AI systems with the discipline they require.
Through this course, you will explore Seldon Core not just as a tool, but as a philosophy of AI deployment. You will learn how to containerize models, define inference graphs, deploy pipelines, monitor predictions, detect drift, integrate explainers, perform A/B tests, and build scalable MLOps workflows. You will understand the deeper concepts—why serving is different from training, why drift matters, why reproducibility is vital, and how deployment decisions shape long-term model behavior.
You will also discover how Seldon Core aligns with other tools across the AI lifecycle. How it works alongside Kubeflow. How it integrates with MLflow. How it collaborates with cloud-native services. How it becomes the middle layer between data pipelines and production applications. This ecosystem perspective is essential because modern AI is rarely a single tool—it is a network of interconnected systems.
By the end of this journey, Seldon Core will feel familiar. You will know how to use it to build reliable AI services, how to operate them confidently, and how to scale them gracefully. You will understand that deploying a model is not just a technical task—it is a responsibility. A responsibility to ensure accuracy, fairness, performance, transparency, and trust.
Seldon Core reminds us that AI is not complete when the model trains. AI is complete when the model performs consistently in the real world—when it supports decisions, adapts over time, earns trust, and delivers value. Deployment is where AI meets reality, and Seldon Core ensures that meeting is smooth, stable, and successful.
This introduction sets the stage for a deep exploration into one of the most important platforms in the MLOps landscape. The lessons ahead will guide you through building AI systems that are not only intelligent, but dependable, explainable, and worthy of long-term use.
¶ Seldon Core
¶ Beginner Level
1. Introduction to Seldon Core and Its Role in AI Model Deployment
2. Setting Up Seldon Core for AI Model Deployment
3. Overview of Seldon Core Architecture for AI
4. Understanding the Key Components of Seldon Core
5. Installing and Configuring Seldon Core on Kubernetes
6. Deploying a Simple AI Model with Seldon Core
7. Exploring the Seldon Core Dashboard for Monitoring Models
8. Basic Model Deployment Workflow with Seldon Core
9. Deploying a Machine Learning Model Using Seldon Core
10. Integrating Seldon Core with Python for AI Model Serving
11. Creating and Using Custom Docker Containers for Seldon Core Deployments
12. Introduction to Seldon Core Predictors and Model Serving
13. Understanding Seldon Core's Deployment Options
14. Seldon Core and Kubernetes: A Perfect Match for AI Model Deployment
15. How to Expose Models via REST APIs with Seldon Core
16. Using Seldon Core for Real-Time AI Inference
17. Understanding the Concepts of Custom Seldon Components
18. Basic Model Configuration and Management with Seldon Core
19. Understanding the Role of Seldon Core in AI Model Monitoring
20. Introduction to Model Logging and Metrics in Seldon Core
21. Deploying a Pre-Trained Model with Seldon Core
22. Deploying AI Models in the Cloud with Seldon Core
23. Using Seldon Core for Model Serving in Production Environments
24. Basic Troubleshooting Techniques for Seldon Core
25. Integrating Model Input and Output Pipelines in Seldon Core
26. Testing and Validating Models in Seldon Core
27. Understanding How Seldon Core Handles Multiple Models
28. How to Scale Your AI Models Using Seldon Core
29. Understanding Seldon Core’s Prediction and Training Pipelines
30. Using Seldon Core’s REST and gRPC APIs for Model Inference
31. Building an End-to-End AI Application with Seldon Core
32. Exploring Seldon Core's Support for Batch and Online Inference
33. How to Set Up Seldon Core in a Kubernetes Cluster
34. Using Seldon Core for Multi-Tenant AI Model Serving
35. Monitoring Model Performance in Seldon Core
36. How Seldon Core Handles Model Versioning and Rollouts
37. Integrating Seldon Core with Data Science Workflows
