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Made With ML

Applied ML ยท MLOps ยท Production

Join 30K+ developers in learning how to responsibly deliver value with ML.

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๐Ÿ†  Among the top ML repositories on GitHub.
โค๏ธ  30K+ community members and growing.
๐Ÿ› ๏ธ  Highly recommended industry resource.

Interactive MLOps Course

While all the lessons below are 100% free, it's hard to learn everything on your own. That's why we're offering an interactive course with the structure and community to actually complete and master these lessons.

Deadline: Sept 30th, 2022
Start date: Oct 1st, 2022
(less than 20 seats remaining!)

Foundations

Learn the foundations of machine learning through intuitive explanations, clean code and visualizations. → GokuMohandas/Made-With-ML


MLOps course

Learn how to combine machine learning with software engineering to build production-grade applications. → GokuMohandas/mlops-course

๐Ÿ’ป Developing ๐Ÿ“ฆ Serving โœ… Testing
โ™ป๏ธ Reproducibility ๐Ÿš€ Production โŽˆ Data engineering

Features

Up-to-date lessons + the structure & community to actually complete and master them.

Lessons
All lessons and code are 100% free with a focus on intuition and implementation.
Structure*
Weekly structure to keep you accountable towards mastering the content.
Live Q&A*
Live deep dives into questions. Offered multiple times a week across multiple timezones.
Community*
Private community for async Q&A, feedback and connections for during and after the course.
Reading sessions*
Cohort reading sessions to supplement and solidify understanding on the concepts.
Certificate*
Recognition of completion of the course with deliverables to prove your mastery.

* = features reserved for the interactive course


Alumni Reviews

Read what alumni from previous cohorts have to say about the interactive course.

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Meet your instructor

Goku Mohandas

Hi, I'm Goku Mohandas

Over the past 7 years, I've worked on ML and product at a large company (Apple), a startup in the oncology space (Ciitizen) and ran my own startup in the rideshare space (HotSpot). Throughout my journey, I've worked with brilliant developers and product managers and learned how to responsibly develop, deploy and iterate on ML systems across various industries.

I currently work closely with early-stage and F500 companies in helping them deliver value with ML while diving into the best and bespoke practices of this rapidly evolving space. I want to share this knowledge with the rest of the world so we can accelerate progress in this space.

ML is not a separate industry, instead, it's a powerful way of thinking about data, so let's make sure we have a solid foundation before we start changing the world with it. Made With ML is our medium to catalyze this goal and though we're off to great start, we still have a long way to go.


Schedule

  • Once you apply to the course, we will approve it and you'll receive a link to our Stripe checkout page.
  • You'll receive instructions to join our private community forum and introduce yourself to the cohort.
  • You'll receive the assignments and deliverables for Week 1.
  • Individually: ๐ŸŽจ  Design + ๐Ÿ”ข  Data lessons
  • Q&A sessions:
  • Cohort reading: Assigned (discussion next week)
  • Individually: ๐Ÿ“ˆ  Modeling lessons
  • Q&A sessions:
  • Cohort reading:
  • Individually: ๐Ÿ’ป  Developing lessons
  • Q&A sessions:
  • Cohort reading: Assigned (discussion next week)
  • Individually: ๐Ÿ“ฆ  Serving lessons
  • Q&A sessions:
  • Cohort reading:
  • Individually: โœ…  Testing lessons
  • Q&A sessions:
  • Cohort reading: Assigned (discussion next week)
  • Individually: โ™ป๏ธ  Reproducibility lessons
  • Q&A sessions:
  • Cohort reading:
  • We're off this and next week for Thanksgiving break. Use this time to catch-up if you have fallen behind on the weekly deliverables and continue to ask and answer questions in the community!
  • A (longer) reading will be assigned to read over break and be discussed once we're back.
  • We're off this and next week for Thanksgiving break. Use this time to catch-up if you have fallen behind on the weekly deliverables and continue to ask and answer questions in the community!
  • Continue with last week's (longer) reading assignment that will be discussed once we're back next week.
  • Individually: ๐Ÿš€  Production lessons
  • Q&A sessions:
  • Cohort reading:
  • Individually: โŽˆ  Data engineering lessons
  • Q&A sessions:
  • Cohort reading: Assigned (discussion next week)
  • Individually: Conclusion
  • Q&A sessions:
  • Cohort reading:

Pricing

Our alumni were able to reimburse the cost through their company's Learning and Development (L&D) budgets. Refer to this reimbursement template for more details.

Deadline: Sept 30th, 2022
Start date: Oct 1st, 2022
(less than 20 seats remaining!)

Free

  •  All lessons and code
  •  Weekly structure + deliverables
  •  Live Q&A office hours
  •  Private community forum
  •  Cohort reading sessions
  •  Certificate of completion

Interactive

  •  All lessons and code
  •  Weekly structure + deliverables
  •  Live Q&A office hours
  •  Private community forum
  •  Cohort reading sessions
  •  Certificate of completion

Frequently Asked Questions (FAQ)

Machine learning is not a separate industry, instead, it's a powerful way of thinking about data that's not reserved for any one type of person.
  • Software engineers looking to learn ML and become even better software engineers. ML is integrated into increasingly more products so it's important to know how ML systems operate.
  • Data scientists who want to want to go way beyond developing models in a notebook to wrapping them around robust workflows that enable ML systems to improve over time.
  • College graduates looking to learn ML and become even better software engineers. ML is integrated into increasingly more products so it's important to know how ML systems operate.
  • Product managers who want to develop a technical foundation in MLOps so they can effectively communicate with their technical team and help develop and iterate on applications.

You should know how to code in Python and the basics of machine learning.

You will NOT need to know any deep learning topics or related libraries (ex. PyTorch) as this course is largely model-agnostic and the focus will be on how to responsibly deliver value with any kind of machine learning model.

Machine learning is increasingly incorporated into many products and so companies are looking for people with increasingly deeper knowledge on not only modeling, but how to operationalize it (MLOps). It's a major advantage to understand the fundamentals of this field at this nascent stage so you can responsibly deliver value with ML as a foundational developer in your industry.
Absolutely, this course is meant for busy people who are in school or working full-time. You can go through the weekly structure when you choose and attend any/all of the live Q&A and reading sessions which are offered multiple times a week across different timezones.
Every week, we'll provide a structured list of todo items that will consist of reading a set of lessons on your own time and completing the corresponding code components alongside. If you have questions, you can attend the live Q&A sessions, post your question on the private community forumn or reach out to the instructors and TAs directly. Estimate 3-5 hours of work per week.
After the course, you'll always have access to our private community where you can connect with alumni and meet future cohorts as well. You can continue to ask questions about the topics (especially as new tools enter the market), get feedback on your work, etc.
Yes, this course is 100% remote which means no travel but you'll meet fellow students from around the world and learn from each other.
If you follow along the weekly structure and complete the deliverables and if you're not 100% satisfied by the end of the first two weeks, we'll refund 100% of the cost.
โ“  If you have additional questions, send us an email and we'll get back to you very soon.

โค๏ธ Wall of Love

See what the community has to say about Made With ML.


To cite this content, please use:

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@article{madewithml,
    author       = {Goku Mohandas},
    title        = { Home - Made With ML },
    howpublished = {\url{https://madewithml.com/}},
    year         = {2022}
}