Build scalable data applications and deploy containerized workloads using Snowflake Snowpark Container Services.

Learn how to use Snowpark Container Services to develop, deploy and manage containerized applications, machine learning workloads and custom data processing solutions within Snowflake cloud environments.

  • Why get trained: Learn how to deploy containerized applications, manage Snowpark workloads and integrate advanced data processing and AI-driven workflows within Snowflake environments.
  • Why it matters: Snowpark Container Services capabilities help organizations modernize data application development, improve scalability and support advanced analytics and AI workloads within secure cloud-native architectures.
  • Who should attend: Data engineers, cloud engineers, platform architects, developers and IT professionals responsible for modern data platform operations and cloud-native application deployment.

Build Snowflake cloud-native application and container services capabilities with Trainocate. HRD Corp Claimable.

Overview

In this one‐day course, learners explore Snowpark Container Services and learn about running container‐based workloads in Snowflake.

This course outlines Snowflake‐recommended best practices, and learners will walk away with experience building their own container‐based Snowflake assets within the Snowflake training environment.

Skills Covered

  • Build containers for your workloads.
  • Configure and manage an image registry.
  • Configure and manage compute pools.
  • Establish an ingress connection for access from outside Snowflake.
  • Summarize Snowflake’s recommended best practices and cost management approach.

Prerequisites

  • Recommended completion of the “Snowflake Multi-Factor Authentication (MFA) Essentials” free on-demand course.
  • Completion of “Snowflake Foundations” one-day course or equivalent Snowflake knowledge.
  • Basic knowledge of SQL is required.
  • Foundational knowledge of databases is recommended.

Target Audience

  • Snowflake users looking to take the fast track to utilize container-based workloads in Snowflake.
  • Administrators
  • Application Developers
  • Data Engineers
  • Data Scientists
  • Anyone with the prerequisite skills who wants to work with Snowpark Container Services. Previous experience with containers is highly recommended, but not required.

Course Curriculum

Overview of Snowpark Container Services

  • What is a Container?
  • Use Cases
  • How are Containers Used in Snowflake?

Container Lifecycle in Snowflake

  • Container Creation
  • Variable Management
  • Role-based Access Control (RBAC)

Snowflake Image Registry and Repository

  • Working with Image Repositories

Compute Pools Explained

  • Compute Pool Creation
  • Instance Family
  • Autoscaling Compute Pool Nodes
  • Compute Pool Lifecycle

Services and Service Functions

  • Creating Service Instances With Autoscaling
  • Service Persistent Storage Options
  • Endpoint Configuration
  • Updating Service Code

Application Observability

  • Managing Container Services
  • Accessing Container Logs
  • Using Event Tables
  • A Guide to Common Errors and Their Resolution

Cost Management Considerations

  • Cost Management Best Practices and Guidance
  • Spend Visibility
  • Setting Limits

Dates & Locations

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Exam & Certification

No associated certification.

Training & Certification Guide

Why train with Trainocate

Learners will gain skills to deploy and manage containerized AI, ML, and application workloads directly within Snowflake.

The course focuses on Snowpark Container Services (SPCS), including compute pools, GPU workloads, container orchestration, Kubernetes concepts, and AI-ready application deployment within the Snowflake AI Data Cloud.

Key learning areas:

  • Snowpark Container Services architecture
  • Compute pools and scaling
  • Containerized workload deployment
  • GPU-enabled AI/ML workloads
  • Kubernetes concepts in Snowflake
  • Service orchestration and monitoring
  • AI-ready application environments

Pro Tip: Focus on understanding workload architecture and orchestration concepts rather than only container deployment syntax.

This course is designed for advanced technical professionals building AI, ML, and containerized applications on Snowflake.

The course targets professionals responsible for modern cloud-native analytics, AI infrastructure, and scalable application deployment environments.

Best suited for:

  • Data Engineers
  • AI Engineers
  • ML Engineers
  • Cloud Architects
  • DevOps Engineers
  • Platform Engineers

Recommended prerequisites include:

  • Snowflake Fundamentals knowledge
  • SQL knowledge
  • Familiarity with container concepts
  • Basic cloud architecture understanding

Pro Tip: Familiarity with Kubernetes and container orchestration concepts will significantly improve your learning experience.

You will learn how to deploy, scale, secure, and operationalize containerized AI and analytics workloads inside Snowflake.

The course emphasizes practical cloud-native application engineering and AI infrastructure skills using Snowflake-native services.

Skills gained:

  • Deploying containerized workloads
  • Managing Snowflake compute pools
  • Running GPU-enabled AI/ML workloads
  • Managing containerized services
  • Implementing scalable orchestration strategies
  • Securing Snowpark execution environments

Snowpark enables data engineering and AI/ML workloads to run directly within Snowflake using secure execution environments.

Pro Tip: Operational reliability and workload scalability are just as important as deployment itself in enterprise AI environments.

Snowpark Container Services is a Snowflake capability that allows organizations to run containerized applications and AI workloads directly within Snowflake.

SPCS enables developers and engineers to deploy custom applications, AI services, APIs, and machine learning workloads using containerized infrastructure managed inside the Snowflake ecosystem.

SPCS capabilities include:

  • Containerized application hosting
  • AI and ML workload execution
  • GPU-based compute support
  • Integrated Snowflake governance
  • Secure workload isolation

Snowpark was introduced to support data engineering and AI/ML workloads using Python and other programming languages directly within Snowflake.

Pro Tip: Containerized AI architectures are increasingly important because enterprises want to operationalize AI securely near governed enterprise data.

SNOW-SCS supports advanced AI infrastructure, platform engineering, and cloud-native analytics architecture roles.

Organizations increasingly require professionals capable of operationalizing scalable AI and analytics workloads within governed cloud data platforms.

Relevant roles include:

  • AI Platform Engineer
  • ML Infrastructure Engineer
  • Cloud Data Architect
  • Platform Engineer
  • DevOps Engineer
  • Snowflake Architect

Snowflake AI Data Cloud expertise continues growing in demand as enterprises modernize AI and analytics infrastructure environments.

Pro Tip: Combining Snowflake expertise with Kubernetes, AI engineering, or cloud-native architecture skills can significantly strengthen your long-term career opportunities.

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