MLOps2 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform from edX

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOps2 (GCP): Data Pipeline Automation & Optimization using Gogle Cloud Platform. In this course you will learn how to set up automated monitoring of your data pipeline for prediction. Data drift, model drift and feedback loops can impair model performance and model stability, and you will learn how to monitor for those phenomena. You will also learn about setting triggers and alarms, so that operators can deal with problems with model instability. You will also cover ethical issues in machine learning and the risks they pose, and learn about the "Responsible Data Science" framework.

What you'll learn

You will learn how to set up automated monitoring of your data pipeline for prediction and get hands on experience with topics like data pipelines, drift and feedback loops, model stability, triggers & alarms, model security, responsible AI and much more.

But most importantly, by the end of this course, you will know…

How to meet the differing requirements of model training versus model inference in your pipeline
How to check for model drift, data drift, and feedback loops
How to apply the principles of Continuous Integration (CI), Continuous Delivery (CDE) and Continuous Deployment (CD)

What's inside

Learning objectives

How to meet the differing requirements of model training versus model inference in your pipeline
How to check for model drift, data drift, and feedback loops

How to apply the principles of continuous integration (ci), continuous delivery (cde) and continuous deployment (cd)

How to meet the differing requirements of model training versus model inference in your pipeline
How to check for model drift, data drift, and feedback loops
How to apply the principles of continuous integration (ci), continuous delivery (cde) and continuous deployment (cd)

Syllabus

Week 1 – Drift and Feedback Loops

Module 1: Training Versus Inference Pipelines

Module 2: Drift & Feedback Loops

Week 2 – Triggers, Alarms & Model Stability

Module 3: Triggers & Alarms

Module 4: Model Stability

Week 3 – CI/CD (Continuous Integration & Continuous Deployment/Delivery)

Module 5: CI/CD

Week 4 – Model Security and Responsible AI

Module 6: Responsible AI

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Relevant to industry professionals interested in deploying machine learning models on the Google Cloud Platform

Taught by recognized thought leaders in the field of applied machine learning on Google Cloud

Develops practical skills for building & managing MLOps pipelines in real world applications

Covers advanced concepts such as data drift, model drift, and feedback loops, which are essential for maintaining model performance

Emphasizes ethical considerations in machine learning, ensuring responsible use of AI algorithms

Aligned with industry best practices and principles of MLOps, including Continuous Integration/Continuous Deployment/Continuous Delivery (CI/CD)

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in MLOps2 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform with these activities:

Review data engineering fundamentals

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Review key concepts of data engineering to prepare for this course

Browse courses on Data Engineering

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Review a data engineering textbook or online resources
Practice data extraction, transformation and loading (ETL) techniques using Python or SQL
Refresh your understanding of NoSQL databases and data warehousing

Join a study group to discuss course topics and work on projects together

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Collaborate with peers to reinforce learning and gain multiple perspectives

Browse courses on Machine Learning

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Find or create a study group with other students in the course
Meet regularly to discuss course material, share ideas, and work on projects
Provide support and feedback to group members

Design and develop a data pipeline for a specific business use case

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Apply the principles of data pipeline design and development to a real-world scenario

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Identify a business use case that requires data processing and analysis
Design the data pipeline architecture, including data sources, data transformation, and data storage
Implement the data pipeline using appropriate tools and technologies
Test and validate the data pipeline to ensure accuracy and performance
Deploy and monitor the data pipeline in a production environment

Four other activities

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Solve coding challenges related to data pipeline automation

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Practice solving technical challenges common in data pipeline automation

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Find online coding challenges or create your own
Attempt to solve the challenges using Python or another relevant programming language
Review your solutions and identify areas for improvement

Follow online tutorials on advanced topics in data pipeline automation

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Expand your knowledge by exploring advanced topics in data pipeline automation

Browse courses on Machine Learning

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Identify online tutorials or courses on topics such as model deployment, performance monitoring, or cloud computing
Follow the tutorials and complete the associated exercises
Apply what you have learned to your own data pipeline projects

Attend workshops on data pipeline automation and optimization

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Learn from experts and network with professionals in the field of data pipeline automation

Browse courses on Data Engineering

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Identify and register for workshops on relevant topics
Attend the workshops and actively participate in discussions and exercises
Connect with other attendees and speakers

Contribute to open-source projects related to data pipeline automation

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Gain practical experience and contribute to the community by working on open-source data pipeline projects

Browse courses on Data Engineering

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Identify open-source projects that align with your interests and skills
Contribute to the project by submitting bug reports, feature requests, or code changes
Collaborate with other contributors and project maintainers

Career center

Learners who complete MLOps2 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer builds, deploys, and maintains machine learning models in production. The course's focus on Continuous Integration and Continuous Deployment (CI/CD) would be particularly useful for this role, as Machine Learning Engineers must be able to automate the deployment and management of machine learning models. Additionally, the course's module on model security would fit well with this role, as Machine Learning Engineers must ensure that models are secure and protected from malicious attacks.

