Build, Train and Deploy ML Models with Keras on Google Cloud
From Code to Cloud: Build AI That Scales.
- Scalable ML Workflows
- Reducing operational overhead
- Train and deploy models at scale using Vertex AI's managed infrastructure
- Efficient data preprocessing
- Production-Ready Skills
- 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
The "Build, Train and Deploy ML Models with Keras on Google Cloud" course on Google Cloud Skills Boost is designed for data scientists and ML engineers seeking to master end-to-end machine learning workflows using industry-standard tools. Participants learn to construct robust TensorFlow input pipelines with the tf.data library, design neural networks via Keras APIs (Sequential and Functional), and deploy scalable models using Vertex AI. The curriculum emphasizes practical skills for improving model accuracy, handling large datasets, and productionalizing ML solutions. Ideal for intermediate learners, the course combines hands-on labs with theoretical insights, preparing users to tackle real-world ML challenges in cloud environments.
Google's Build, Train and Deploy ML Models with Keras on Google Cloud course Outline
Learn the Build, Train and Deploy ML Models with Keras on Google Cloud
For machine learning practitioners aiming to bridge the gap between model development and production, Google Cloud's "Build, Train and Deploy ML Models with Keras" course is a game-changer. Dive into tools like TensorFlow and Vertex AI to streamline workflows—from designing neural networks with Keras to deploying models at scale. Whether you're refining data pipelines or tackling real-world deployment challenges, this course offers hands-on labs and industry-aligned skills. Ready to transform prototypes into production-grade AI? Explore the course here and earn a credential that showcases your mastery of cloud-native ML engineering.
Start your journey today:
Course Structure Includes:
The curriculum includes five core modules with labs and quizzes:
- TensorFlow Ecosystem: Overview of TensorFlow components, API hierarchy, and tensors.
- Data Pipeline Design: Build scalable input pipelines using tf.data, handle embeddings, and preprocess data with Keras layers.
- Neural Network Development: Utilize Keras Sequential/Functional APIs, activation functions, and regularization techniques.
- Large-Scale Training with Vertex AI: Optimize distributed training workflows and hyperparameter tuning.
- Model Deployment: Productionalize models on Vertex AI for real-time inference and monitoring.
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 design, train, and deploy scalable ML models using TensorFlow, Keras, and Vertex AI. Master data pipelines, neural networks, and cloud-native deployment.
Pre-requisites
No Experience needed.
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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.
Advantages of Using SPOTO's Exam Preparation Materials
SPOTO's study resources provide comprehensive coverage of the exam syllabus, aligning with key topics like ML problem framing, data processing, model optimization, and pipeline automation. Their materials likely include structured learning paths, practice exams, and real-world case studies that mirror the certification's focus on Google Cloud tools and ML workflows. By leveraging SPOTO's targeted content, candidates can efficiently bridge knowledge gaps, reinforce practical skills, and gain familiarity with the exam format. This focused preparation increases confidence and readiness, particularly for complex tasks such as deploying CI/CD pipelines or optimizing model performance—areas emphasized in the certification. Combined with hands-on experience, SPOTO's resources offer a strategic advantage for achieving certification success.
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