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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
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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
Save this course
Save Building a Reproducible Model Workflow to your list so you can find it easily later:
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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 Building a Reproducible Model Workflow with these
activities:
Review Jupyter Notebooks
Show steps
Ensure fluency in Jupyter Notebooks, which are commonly used in the course and MLops in general.
Browse courses on
Jupyter Notebooks
Show steps
-
Review online tutorials or documentation on Jupyter Notebooks.
-
Practice creating and editing notebooks using the Jupyter interface.
Intermediate Python Programming Practice
Show steps
Strengthen your Python programming skills, which are essential for implementing MLops practices.
Browse courses on
Python
Show steps
-
Solve intermediate-level Python programming problems on coding platforms like LeetCode or HackerRank.
-
Participate in online forums or discussion groups to clarify concepts and get feedback on your code.
Guided Tutorials on Machine Learning Operations (MLops)
Show steps
Familiarize yourself with the fundamental concepts of MLops, preparing you for the topics covered in the course.
Browse courses on
MLOps
Show steps
-
Enroll in a beginner-friendly MLops tutorial series or course.
-
Complete the tutorials and exercises to grasp the basics of MLops.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow along with interactive MLFlow tutorials
Show steps
Gain hands-on experience and build a deeper understanding of how to use MLFlow effectively by following interactive tutorials.
Browse courses on
MlFlow
Show steps
-
Find MLFlow tutorials on platforms like Coursera, edX, or Udemy.
-
Set aside dedicated time to follow along with the tutorials, experimenting with different features.
-
Take notes and document your learning to reinforce understanding.
Solve MLFlow practice problems
Show steps
Build confidence and understanding of fundamental MLFlow principles by solving practice problems.
Browse courses on
MlFlow
Show steps
-
Attempt beginner level MLFlow problems from online platforms.
-
Enroll in practice-based MLFlow courses or online programs.
-
Join MLFlow online forums and actively engage in discussions and problem-solving.
Exploratory Data Analysis and Preprocessing Project
Show steps
Gain hands-on experience in performing EDA and data preprocessing, which are crucial steps in MLops.
Browse courses on
Exploratory Data Analysis
Show steps
-
Obtain a publicly available dataset related to your interests.
-
Perform exploratory data analysis using techniques like visualizations and statistical analysis.
-
Apply data preprocessing techniques such as cleaning, handling missing values, and feature engineering.
-
Document your findings and insights in a report or presentation.
Workshop on ML Model Validation and Testing
Show steps
Gain practical experience in validating and testing ML models, a crucial aspect of MLops covered in the course.
Show steps
-
Attend a hands-on workshop on ML model validation and testing.
-
Participate in exercises and discussions to apply concepts and techniques.
Study Group for Machine Learning Pipelines
Show steps
Engage with peers to discuss and clarify concepts related to building ML pipelines, a key topic in the course.
Browse courses on
Machine Learning Pipelines
Show steps
-
Form a study group with classmates or other learners interested in ML pipelines.
-
Meet regularly to discuss course materials, share insights, and work on ML pipeline projects together.
Build a mini MLFlow project
Show steps
Synthesize your understanding of MLFlow by creating a practical project, solidifying your knowledge and enhancing your portfolio.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a small-scale MLFlow project idea that aligns with your interests.
-
Gather and prepare a dataset relevant to your project.
-
Implement your project using MLFlow, ensuring proper versioning and tracking.
-
Document your project, highlighting your approach and key findings.
Compilation of MLflow Resources and Best Practices
Show steps
Gather and organize valuable resources and best practices related to MLflow, a key tool in MLops.
Browse courses on
MlFlow
Show steps
-
Collect documentation, tutorials, and articles on MLflow from reputable sources.
-
Identify and include best practices for using MLflow in MLops workflows.
-
Organize the resources into a structured compilation, such as a document or website.
Participation in Kaggle MLops Competition
Show steps
Test your MLops skills and learn from others by participating in a Kaggle competition focused on MLops.
Browse courses on
Kaggle Competitions
Show steps
-
Identify an MLops-related competition on Kaggle that aligns with your interests.
-
Form a team or participate individually.
-
Develop and implement an MLops solution to address the competition challenge.
-
Submit your solution and track your progress on the leaderboard.