See salaries and explore the career path for Machine Learning Engineer

Data Science Manager

A Data Science Manager leads a team of data scientists and engineers to develop and implement data science solutions. The course's modules on drift and feedback loops, model stability, and Continuous Integration and Continuous Deployment (CI/CD) would be very useful for this role, as Data Science Managers must understand how to manage data science projects from start to finish and ensure that models are reliable, accurate, and deployed in a timely manner.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. The course's focus on Continuous Integration and Continuous Deployment (CI/CD) would be particularly useful for this role, as Software Engineers must be able to automate the deployment and management of software applications. Additionally, the course's module on model security would fit well with this role, as Software Engineers must ensure that software applications are secure and protected from malicious attacks.

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Data Science Consultant

A Data Science Consultant provides expertise and guidance to organizations on how to use data to solve business problems. The course's emphasis on Continuous Integration and Continuous Deployment (CI/CD) would be particularly useful for this role, as Data Science Consultants must be able to help organizations automate the deployment and management of data science solutions. Additionally, the course's module on model security would fit well with this role, as Data Science Consultants must ensure that data science solutions are secure and protected from malicious attacks.

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Data Architect

A Data Architect designs, builds, and manages data platforms and solutions. The course's module on training versus inference pipelines would be particularly useful for this role, as Data Architects must understand how to optimize data for different purposes. Additionally, the course's emphasis on Continuous Integration and Continuous Deployment (CI/CD) would fit well with this role, as Data Architects must be able to automate the deployment and management of data platforms and solutions.

See salaries and explore the career path for Data Architect

Data Engineer

A Data Engineer will typically design, build, deploy, maintain, and manage data systems and infrastructure. This includes working on data warehouses, data lakes, and pipelines to ingest, integrate, and transform data. The course's module on training versus inference pipelines may be particularly useful for this role, as Data Engineers must understand how to optimize data for different purposes. Additionally, the course's emphasis on Continuous Integration and Continuous Deployment (CI/CD) would fit well with a Data Engineer's role.

See salaries and explore the career path for Data Engineer

AI Engineer

An AI Engineer applies artificial intelligence techniques to solve business problems and develop new products and services. The course's focus on Continuous Integration and Continuous Deployment (CI/CD) would be particularly useful for this role, as AI Engineers must be able to automate the deployment and management of AI solutions. Additionally, the course's module on model security would fit well with this role, as AI Engineers must ensure that AI solutions are secure and protected from malicious attacks.

See salaries and explore the career path for AI Engineer

Machine Learning Researcher

A Machine Learning Researcher develops new machine learning algorithms and techniques for solving various problems. The course's module on drift and feedback loops could be very relevant to this role, as Machine Learning Researchers must understand how to monitor data and models for changes over time. The course also includes a module on model stability, which is critical for ensuring that machine learning models are reliable and accurate.

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Data Scientist

A Data Scientist focuses on extracting knowledge and insights from data using advanced analytical techniques and machine learning algorithms. The course's module on drift and feedback loops could be very relevant to this role, as Data Scientists must understand how to monitor data and models for changes over time. The course also includes a module on model stability, which is critical for ensuring that data science models are reliable and accurate.

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Product Manager

A Product Manager manages the development and launch of new products and services. The course's focus on Continuous Integration and Continuous Deployment (CI/CD) would be particularly useful for this role, as Product Managers must be able to work with engineers and other stakeholders to ensure that products are released on time and meet customer needs. Additionally, the course's module on model security would fit well with this role, as Product Managers must ensure that products are secure and protected from malicious attacks.

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Data Analyst

A Data Analyst collects, processes, analyzes, and interprets data to uncover insights and trends. The module on responsible AI would be very useful here, since Data Analysts must ensure that AI is used ethically and responsibly as they are heavily involved in the data analysis and interpretation that goes into creating insights.

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Cloud Architect

A Cloud Architect designs, builds, and manages cloud computing solutions. The course's focus on Continuous Integration and Continuous Deployment (CI/CD) would be particularly useful for this role, as Cloud Architects must be able to automate the deployment and management of cloud computing solutions. Additionally, the course's module on model security would fit well with this role, as Cloud Architects must ensure that cloud computing solutions are secure and protected from malicious attacks.

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Business Intelligence Analyst

A Business Intelligence Analyst uses data to identify trends, patterns, and opportunities that can help organizations make better decisions. The module on responsible AI would be very useful here, since Business Intelligence Analysts must ensure that AI is used ethically and responsibly as they are heavily involved in the data analysis and interpretation that goes into creating insights.

See salaries and explore the career path for Business Intelligence Analyst