The ​"Production Machine Learning Systems" course on Google Cloud Skills Boost equips learners with advanced skills to design, deploy, and maintain robust ML systems in real-world environments. Tailored for ML engineers and cloud architects, this course explores diverse production ML architectures—static, dynamic, and continuous training workflows; batch vs. online inference; and hybrid systems integrating on-premises and cloud resources. Participants will gain hands-on experience with ​TensorFlow, distributed training strategies, and performance optimization techniques while addressing critical challenges like model dependencies, fault tolerance, and cost-efficient scaling. Through labs and case studies, learners master tools to build adaptable, high-performance ML pipelines aligned with industry best practices.

The ​"Machine Learning Operations (MLOps): Getting Started" course on Google Cloud Skills Boost provides a foundational understanding of MLOps principles and tools essential for deploying, monitoring, and maintaining production-grade ML systems. Designed for data scientists and ML engineers, this course focuses on Google Cloud's ​Vertex AI platform to streamline workflows, automate CI/CD pipelines, and ensure reproducibility in model training and inference. Participants will learn to configure reliable MLOps environments, adopt DevOps-inspired practices for ML systems, and integrate continuous evaluation to optimize model performance in real-world scenarios. Ideal for professionals transitioning from prototyping to production, this course bridges the gap between theoretical ML and scalable, operationalized AI solutions.

"Machine Learning Operations (MLOps) with Vertex AI: Manage Features" is a hands-on course designed for data engineers and ML practitioners looking to master MLOps tools and best practices on Google Cloud. It focuses on deploying, evaluating, monitoring, and automating production ML systems using ​Vertex AI Feature Store. Learners will gain practical experience in streaming data ingestion, feature sharing, and ensuring reproducibility in ML workflows. The course combines video lectures and labs to teach scalable solutions for overcoming data challenges in real-world ML pipelines.

"Build and Deploy Machine Learning Solutions on Vertex AI" is an intermediate-level course designed for data scientists and ML engineers aiming to master end-to-end ML workflows on Google Cloud. Through ​four hands-on labs, learners will explore Vertex AI's AutoML, custom training, and pipeline tools to train, tune, explain, and deploy models. The course covers real-world use cases, such as image classification for damaged car parts and predicting customer churn using BigQuery ML, while emphasizing containerization, pipeline automation, and model deployment best practices. Completing the course earns an official skill badge, validating your expertise in production-grade ML solutions.

​​"Create Generative AI Apps on Google Cloud"​​ is an immersive course designed for developers and AI enthusiasts aiming to harness the power of generative AI and large language models (LLMs) to build innovative applications. Through hands-on labs and guided modules, learners explore ​​prompt design​​, ​​retrieval-augmented generation (RAG)​​, and Google Cloud’s Vertex AI Studio to prototype and deploy AI-driven solutions. The course covers real-world use cases, including building a RAG-based chat application, grounding LLMs with external knowledge, and optimizing model responses. By the end, participants will master production-ready architectures for generative AI and earn a skill badge validating their expertise in cutting-edge AI development.

1. 華為權威認證：融合HCIA+HCIP雙認證體系，華為生態企業直接認可。
2. 零基礎友善：無需程式設計經驗，從AI應用到部署全流程實戰，系統建構AI知識體系。
3. 薪資飛躍：AI網工薪資比傳統職缺高，搭配AI產品經理、解決方案工程師等高階職位。
4. 實戰為王：基於昇騰910B晶片、MindFormers框架，22小時免費實驗環境，涵蓋大模型部署、RAG工程、提示字優化等核心技能。
5. 講師天團：20年華為AI架構師領銜，出版技術教材，指導昇騰AI無人船等國家級計畫。
限時福利：報名即贈Python基礎課程+華為官方教材!立即搶佔席位