-
Analyze the results and learn from the top-performing solutions.
Review Jupyter Notebooks
Show steps
Ensure fluency in Jupyter Notebooks, which are commonly used in the course and MLops in general.
Browse courses on
Jupyter Notebooks
Show steps
Show steps
Show steps
- Review online tutorials or documentation on Jupyter Notebooks.
- Practice creating and editing notebooks using the Jupyter interface.
Intermediate Python Programming Practice
Show steps
Strengthen your Python programming skills, which are essential for implementing MLops practices.
Browse courses on
Python
Show steps
Show steps
Show steps
- Solve intermediate-level Python programming problems on coding platforms like LeetCode or HackerRank.
- Participate in online forums or discussion groups to clarify concepts and get feedback on your code.
Guided Tutorials on Machine Learning Operations (MLops)
Show steps
Familiarize yourself with the fundamental concepts of MLops, preparing you for the topics covered in the course.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Enroll in a beginner-friendly MLops tutorial series or course.
- Complete the tutorials and exercises to grasp the basics of MLops.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow along with interactive MLFlow tutorials
Show steps
Gain hands-on experience and build a deeper understanding of how to use MLFlow effectively by following interactive tutorials.
Browse courses on
MlFlow
Show steps
Show steps
Show steps
- Find MLFlow tutorials on platforms like Coursera, edX, or Udemy.
- Set aside dedicated time to follow along with the tutorials, experimenting with different features.
- Take notes and document your learning to reinforce understanding.
Solve MLFlow practice problems
Show steps
Build confidence and understanding of fundamental MLFlow principles by solving practice problems.
Browse courses on
MlFlow
Show steps
Show steps
Show steps
- Attempt beginner level MLFlow problems from online platforms.
- Enroll in practice-based MLFlow courses or online programs.
- Join MLFlow online forums and actively engage in discussions and problem-solving.
Exploratory Data Analysis and Preprocessing Project
Show steps
Gain hands-on experience in performing EDA and data preprocessing, which are crucial steps in MLops.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Obtain a publicly available dataset related to your interests.
- Perform exploratory data analysis using techniques like visualizations and statistical analysis.
- Apply data preprocessing techniques such as cleaning, handling missing values, and feature engineering.
- Document your findings and insights in a report or presentation.
Workshop on ML Model Validation and Testing
Show steps
Gain practical experience in validating and testing ML models, a crucial aspect of MLops covered in the course.
Show steps
Show steps
Show steps
- Attend a hands-on workshop on ML model validation and testing.
- Participate in exercises and discussions to apply concepts and techniques.
Study Group for Machine Learning Pipelines
Show steps
Engage with peers to discuss and clarify concepts related to building ML pipelines, a key topic in the course.
Browse courses on
Machine Learning Pipelines
Show steps
Show steps
Show steps
- Form a study group with classmates or other learners interested in ML pipelines.
- Meet regularly to discuss course materials, share insights, and work on ML pipeline projects together.
Build a mini MLFlow project
Show steps
Synthesize your understanding of MLFlow by creating a practical project, solidifying your knowledge and enhancing your portfolio.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a small-scale MLFlow project idea that aligns with your interests.
- Gather and prepare a dataset relevant to your project.
- Implement your project using MLFlow, ensuring proper versioning and tracking.
- Document your project, highlighting your approach and key findings.
Compilation of MLflow Resources and Best Practices
Show steps
Gather and organize valuable resources and best practices related to MLflow, a key tool in MLops.
Browse courses on
MlFlow
Show steps
Show steps
Show steps
- Collect documentation, tutorials, and articles on MLflow from reputable sources.
- Identify and include best practices for using MLflow in MLops workflows.
- Organize the resources into a structured compilation, such as a document or website.
Participation in Kaggle MLops Competition
Show steps
Test your MLops skills and learn from others by participating in a Kaggle competition focused on MLops.
Browse courses on
Kaggle Competitions
Show steps
Show steps
Show steps
- Identify an MLops-related competition on Kaggle that aligns with your interests.
- Form a team or participate individually.
- Develop and implement an MLops solution to address the competition challenge.
- Submit your solution and track your progress on the leaderboard.
- Analyze the results and learn from the top-performing solutions.
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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