Live cohort details

The current live cohort is underway, but you can join the next one starting in January 2025. Our live cohort offers an intensive, hands-on journey to master ML projects end-to-end. This is a time-sensitive chance to work directly with me, gaining practical experience and insights to transform your career in just 2 months.

  • Weekly Live Practical Workshops

    Join weekly sessions with Kyryl on Saturdays at 10:30 AM EST, where you'll tackle real-world challenges and receive in-depth explanations for each module's assignments, ensuring you can apply what you learn immediately.

  • Personalized Feedback

    Receive detailed reviews on your assignments, including Q&As, code reviews, and practical tips to improve your work + group chat access for ongoing support, networking, and direct interaction with Kyryl and your peers.

The Plan

Your journey to becoming a complete ML expert

In just 8 weeks, you’ll transform from being a data scientist focused on modeling to a professional who can handle the entire ML lifecycle. Choose between our live cohort starting September 9th, 2024, or our asynchronous course to learn at your own pace.

Week 1: Learn to set up and manage Docker, Kubernetes, and CI/CD pipelines.

Week 2: Master advanced data storage, processing, versioning, labeling techniques, and Retrieval-Augmented Generation (RAG).

Week 3: Structure, run, and optimize experiments to ensure peak model performance.

Week 4: Streamline workflows with powerful tools like Dagster, Kubeflow and AirFlow.

Week 5-6: Implement, scale, and serve your models using the latest strategies, including handling Large Language Models (LLMs).

Week 7: Keep your models performing at their best with robust monitoring and maintenance strategies, including tools and techniques for monitoring LLMs and managing data drift.

Week 8: Navigate the complexities of vendor selection and platform integration with a focus on AWS SageMaker, GCP Vertex AI, and the latest trends.

Capstone Project Presentation - Apply everything you’ve learned to complete an end-to-end ML project and present it.
Start your journey

Course curriculum

    1. A message from the instructor

      FREE PREVIEW

    2. How to use this course

      FREE PREVIEW

    3. The live cohort workshops schedule

      FREE PREVIEW

    4. Setup

    5. Project examples

    6. Before we begin ...

    1. Agenda

      FREE PREVIEW

    2. Why: Motivation

      FREE PREVIEW

    3. MLOps Stages

    4. MLOps assessment

    5. Design document

    6. Practice

    7. Practice submission

    8. Practice implementation example

    9. Takeaways

    10. Feedback

    1. Agenda

      FREE PREVIEW

    2. Why: Motivation

    3. Docker

    4. Kubernetes

    5. Costs & CI/CD

    6. Practice

    7. Practice submission

    8. Practice implementation example

    9. Takeaways

    10. Feedback

    1. Agenda

      FREE PREVIEW

    2. Why: Motivation

    3. Storage

    4. RAG

    5. Formats

    6. Practice

    7. Practice submission

    8. Practice implementation example

    9. Takeaways

    10. Feedback

    1. Agenda

      FREE PREVIEW

    2. Why: Motivation

    3. Labeling

    4. Versioning / Validation

    5. Practice

    6. Practice submission

    7. Practice implementation example

    8. Takeaways

    9. Feedback

    1. Agenda

      FREE PREVIEW

    2. Why: Motivation

    3. Project structure

    4. Experiment management

    5. Experiment running

    6. Practice

    7. Practice submission

    8. Practice implementation example

    9. Takeaways

    10. Feedback

About this course

  • 164 lessons
  • 14 hours of video content
  • > 5 hours a week
  • 8 weeks

Course artifacts

As you progress through "Machine Learning in Production," you'll develop a range of valuable artifacts that showcase your skills and learning.

  • Capstone project
    A fully implemented end-to-end ML project demonstrating your ability to handle real-life challenges from start to finish.

  • Design document
    A detailed and comprehensive design document refined throughout the course, covering all aspects of your ML project.

  • Reusable code templates
    Practical and reusable code templates from each module, providing a solid foundation for your future ML projects.

