Explore the powerful integration of machine learning into data pipelines with the 'Smart Analytics, Machine Learning, and AI on GCP en Español' course offered by Google Cloud. This course equips you with the knowledge and hands-on experience to leverage machine learning capabilities within Google Cloud data pipelines, catering to diverse customization needs.
Throughout the course, you will delve into various methods of incorporating machine learning into data pipelines on Google Cloud, ranging from minimal to extensive customization. From addressing AutoML for limited customization to delving into Notebooks and BigQuery Machine Learning for advanced machine learning capabilities, the course offers comprehensive insights and practical guidance.
Moreover, you will learn to deploy machine learning solutions in production with Kubeflow, gaining valuable proficiency in creating machine learning models on Google Cloud through practical exercises in Qwiklabs.
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This course comprises eight modules covering a range of topics, including an introduction to analytics and AI, utilization of pre-built ML models for unstructured data, analysis of big data with Notebooks, productionizing ML pipelines with Kubeflow, creating custom models with SQL in BigQuery ML, and developing models with AutoML.
Module 1 provides a foundational introduction to the course, setting the stage for the comprehensive learning journey ahead.
Module 2 covers the basics of analytics and AI, offering insights into the transition from ad-hoc data analysis to data-driven decision-making, as well as the various options for AI models on Google Cloud.
Module 3 focuses on utilizing pre-built AI models for unstructured data, emphasizing their complexity and the practical usage of the Natural Language API through informative labs and exercises.
Module 4 delves into the analysis of big data using Notebooks, providing insights into the utilization of Notebooks for data analysis and the practical application of BigQuery in JupyterLab through hands-on labs.
Module 5 explores the productionization of AI pipelines with Kubeflow, presenting different application methods and the practical execution of AI pipelines in Kubeflow, enhancing your proficiency in deploying models.
Module 6 guides you through creating custom models using SQL in BigQuery ML, offering a quick overview of BigQuery ML and its supported models, culminating in the practical application of regression models and movie recommendations.
Module 7 focuses on creating custom models with AutoML, emphasizing its significance and applicability in vision, natural language processing, and table-based models, providing insights into its usage and practical considerations.
Module 8 offers a comprehensive summary of the course, consolidating the key learnings and insights gained throughout the learning journey.
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