Machine Learning Operations (MLOps) with Vertex AI: Manage Features
From Data to Deployment: Manage Features, Scale Impact.
- Deploy ML systems efficiently
- Streamline feature management
- Scalable, reproducible experiments
- Improve model performance
- Enhance collaboration through shared feature repositories
- Align with industry
- Build a base for specialized roles in AI development or cloud engineering
- Prepare for more advanced Google Cloud certifications in AI
What you will learn
"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.
Google's Machine Learning Operations (MLOps) with Vertex AI: Manage Features course Outline
Learn the Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Ready to level up your MLOps expertise? Dive into Machine Learning Operations (MLOps) with Vertex AI: Manage Features on Google Cloud Skills Boost. This course offers hands-on labs and expert guidance to help you master feature management, streamline ML deployments, and solve real-world data challenges. Whether you're a data engineer or an ML practitioner, you'll learn how Vertex AI Feature Store can transform your workflows. Click the link to start building scalable, production-ready ML systems today!
Start your journey today:
Course Structure Includes:
Introduction to MLOps & Vertex AI (1 Video): Overview of course goals and MLOps principles.
Vertex AI Feature Store Deep Dive (3 Videos): Addressing data challenges, feature sharing, and scalability.
Hands-On Lab: Practice streaming ingestion and feature management using Vertex AI SDK.
Advanced Capabilities (4 Videos): Feature Store integration, monitoring, and automation strategies.
Course Summary (1 Video): Key takeaways and next steps.
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
Learn to deploy, monitor, and scale ML systems using Google Cloud's Vertex AI Feature Store. Streamline feature sharing, ensure reproducibility, and tackle production challenges with hands-on labs.
Pre-requisites
Proficiency with Python on topics covered in the Crash Course on Python. Prior experience with foundational machine learning concepts and building machine learning solutions on Google Cloud as covered in the Machine Learning on Google Cloud course.
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SPOTO Empowers You to Earn Your Certification.
Benefits of Google Professional Machine Learning Engineer Certification
The Google Professional Machine Learning Engineer certification validates expertise in designing, building, and deploying machine learning models using Google Cloud technologies. It demonstrates proficiency in critical areas such as framing ML problems, architecting scalable solutions, data preparation, model development, and productionization. This certification is highly regarded in the industry, enhancing career prospects by signaling advanced technical skills and practical experience in solving real-world business challenges. Certified professionals often gain a competitive edge in roles like machine learning engineer, data scientist, or AI developer, with opportunities at leading global companies. Additionally, Google recommends at least three years of ML experience for the exam, ensuring that certified individuals possess both theoretical knowledge and hands-on capabilities.
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