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Seldon

Master Seldon Core to deploy, scale, monitor, and manage machine learning models on Kubernetes with advanced inference graphs, canary releases, and pr

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Course Duration: 10 Hours

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As machine learning systems mature from experimentation into large-scale production, organizations face growing challenges around model deployment, scalability, observability, governance, and lifecycle management. While training models has become increasingly accessible, deploying and operating them reliably in production—especially across multiple teams and environments—remains complex. This complexity increases further in cloud-native and Kubernetes-based infrastructures.

Seldon Core is an open-source, Kubernetes-native platform designed to address these challenges. It provides a standardized, scalable, and extensible way to deploy machine learning models as production inference services. Built specifically for Kubernetes, Seldon Core enables organizations to manage model serving, traffic routing, monitoring, explainability, and experimentation using declarative configuration and cloud-native principles.

Seldon Core supports models built with any machine learning framework, including TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM, and custom Python models. It integrates seamlessly with popular MLOps and DevOps tools such as Kubernetes, Istio, KNative, Prometheus, Grafana, MLflow, Kubeflow, and Argo CD, making it a foundational component of enterprise ML platforms.

Modern AI-driven organizations require more than simple model serving. They need advanced deployment patterns such as canary releases, A/B testing, shadow deployments, inference pipelines, and ensemble models. Seldon Core enables these patterns through inference graphs, which allow multiple models and components to be composed into complex prediction workflows.

The Seldon course by Uplatz provides a comprehensive, hands-on journey into deploying and operating machine learning models using Seldon Core. Learners will gain deep insight into Seldon’s architecture, custom inference components, Kubernetes integration, and production deployment strategies. The course emphasizes real-world use cases, enterprise best practices, and end-to-end MLOps workflows.

By the end of this course, learners will be able to design, deploy, monitor, and scale machine learning inference systems on Kubernetes with confidence—using Seldon as a production-grade MLOps platform.

🔍 What Is Seldon?

Seldon Core is an open-source MLOps framework for deploying machine learning models on Kubernetes. It provides a standardized way to serve, scale, and manage models using Kubernetes-native APIs and custom resources.

Key capabilities include:

Kubernetes-native model deployment
Support for multiple ML frameworks
REST and gRPC inference APIs
Advanced inference graphs and pipelines
Canary releases and A/B testing
Monitoring, logging, and metrics
Explainability and model insights
Autoscaling and traffic management

Seldon Core abstracts the complexity of production ML deployment while remaining highly customizable and extensible.

⚙️ How Seldon Works

Seldon Core is built around Kubernetes Custom Resource Definitions (CRDs) and cloud-native design principles.

1. Kubernetes-Native Architecture

Seldon introduces custom resources such as:

SeldonDeployment
InferenceService (in some integrations)

These resources define how models are deployed, scaled, and exposed within a Kubernetes cluster.

2. Model Serving Containers

Models are packaged as containers or wrapped using Seldon’s built-in servers. Seldon supports:

Pre-built model servers
Custom Python inference servers
Framework-specific runtimes

This enables flexibility across different ML stacks.

3. Inference Graphs

One of Seldon’s most powerful features is inference graphs, which allow:

Model ensembles
Preprocessing and postprocessing steps
Multi-stage inference pipelines
Conditional routing

Inference graphs enable sophisticated production workflows beyond simple single-model serving.

4. Traffic Management & Experimentation

Seldon supports advanced deployment strategies such as:

Canary deployments
A/B testing
Shadow deployments

These techniques allow teams to test new models safely in production.

5. Observability & Explainability

Seldon integrates with:

Prometheus for metrics
Grafana for dashboards
Logging systems for debugging
Explainability tools for model insights

This ensures transparency, accountability, and performance monitoring in production systems.

🏭 Where Seldon Is Used in Industry

Seldon is widely adopted in organizations building cloud-native AI platforms.

1. Enterprise AI Platforms

Centralized model deployment platforms serving multiple teams.

2. Recommendation & Personalization Systems

A/B testing and traffic splitting for ranking models.

3. Financial Services

Risk scoring, fraud detection, and compliance-sensitive inference.

