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Self Service ML Pipelines Using Dataprep and AutoML Tables
Google Cloud Training
Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will learn how to use Dataprep in conjunction with AutoML Tables to build and operate your machine learning pipelines.
Register for this course and see more details by visiting:
OpenCourser.com/course/gmbb0w/self
What's inside
Syllabus
Self Service ML Pipelines Using Dataprep and AutoML Tables
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the basics of using Dataprep and AutoML Tables to kickstart machine learning pipelines
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Self Service ML Pipelines Using Dataprep and AutoML Tables.
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Career center
Learners who complete Self Service ML Pipelines Using Dataprep and AutoML Tables will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models and systems. This role requires expertise in building and maintaining ML pipelines. Familiarity with self-serve ML pipelines is essential to enabling a Machine Learning Engineer to leverage pre-built components and automation capabilities offered by Dataprep and AutoML Tables. This course can equip you with the knowledge and hands-on experience in self-serve ML pipelines, providing a competitive edge in this field.
Data Scientist
Data Scientist
Data Scientists utilize their expertise in statistics, machine learning, and data analysis to solve complex business problems. Knowledge of self-serve ML pipelines is becoming increasingly important in this role, as it allows Data Scientists to automate and streamline their ML workflows, enabling them to focus on higher-level tasks and experimentation. This course can provide you with the necessary understanding and practical skills in self-serve ML pipelines to excel as a Data Scientist.
Data Analyst
Data Analyst
The role of Data Analyst involves collecting, cleaning, and analyzing data to extract insights. Proficiency in self-serve ML pipelines can empower a Data Analyst to leverage Dataprep's capabilities for data preparation and AutoML tables' capabilities for automated feature engineering and model training to streamline analytical processes and derive valuable insights efficiently. This course provides a strong foundation in these aspects, making it beneficial for aspiring and practicing Data Analysts.
ML Ops Engineer
ML Ops Engineer
ML Ops Engineers are responsible for deploying, monitoring, and maintaining machine learning models in production. Knowledge of self-serve ML pipelines can be beneficial for ML Ops Engineers, as it enables them to understand the end-to-end process of ML pipeline development and deployment, ensuring smooth and efficient operation. This course can provide you with a foundation in self-serve ML pipelines, equipping you with skills that are in high demand in the field of ML operations.
Data Engineer
Data Engineer
Data Engineers use programming and analytical skills to build pipelines that transform, manage, and secure data. A foundational understanding of self-serve ML pipelines can be critical for those in this role, as it enables them to efficiently develop pipelines that automate the machine learning lifecycle. This course can help you develop the foundational knowledge and skills in self-serve ML pipelines leveraging Datprep and AutoML Tables that are essential for a Data Engineer.
Fraud Analyst
Fraud Analyst
Fraud Analysts investigate and prevent fraudulent activities. Proficiency in self-serve ML pipelines can be beneficial for Fraud Analysts, as it enables them to automate data analysis tasks and leverage machine learning algorithms to detect fraudulent patterns and identify suspicious transactions more effectively. This course can provide you with a foundation in self-serve ML pipelines, making you a more skilled and efficient Fraud Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Familiarity with self-serve ML pipelines can be beneficial for Quantitative Analysts, as it enables them to leverage the power of machine learning to enhance their models, improve risk management, and make more informed investment decisions. This course can provide you with a foundation in self-serve ML pipelines, equipping you with skills that are increasingly sought after in the field of quantitative finance.
Data Architect
Data Architect
Data Architects design and manage data systems and infrastructure. An understanding of self-serve ML pipelines can be beneficial for Data Architects, as it enables them to understand the technical requirements and considerations for integrating ML pipelines into their architectures, ensuring data integrity and performance. This course can provide you with the knowledge and skills in self-serve ML pipelines that are essential for the role of a Data Architect.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risks within financial institutions. An understanding of self-serve ML pipelines can be valuable for Risk Analysts, as it enables them to leverage machine learning techniques to identify and quantify risks more accurately and efficiently. This course can provide you with the necessary knowledge and skills in self-serve ML pipelines relevant to the field of risk management, empowering you to make more informed decisions.
Product Manager
Product Manager
Product Managers are responsible for defining and managing product roadmaps, working closely with technical teams to deliver successful products. An understanding of self-serve ML pipelines can be valuable for Product Managers, as it enables them to understand the technical capabilities and limitations of ML and make informed decisions about incorporating ML into their products. This course can provide you with the essential knowledge in self-serve ML pipelines, empowering you to be a more effective Product Manager.
Business Analyst
Business Analyst
Business Analysts bridge the gap between technical and business teams by analyzing data and providing insights to support decision-making. An understanding of self-serve ML pipelines can be beneficial for Business Analysts, as it empowers them to leverage data-driven insights and communicate complex technical concepts effectively. This course can help you develop a foundation in self-serve ML pipelines, making you a more well-rounded Business Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use financial data and models to evaluate and make investment decisions. An understanding of self-serve ML pipelines can be beneficial for Financial Analysts, as it enables them to automate data preparation tasks and leverage machine learning algorithms to identify patterns and trends in financial data, supporting more accurate and data-driven decision-making. This course can provide you with the necessary knowledge and skills in self-serve ML pipelines that are relevant to the role of a Financial Analyst.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze marketing campaigns and customer data to identify trends and patterns, providing insights to support marketing strategies. Knowledge of self-serve ML pipelines can be beneficial for Marketing Analysts, as it enables them to automate data analysis tasks and leverage predictive models to optimize campaigns and target audiences more effectively. This course can provide you with a foundation in self-serve ML pipelines, making you a more data-driven Marketing Analyst.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases, ensuring data integrity and performance. Knowledge of self-serve ML pipelines can be advantageous for Database Administrators, as it enables them to automate data preparation tasks and leverage machine learning capabilities to optimize database performance and resource allocation. This course can provide you with the necessary understanding and skills in self-serve ML pipelines that are relevant to the role of a Database Administrator.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Familiarity with self-serve ML pipelines can be beneficial for Software Engineers who work on data-driven applications, as it enables them to integrate machine learning capabilities into their software solutions effectively. This course can provide you with foundational knowledge and practical experience in self-serve ML pipelines, enhancing your skillset as a Software Engineer.
For more career information including salaries, visit:
OpenCourser.com/course/gmbb0w/self
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Self Service ML Pipelines Using Dataprep and AutoML Tables.
Provides a hands-on guide to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers topics such as data preprocessing, model training, and model deployment.
Provides a practical introduction to machine learning using Python. It covers topics such as data preprocessing, model training, and model evaluation.
Provides a comprehensive overview of feature engineering techniques for machine learning. It covers topics such as feature selection, feature extraction, and feature transformation.
Provides a comprehensive overview of deep learning concepts and techniques. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive overview of natural language processing with Python. It covers topics such as text preprocessing, text classification, and text generation.
Save
Provides a comprehensive overview of generative adversarial networks. It covers topics such as GAN architecture, GAN training, and GAN applications.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers topics such as image formation, image processing, and object recognition.
Provides a detailed overview of model selection and evaluation techniques for machine learning. It covers topics such as model selection criteria, overfitting and underfitting, and model evaluation metrics.
Provides a comprehensive overview of speech and language processing. It covers topics such as speech recognition, natural language understanding, and natural language generation.
Provides a comprehensive overview of reinforcement learning concepts and techniques. It covers topics such as Markov decision processes, value functions, and policy gradients.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gmbb0w/self
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Teaches the basics of using Dataprep and AutoML Tables to kickstart machine learning pipelines
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