Hands-on Courses for ML Engineers
A comprehensive, hands-on learning path designed for experienced practitioners ready to design, build, and productionise ML systems at scale.
A comprehensive, hands-on learning path designed for experienced practitioners ready to design, build, and productionise ML systems at scale.
Provider: Google
Time commitment: ranging from 1 - 24 hours
A comprehensive, hands-on learning path designed for experienced practitioners ready to design, build, and productionise ML systems at scale. This curated collection of on-demand courses and labs guides you through the complete ML engineering workflow using Google Cloud technologies: from managing large, complex datasets and creating repeatable code to deploying generative AI solutions and implementing MLOps practices.
You'll gain practical experience with Vertex AI, TensorFlow, BigQuery ML, feature engineering, model architecture design, and ML pipeline creation. The path covers critical production considerations including data governance, hyperparameter tuning, batch and online predictions, and model monitoring. Ideal for data engineers and developers with strong programming skills (particularly Python) who want to advance into production ML engineering roles.
Upon completion, you'll be well-prepared to pursue the industry-recognised Google Cloud Professional Machine Learning Engineer certification. Requires intermediate to advanced technical background with 3+ years of hands-on experience recommended for certification.
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