Data Centric Deep Learning
Build, improve, and repair deep learning applications with a data-centric approach. Data is the key to success in modern machine learning, and this course provides hands-on experience with the impact of data quality, improving models via data, realistic performance evaluation, and human-in-the-loop data improvement methods. Learn best practices for achieving production-quality deep learning results, and how new technologies like pre-trained foundation models can make development faster and simpler. Understand how data-centric principles apply when developing LLM-based applications, agents, and retrieval-augmented generation (RAG) systems.
Course taught by expert instructors
Andrew Maas
Co-founder and CEO of Pointable
Andrew Maas is co-founder and CEO of Pointable, a platform for metrics-driven development of RAG-LLM conversational agents. He previously led teams developing data-centric deep learning approaches at Apple and was a co-founder of Roam Analytics (acquired by Parexel) -- a natural language extraction platform for healthcare. Andrew earned a PhD in computer science from Stanford University, advised by Andrew Ng and Dan Jurafsky, where his work focused on large-scale deep learning for spoken and written language. Andrew also advises machine learning startups and teaches a graduate course on spoken language processing at Stanford.
Mike Wu
PhD Scholar at Stanford
Mike Wu is currently a fifth year PhD student at Stanford University advised by Noah Goodman. His research spans the fields of inference algorithms, deep generative models, and unsupervised learning. Mike’s research has appeared in NeurIPS, ICLR, AISTATS, and other top ML conferences with two best paper awards and his work has been featured in the New York Times. Mike previously worked as a software engineer at an AI startup called Lattice Data, and as a research engineer at Meta’s applied machine learning group. Mike and Andrew designed and taught a new version of Stanford’s CS224S: Spoken Language Processing in 2022.
The course
Learn and apply skills with real-world projects.
Try these prep courses first
A course you'll actually complete. AI-powered learning that drives results.
AI-powered learning
Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.
Live courses by leading experts
Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.
Practice on the cutting edge
Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.
Flexible schedule for busy professionals
We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.
Completion certificates
Each course comes with a certificate for learners to add to their resume.
Best-in-class outcomes
15-20x engagement compared to async courses
Support & accountability
You are never alone, we provide support throughout the course.
Get reimbursed by your company
More than half of learners get their Courses and Memberships reimbursed by their company.
Hundreds of companies have dedicated L&D and education budgets that have covered the costs.
Course success stories
Learn together and share experiences with other industry professionals
This course is incredibly important and useful! I believe it should be required in any data-science curriculum. We gained practical skills to tackle problems that data scientists and machine learning engineers often face when dealing with real-world messy data. I learned so much more than the course material due to the encouragement and guidance of Mike Wu!
DCDL has taken my experience with ML from modeling datasets in Colab notebooks to working in a full ML system in a codebase. We touched upon the full lifecycle of ML — from annotating and cleaning data, to model training, to evaluation and testing, deployment, and monitoring. What an incredibly insightful 4 weeks of learning!
This final course in the ML track series provided a realistic framework bridging the concepts we have covered in all 3 classes into a more productionalized format. This course has given a real insight into what a real ML backend may look like and the steps required to get there.
