Cloudera Educational Services

About This Training Generative AI (GenAI) and Large Language Models (LLMs) are extremely powerful new tools that are changing every industry. To fully take advantage of GenAI and LLMs, these new capabilities need to be combined with your existing enterprise data. This two-day course teaches how to use Cloudera AI to train, augment, fine tune, and host LLMs to create powerful enterprise AI solutions. What Skills You Will Gain Through lecture and Hands-On exercises, you will learn how to: Select the right LLM model for a use case Configure a Prompt for an LLM Use Retrieval Augmented Generation (RAG) Fine Tune an LLM Model with Enterprise Data Use the AI Model Registry and host an LLM Create an AI Agent with Crew AI Who Should Take This Course This course is designed for data scientists and machine learning engineers who need to understand how to utilize Cloudera AI to leverage the full power of their enterprise data, generative AI, and Large Language Models and deliver powerful business solutions.     DATE: August 31 - September 1, 2026 9:00 - 17:00 (CEST TIMEZONE) Virtual Classroom, EMEA Read more

This four-day course teaches the architecture, deployment, and configuration of Cloudera Data Services on Embedded Containerized Services (ECS). Cloudera Data Services provide a state-of-the-art, low- code platform that unifies the entire data lifecycle, reducing development costs and accelerating the development and deployment of use cases.   The course starts by covering best practices for managing Docker images and containers. Students will then build a Docker private registry. This Docker private registry will be used to deploy a Data Services cluster on ECS. Students will install, configure, and validate Cloudera Data Engineering, Cloudera Data Warehouse, and Cloudera Machine Learning. Through hands-on exercises, students will gain experience with Kubernetes, install a Private Cloud Embedded Container Service (ECS), and deploy Cloudera Data Services. Additionally, the course covers networking and hardware requirements and explains how Kubernetes pods dynamically scale to support Cloudera Data Services. Who should take this course This immersive course is designed for Cloudera Administrators transitioning to managing Cloudera Data Services on premises. Students should have at least 3 to 5 years of system administration experience. Students must have proficiency in the Linux Command Line Interface and knowledge of Identity Management, including Transport Layer Security and Kerberos. Familiarity with SQL select statements is recommended. Prior experience with Cloudera products is required. Students need reliable internet access to connect to the Amazon Web Services environment used in this course. Recommended prerequisite courses • ADMIN-230: Administering Cloudera on premises • ADMIN-332: Securing Cloudera on premises     DATE: July 20-23, 2026 9:00 - 17:00 (CEST TIMEZONE) Virtual Classroom, EMEA Read more

This course introduces Apache Iceberg, a high-performance open table format for organizing petabyte-scale analytic datasets on a file system or object store, available on Cloudera Data Warehouse and Cloudera Data Engineering on both Private and Public Cloud. Combined with Cloudera Data Platform, Iceberg can enable users to build an open data lakehouse architecture for multi-function analytics and to deploy large-scale end-to-end pipelines. This course covers various aspects of Apache Iceberg, such as benefits, architecture, internal operation, read and write operations, and advanced functions, all while drawing comparisons to Hive and building on the students’ existing knowledge and experience. DATE: August 17-20, 2026 9:00 - 17:00 (CEST TIMEZONE) Virtual Classroom, EMEA Read more

Enterprise data science teams need collaborative access to business data, tools, and computing resources required to develop and deploy machine learning workflows. Cloudera AI, part of the Cloudera platform, provides the solution, giving data science teams the required resources. This four-day course covers machine learning workflows and operations using Cloudera AI. Participants explore, visualize, and analyze data. You will also train, evaluate, and deploy machine learning models. The course walks through an end-to-end data science and machine learning workflow based on realistic scenarios and datasets from a fictitious technology company. The demonstrations and exercises are conducted in Python (with PySpark) using Cloudera AI. Download full course description     DATE: August 24-27, 2026 9:00 - 17:00 (GMT+2 TIMEZONE) Virtual Classroom, EMEA Read more

Overview This five-day course provides the in-depth explanation and skills to become highly productive with Kubernetes by teaching its architecture, deployment, configuration, logging, reporting, and more. Kubernetes's complexity is due to its extensive feature set and intricate object model, which includes concepts like Pods, Deployments, and Services. It also relies on YAML configuration files and requires managing a distributed system across multiple nodes. The course provides references for architecture and recommended practices used by administrators. Download full course description   What you'll learn Understand the architecture of Kubernetes and its components. Deploy and manage applications on Kubernetes. Utilize Kubernetes features for scaling and self-healing. Implement best practices for security and networking. Troubleshoot common issues in Kubernetes environment   Who Should Take This Course? This course is an immersive training for a full range of Kubernetes users, from administrators to DevOps to developers. This course is intended for senior technicians with strong skills in Linux and application lifecycle. Students must have proficiency in Linux CLI. Students must have access to high-speed Internet to download various public Git repositories. The classroom environment is a four-node cluster running on Linux.   DATE: December 14-18, 2026 9:30 - 17:30 (Seoul TIMEZONE) Onsite Classroom, APAC Read more

Overview This five-day course provides the in-depth explanation and skills to become highly productive with Kubernetes by teaching its architecture, deployment, configuration, logging, reporting, and more. Kubernetes's complexity is due to its extensive feature set and intricate object model, which includes concepts like Pods, Deployments, and Services. It also relies on YAML configuration files and requires managing a distributed system across multiple nodes. The course provides references for architecture and recommended practices used by administrators. Download full course description   What you'll learn Understand the architecture of Kubernetes and its components. Deploy and manage applications on Kubernetes. Utilize Kubernetes features for scaling and self-healing. Implement best practices for security and networking. Troubleshoot common issues in Kubernetes environment   Who Should Take This Course? This course is an immersive training for a full range of Kubernetes users, from administrators to DevOps to developers. This course is intended for senior technicians with strong skills in Linux and application lifecycle. Students must have proficiency in Linux CLI. Students must have access to high-speed Internet to download various public Git repositories. The classroom environment is a four-node cluster running on Linux.   DATE: October 12-16, 2026 9:30 - 17:30 (Seoul TIMEZONE) Onsite Classroom, APAC Read more