Master Google Cloud ML exam with practice questions on MLOps, model scaling, monitoring & low-code AI solutions
π₯ 20 students
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- Course Overview
- This comprehensive program is meticulously designed to equip aspiring and practicing Machine Learning Engineers with the deep knowledge and practical skills required to excel in the Google Cloud Professional Machine Learning Engineer certification exam.
- We go beyond surface-level understanding, delving into the intricate architecture and services that power cutting-edge AI and ML solutions on Google Cloud Platform (GCP).
- The curriculum emphasizes a hands-on, project-driven approach, ensuring participants can not only comprehend theoretical concepts but also apply them effectively in real-world scenarios.
- You will gain proficiency in building, deploying, and managing robust ML pipelines, from data ingestion and preprocessing to model training, evaluation, and production deployment.
- Special attention is given to the operational aspects of machine learning (MLOps), covering continuous integration, continuous delivery (CI/CD) for ML, and strategies for scalable and reliable model serving.
- The course will guide you through the nuances of optimizing ML models for performance, cost-effectiveness, and efficient resource utilization within the GCP ecosystem.
- Understanding and implementing effective model monitoring strategies to detect drift, performance degradation, and other critical issues in production is a core focus.
- We will explore Google Cloud’s suite of AI and ML services, including Vertex AI, BigQuery ML, TensorFlow Enterprise, and specialized AI APIs, highlighting their integration and best practices.
- The program is tailored for success, providing ample opportunities to test your knowledge through realistic practice questions and scenario-based challenges, mirroring the exam format.
- Join a cohort of like-minded professionals, fostering a collaborative learning environment with a maximum of 20 students for personalized attention and engaging discussions.
- Requirements / Prerequisites
- A foundational understanding of machine learning concepts, including common algorithms, model evaluation metrics, and the ML lifecycle.
- Familiarity with at least one programming language commonly used in ML, such as Python.
- Basic command-line interface (CLI) skills and experience with Git for version control.
- Prior exposure to cloud computing concepts is beneficial but not strictly mandatory.
- A general understanding of data structures and algorithms will aid in grasping complex ML concepts.
- Participants should possess a working knowledge of data preprocessing techniques and feature engineering.
- Experience with SQL or similar query languages for data manipulation is recommended, especially for BigQuery ML integration.
- An inquisitive mind and a strong desire to master Google Cloud’s ML capabilities.
- Skills Covered / Tools Used
- Google Cloud Platform (GCP) Services: Extensive hands-on experience with Vertex AI (including pipelines, training jobs, model registry, endpoints), BigQuery ML for in-database ML, Cloud Storage for data management, Compute Engine for custom training, and Kubernetes Engine (GKE) for scalable deployments.
- MLOps Principles and Practices: Implementing CI/CD pipelines for ML using tools like Cloud Build, Kubeflow, and Vertex AI Pipelines.
- Model Development and Training: Proficiently training models using popular frameworks like TensorFlow, PyTorch, and scikit-learn within the GCP environment.
- Model Deployment and Serving: Deploying models to production endpoints using Vertex AI Endpoints, optimizing for low latency and high throughput.
- Model Monitoring and Management: Implementing strategies for detecting data drift, concept drift, and performance degradation using Vertex AI Model Monitoring.
- Data Engineering for ML: Leveraging BigQuery and Cloud Storage for efficient data ingestion, transformation, and feature stores.
- Scalability and Performance Optimization: Strategies for scaling ML training and inference workloads, and optimizing resource utilization for cost efficiency.
- Low-Code/No-Code AI Solutions: Understanding and utilizing services like Vertex AI AutoML and specialized AI APIs for rapid AI solution development.
- Security and Governance in ML: Implementing best practices for securing ML data and models on GCP.
- Containerization: Experience with Docker for creating reproducible ML environments.
- Benefits / Outcomes
- Achieve demonstrable expertise and readiness for the Google Cloud Professional Machine Learning Engineer certification exam.
- Gain the confidence and practical skills to design, build, and deploy production-ready ML solutions on Google Cloud.
- Become proficient in applying MLOps best practices to streamline the ML lifecycle, ensuring reliability and scalability.
- Develop the ability to effectively monitor and manage ML models in production, proactively addressing issues and maintaining performance.
- Understand how to leverage Google Cloud’s advanced AI and ML services to solve complex business problems.
- Enhance your career prospects and marketability in the rapidly growing field of cloud-based machine learning.
- Acquire the skills to optimize ML workflows for cost efficiency and performance gains.
- Be equipped to contribute meaningfully to AI initiatives within organizations utilizing Google Cloud.
- Build a portfolio of practical projects that showcase your ML engineering capabilities on GCP.
- Join an exclusive group of certified professionals recognized for their proficiency in Google Cloud ML.
- PROS
- Exam-focused curriculum: Directly addresses the content and format of the Google Cloud Professional Machine Learning Engineer certification exam.
- Hands-on Labs: Practical exercises reinforce learning and build real-world application skills.
- Small Class Size (20 students): Ensures personalized attention and greater interaction with instructors and peers.
- Comprehensive MLOps Coverage: Emphasizes the critical operational aspects of ML, which are highly sought after.
- Exposure to diverse GCP ML Services: Covers a wide range of Google Cloud’s AI and ML offerings.
- CONS
- Potentially demanding pace: Given the breadth of the exam, the course might require a significant time commitment and intensive study outside of scheduled sessions to fully grasp all concepts.
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