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Building a Reproducible Model Workflow
Giacomo Vianello,
Ulrika Jägare,
Justin Clifford Smith, PhD,
Bradford Tuckfield,
and
Joshua Bernhard
Giacomo Vianello,
Ulrika Jägare,
Justin Clifford Smith, PhD,
Bradford Tuckfield,
and
Joshua Bernhard
Take Udacity's Building a Reproducible Model Workflow course to get an introduction on machine learning operations and learn how to create a clean, end-to-end machine learning pipeline MLflow.
Prerequisite details
Read more
Take Udacity's Building a Reproducible Model Workflow course to get an introduction on machine learning operations and learn how to create a clean, end-to-end machine learning pipeline MLflow.
Prerequisite details
Read more
Take Udacity's Building a Reproducible Model Workflow course to get an introduction on machine learning operations and learn how to create a clean, end-to-end machine learning pipeline MLflow.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Jupyter notebooks
- Intermediate Python
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/h41ese/building
What's inside
Syllabus
Dive into reproducible model workflows and machine learning operations, learning about use cases, its history, and what you'll build at the end of the course.
Read more
Syllabus
Dive into reproducible model workflows and machine learning operations, learning about use cases, its history, and what you'll build at the end of the course.
Read more
Build out machine learning pipelines, as well as learning how to version data and model artifacts.
Come up with re-usable processes for performing exploratory data analysis (EDA), cleaning and pre-processing data, and segregating/splitting data.
Validate data through deterministic and non-deterministic testing, and look at handling different parameters with PyTest.
Write an inference pipeline, validate and choose your best performing models from experiments, and test your final model artifacts.
Write a full end-to-end pipeline, release the pipeline, and deploy with MLflow.
Create a re-usable end-to-end pipeline for predicting short-term rental prices in New York City!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers machine learning operations and tools, such as MLflow, which are standard in industry
Taught by Giacomo Vianello, Ulrika Jägare, Justin Clifford Smith, PhD, Bradford Tuckfield, and Joshua Bernhard, who are recognized for their work in machine learning and related fields
Teaches learners how to create reproducible model workflows and deploy them with MLflow
Develops skills in testing and validating data and models, including deterministic and non-deterministic testing with PyTest
Builds a strong foundation in machine learning operations and best practices
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Activities
Coming soon
We're preparing activities for
Building a Reproducible Model Workflow.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Building a Reproducible Model Workflow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will design, build, and maintain machine learning models. You can automate model development with a range of tools like MLflow, helping you ensure that your models are reproducible and deployed effectively. Taking Udacity's Building a Reproducible Model Workflow can be a stepping stone in your journey towards becoming a Machine Learning Engineer as it will help you build a solid foundation in designing and deploying ML pipelines using MLflow.
Data Scientist
Data Scientist
As a Data Scientist, you need to be able to build end-to-end ML pipelines for model development and deployment. Udacity's Building a Reproducible Model Workflow course teaches you how to build ML pipelines using MLflow, a tool that streamlines the ML lifecycle, which can be a valuable skill for success in this field.
Software Engineer
Software Engineer
If you are a Software Engineer interested in working with machine learning, taking Udacity's Building a Reproducible Model Workflow can be beneficial. This course teaches you how to design and deploy ML pipelines using MLflow, enabling you to build software that leverages ML capabilities effectively.
Data Analyst
Data Analyst
As a Data Analyst, you are responsible for analyzing data and building models to extract insights. Udacity's Building a Reproducible Model Workflow course teaches you how to build ML pipelines using MLflow, a tool that can help you automate and streamline the ML lifecycle, making you a more efficient and effective Data Analyst.
Machine Learning Architect
Machine Learning Architect
As a Machine Learning Architect, you are responsible for designing and implementing ML systems. Udacity's Building a Reproducible Model Workflow can aid you in becoming a Machine Learning Architect by providing you with knowledge of designing and deploying ML pipelines using MLflow, an essential tool for building scalable and robust ML systems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions. By taking Udacity's Building a Reproducible Model Workflow, you can enhance your skills in building ML pipelines, which can be valuable in developing predictive models for financial analysis.
Business Analyst
Business Analyst
Business Analysts help businesses make data-driven decisions. By taking Udacity's Building a Reproducible Model Workflow, you can learn how to build ML pipelines, a skill that can empower you to make more informed and accurate recommendations based on data analysis.
Data Engineer
Data Engineer
Data Engineers are responsible for managing and processing data for analysis. Taking Udacity's Building a Reproducible Model Workflow can help you learn how to build ML pipelines using MLflow, a tool that can streamline the ML lifecycle, making you a more efficient and effective Data Engineer.
Product Manager
Product Manager
Product Managers are responsible for defining and managing products. By taking Udacity's Building a Reproducible Model Workflow, you can learn how to incorporate ML into product development and use it to drive product decisions, potentially enhancing your effectiveness as a Product Manager.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems. Udacity's Building a Reproducible Model Workflow teaches you how to build ML pipelines, providing you with additional tools to analyze data and develop solutions for operational challenges.
Financial Analyst
Financial Analyst
Financial Analysts use data to make recommendations on investments. Taking Udacity's Building a Reproducible Model Workflow can enhance your understanding of building ML pipelines, which can be useful for analyzing financial data and making informed investment decisions.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risks in various fields. By taking Udacity's Building a Reproducible Model Workflow, you can gain knowledge in building ML pipelines, a valuable skill for developing risk assessment models and making data-driven risk management decisions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. Udacity's Building a Reproducible Model Workflow can provide you with additional skills in building ML pipelines, which can enhance your ability to analyze data and draw meaningful conclusions.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. By taking Udacity's Building a Reproducible Model Workflow, you can learn to build ML pipelines, a skill that can complement your actuarial knowledge and enhance your ability to model and manage risks.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. Udacity's Building a Reproducible Model Workflow can equip you with additional skills in building ML pipelines, which can be valuable for analyzing economic data and developing economic models.
For more career information including salaries, visit:
OpenCourser.com/course/h41ese/building
Reading list
We've selected six 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
Building a Reproducible Model Workflow.
Provides a comprehensive overview of Python for data analysis. It great resource for anyone who wants to learn more about Python and how to use it for data analysis.
Provides a comprehensive overview of data science from first principles using Python. It great resource for anyone who wants to learn more about data science and how to apply it in practice.
Provides a comprehensive overview of deep learning for coders using Fastai and PyTorch. It great resource for anyone who wants to learn more about deep learning and how to apply it in practice.
Provides a comprehensive overview of reinforcement learning. It great resource for anyone who wants to learn more about RL and how to apply it in practice.
Provides a comprehensive overview of Bayesian data analysis. It great resource for anyone who wants to learn more about Bayesian data analysis and how to apply it in practice.
Save
Provides a comprehensive overview of causal inference. It great resource for anyone who wants to learn more about causal inference and how to apply it in practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/h41ese/building
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Effort
4 weeks
Level
Advanced
Via
Udacity
Instructors
Giacomo Vianello
Ulrika Jägare
Justin Clifford Smith, PhD
Bradford Tuckfield
Joshua Bernhard
Language
English
Good to know
Covers machine learning operations and tools, such as MLflow, which are standard in industry
Taught by Giacomo Vianello, Ulrika Jägare, Justin Clifford Smith, PhD, Bradford Tuckfield, and Joshua Bernhard, who are recognized for their work in machine learning and related fields
Teaches learners how to create reproducible model workflows and deploy them with MLflow
Develops skills in testing and validating data and models, including deterministic and non-deterministic testing with PyTest
Builds a strong foundation in machine learning operations and best practices
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