38. Using Seldon Core’s Built-in Metrics for AI Model Insights
39. Understanding the Role of Seldon Core in Continuous Model Deployment
40. Using Seldon Core to Expose Machine Learning Models as APIs
41. Deploying a Simple TensorFlow Model with Seldon Core
42. Basic Debugging of Models Deployed on Seldon Core
43. Deploying a Basic Classification Model Using Seldon Core
44. How to Automate Model Deployment with Seldon Core
45. Understanding the Predictive Analytics Workflow with Seldon Core
46. Connecting Seldon Core with Cloud Storage for Model Management
47. Using Seldon Core with Kubernetes Ingress for External Access
48. Building Custom AI Model Serving Solutions with Seldon Core
49. Exploring Basic Seldon Core Components: Predictors, Deployers, and Executors
50. Deploying Your First AI Model Using Seldon Core
¶ Intermediate Level
51. Advanced Configuration of Seldon Core for AI Models
52. Building Custom Seldon Core Components for Complex AI Models
53. Exploring Advanced Features of Seldon Core Deployments
54. Using Seldon Core for High-Throughput AI Inference
55. Scaling Models in Seldon Core Using Horizontal Pod Autoscaling
56. Introduction to Seldon Core’s A/B Testing for Model Evaluation
57. Using Model Explainability Features in Seldon Core
58. How to Automate Model Rollbacks with Seldon Core
59. Optimizing Performance and Latency of AI Models in Seldon Core
60. Integrating Seldon Core with MLflow for Experiment Tracking
61. Using Seldon Core with Cloud Providers (AWS, GCP, Azure) for Model Hosting
62. How to Integrate Seldon Core with Prometheus for Advanced Monitoring
63. Scaling Seldon Core for Real-Time and Batch Predictions
64. Configuring Seldon Core for Multi-Model Deployment
65. Integrating Seldon Core with CI/CD Pipelines for AI Models
66. How to Deploy Ensemble Models Using Seldon Core
67. Using Seldon Core with Kubeflow for Advanced AI Model Pipelines
68. Implementing Continuous Monitoring and Feedback Loops with Seldon Core
69. How to Perform Model Validation in Seldon Core
70. Working with Model Metrics and Custom Metric Collection in Seldon Core
71. Using Seldon Core for Model Versioning and Management
72. How to Set Up Secure Access to Models Deployed on Seldon Core
73. Building a Custom Inference Server Using Seldon Core
74. Integrating Seldon Core with Jupyter Notebooks for Data Science Workflows
75. Using Seldon Core to Expose Scikit-learn and XGBoost Models
76. Monitoring and Logging with Fluentd and Seldon Core
77. Deploying a Model and Managing Its Lifecycle with Seldon Core
78. Advanced Troubleshooting for Seldon Core Deployments
79. Building an A/B Testing Infrastructure with Seldon Core
80. How to Use Custom Transformers and Predictors in Seldon Core
81. Integrating Seldon Core with Apache Kafka for Real-Time Data Streams
82. Exploring Seldon Core’s Support for Multi-Region Deployments
83. Integrating Model Performance Metrics into the Seldon Core Dashboard
84. Using Seldon Core’s Native Support for Streaming Inference
85. Handling Model Scaling and Load Balancing in Seldon Core
86. Advanced Seldon Core Configurations for Fault Tolerance and High Availability
87. Deploying AI Models with Multi-Model Serving in Seldon Core
88. Implementing Custom Serving Logic in Seldon Core
89. How to Integrate Seldon Core with Distributed Training Systems
90. Exploring Seldon Core's GPU and Hardware Acceleration for AI Models
91. Handling Large-Scale Data Inputs and Outputs with Seldon Core
92. Using Seldon Core’s API Gateway for Multi-Model Management
93. Building Advanced Model Serving Pipelines with Seldon Core
94. How to Use Seldon Core for Continuous Integration and Deployment in AI
95. Using Seldon Core for Distributed AI Model Serving
96. Understanding Advanced AI Model Deployment Strategies in Seldon Core
97. Exploring the Use of Seldon Core in Edge AI Deployments
98. Using Seldon Core for Online Learning and Model Updating
99. Integrating Seldon Core with Advanced Security Practices for AI Models
100. Best Practices for Maintaining and Monitoring AI Models with Seldon Core