Real-world projects from industry experts
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
Learn to be more productive through ML projects that require reproducible workflow best practices.
Get access to classroom immediately on enrollment
Learn the fundamentals of MLOps and how to create a clean, organized, reproducible, end-to-end machine learning pipeline from scratch using MLflow. Clean and validate data using pytest and tracking experiments, code, and results using GitHub and Weights & Biases. Plus, learn to select the best-performing model for production and deploy a model using MLflow.
Intermediate Python, Jupyter Notebooks
Learn MLOps fundamentals and dive into version data and artifacts. Write a ML pipeline component and link together ML components.
Execute and track the Exploratory Data Analysis (EDA). Clean and pre-process the data and segregate (split) datasets.
Use pytest with parameters for reproducible and automatic data tests. Perform deterministic and non-deterministic data tests.
Tame the chaos with experiment, code, and data tracking. Track experiments with W&B. Validate and choose the best-performing model. Export model as an inference artifact and test final inference artifact.
Release pipeline code and learn options for deployment and how to deploy a model.
Write a machine learning pipeline to solve the following problem: A property management company is renting rooms and properties in New York for short periods on various rental platforms. They need to estimate the typical price for a given property based on the price of similar properties. The company receives new data in bulk every week, so the model needs to be retrained with the same cadence, necessitating a reusable pipeline. Write an end-to-end pipeline covering data fetching, validation, segregation, train and validation, test, and release. Run it on an initial data sample, then re-run it on a new data sample simulating a new data delivery.
With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.
On demand help. Receive instant help with your learning directly in the classroom. Stay on track and get unstuck.
Validate your understanding of concepts learned by checking the output and quality of your code in real-time.
Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.
We provide services customized for your needs at every step of your learning journey to ensure your success.
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Giacomo Vianello is an end-to-end data scientist with a passion for state-of-the-art but practical technical solutions. He is Principal Data Scientist at Cape Analytics, where he develops AI systems to extract intelligence from geospatial imagery bringing, cutting-edge AI solutions to the insurance and real estate industries.
Learn to be more productive through ML projects that require reproducible workflow best practices.
On average, successful students take 1 month to complete this program.
No. This Course accepts all applicants regardless of experience and specific background.
To be successful in this program, learners should have intermediate Python skills and understanding of Jupyter Notebooks.
This course is comprised of content and curriculum to support one project. We estimate that students can complete the program in one month.
The project will be reviewed by the Udacity reviewer network and platform. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
Access to this course runs for the length of time specified in the payment card above. If you do not graduate within that time period, you will continue learning with month to month payments. See the Terms of Use and FAQs for other policies regarding the terms of access to our programs.
Please see the Udacity Program Terms of Use and FAQs for policies on enrollment in our programs.
Learners should have access to Python, Pytorch, Weights & Biases, Hydra, and MLflow.