4. Healthcare & Life Sciences

Auditable, explainable ML systems deployed on secure infrastructure.

5. MLOps & Platform Engineering

Standardized ML deployment pipelines on Kubernetes.

6. SaaS & Cloud Products

Scalable ML APIs integrated into customer-facing applications.

Seldon is especially valuable in Kubernetes-first environments.

🌟 Benefits of Learning Seldon

By mastering Seldon, learners gain:

Kubernetes-native ML deployment expertise
Advanced MLOps workflow skills
Experience with production-grade ML platforms
Knowledge of traffic routing and experimentation
Strong observability and governance practices
High-demand skills for enterprise AI roles

Seldon expertise is highly valued in platform engineering and MLOps teams.

📘 What You’ll Learn in This Course

You will learn how to:

Understand Seldon Core architecture
Deploy models using SeldonDeployment
Serve models with REST and gRPC
Build inference graphs and pipelines
Implement canary and A/B deployments
Monitor and debug inference services
Integrate Seldon with Kubernetes ecosystems
Secure and scale production deployments
Manage model lifecycle in enterprise systems
Build end-to-end MLOps workflows

🧠 How to Use This Course Effectively

Start with basic Seldon deployments
Practice deploying simple models
Build inference graphs step by step
Experiment with traffic splitting
Integrate monitoring and logging
Deploy models on Kubernetes clusters
Complete the capstone project

👩‍💻 Who Should Take This Course

This course is ideal for:

MLOps Engineers
Machine Learning Engineers
Platform Engineers
Cloud Engineers
DevOps professionals
Data Scientists moving to production
AI architects and technical leads

🚀 Final Takeaway

Seldon Core brings cloud-native principles to machine learning deployment. It enables organizations to deploy, manage, and scale ML models reliably on Kubernetes while supporting advanced experimentation, observability, and governance.

By completing this course, learners gain the skills needed to operate production-grade ML systems that are scalable, transparent, and enterprise-ready—making Seldon a cornerstone of modern MLOps platforms.

Course Objectives Back to Top

By the end of this course, learners will:

Understand Seldon Core internals
Deploy and manage models on Kubernetes
Build advanced inference pipelines
Implement canary and A/B deployments
Monitor and explain model predictions
Operate Seldon in production environments

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to Seldon

ML deployment challenges
Why Kubernetes-native serving

Module 2: Seldon Core Architecture

CRDs and controllers
Serving components

Module 3: Deploying Models

SeldonDeployment basics
REST and gRPC endpoints

Module 4: Inference Graphs

Pipelines and ensembles
Routing logic

Module 5: Advanced Deployment Strategies

Canary releases
A/B testing

Module 6: Observability & Monitoring

Metrics and logs
Explainability

Module 7: Scaling & Performance

Autoscaling
Resource management

Module 8: Security & Governance

Access control
Auditing

Module 9: Production Best Practices

CI/CD integration
Platform operations

Module 10: Capstone Project

Deploy an enterprise-ready ML platform using Seldon

Certification Back to Top

Upon completion, learners receive a Uplatz Certificate in Seldon & Kubernetes-Native MLOps, validating expertise in production ML deployment on Kubernetes.

Career & Jobs Back to Top

This course prepares learners for roles such as:

MLOps Engineer
Machine Learning Platform Engineer
AI Infrastructure Engineer
Cloud AI Architect
Applied Machine Learning Engineer

Interview Questions Back to Top

What is Seldon Core?
A Kubernetes-native platform for deploying ML models.
Which frameworks does Seldon support?
TensorFlow, PyTorch, Scikit-learn, and more.
What are inference graphs?
Composable ML pipelines for complex inference workflows.
Does Seldon support A/B testing?
Yes.
Is Seldon cloud-native?
Yes, it is built for Kubernetes.
Which APIs does Seldon support?
REST and gRPC.
Can Seldon scale automatically?
Yes, via Kubernetes autoscaling.
Is Seldon open source?
Yes.
Who should use Seldon?
Teams deploying ML models on Kubernetes.
What problem does Seldon solve?
Reliable, scalable, and observable ML deployment.

Course Quiz Back to Top

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