What our students say

“I am incredibly satisfied with the course! I received everything I expected, and even much more. I liked the use of Kubernetes in homework assignments: I never got around to learning it, but during the course, it solved the given tasks very well. The program’s structure is amazing! I like that everything is built around problem-solving and the widely accepted approaches/products that help in solving these problems. I appreciated the Kyryl’s awareness of trends: what everyone is using now, and what is better not to use – such things are unlikely to be explained in classical academic courses. It’s great that the knowledge from the courses can be applied at work. To thoroughly study the course, doing homework is necessary. I have gaps in building models, so it would be interesting if a similar course was created for in-depth learning of different models (LLM, CV, etc.).”

Roma Slyusarchuk | Staff Software Engineer @ Google

“I really enjoyed the “Machine Learning in Production” course. The lectures were fun, filled with up-to-date material, concise, understandable, and insightful. Also, I liked weekly live workshops, which were very helpful, and I learned a lot from seeing how everything should work and listening to other students’ use cases and questions. Kyryl’s explanations of different concepts and nuances were very clear and to point. Very important is that this course had quite challenging and interesting homeworks, which I think greatly impacted everyone’s knowledge gain. Overall, this is the best MLOps course I’ve seen, and I highly recommend it to anyone interested in this topic.”

Maria Ponomarenko | NLP Engineer @ Larus Technologies

“I really enjoyed this course on the machine learning model lifecycle, taught by Kyryl Truskovskyi. It was hands-on, covering everything from data handling to model deployment, with real-life challenges that kept me hooked. The personal feedback and the major project we had to complete were especially rewarding. Big thanks to Kyryl for making it such a valuable experience.”

Nazar Shmatko | Machine Learning Engineer @ GPTZero

Pricing options

Choose the payment option that fits your needs and start mastering ML in production today! All options provide complete access to course materials.

FAQ

  • Who is this course for?

    This course is perfect for professionals who want to master ML applications in real-world scenarios, including:

    Software Engineers: Engineers with a solid foundation who want to build LLM/ML-based products while maintaining their strong engineering skills.

    Data/Research Scientists: Professionals who want to go beyond ML/LLM modeling and engage in the final stages of product delivery, ensuring their models are effectively implemented in production.

    ML/AI Engineers: Engineers looking to structure and revisit their knowledge, staying updated with the latest developments and best practices in the field.

  • What is the live cohort?

    The live cohort is a time-bound, intensive version of the course that starts on September 9th. It includes weekly live workshops with Kyryl, where you'll receive real-time instruction and tackle practical assignments. Kyryl will personally review all homework and provide feedback. Additionally, you'll have access to a private group chat on Discord for continuous support and networking with Kyryl and fellow learners.

    The live cohort starts on September 9th. It is a time-bound program, so you need to enroll before this date to participate.

  • How does the live cohort differ from the asynchronous course?

    The live cohort offers weekly live sessions with Kyryl, personalized homework feedback, and a dedicated group chat for real-time interaction and support. In contrast, the asynchronous course allows you to study at your own pace without live sessions or direct feedback on your assignments. Both versions include full course access, but the live cohort provides a more interactive and guided learning experience.

    There is also a price difference. The live cohort is priced at $990 USD. The asynchronous course, which provides the same comprehensive content without live sessions and direct feedback, is priced at $490 USD.

  • What prerequisites do I need?

    Basic understanding of programming concepts and familiarity with machine learning principles are recommended to get the most out of this course.

  • How long is the course?

    The course consists of 8 modules, each with 2 lectures. Ideally, it's designed to be completed over 8 weeks, dedicating one week per module. However, for flexibility, participants can choose to extend their learning to 16 weeks, focusing on one lecture per week to accommodate varying schedules and learning paces.

  • Will I lose access to the course materials after 2 months?

    No, you have access to the course materials indefinitely.

  • How much time will I need to dedicate to the course each week?

    The course is flexible, allowing you to adjust your learning pace according to your schedule. For those in the live cohort, you can expect at least 2 hours of live practical sessions with Kyryl each week, plus time for assignments and self-study. This structure ensures you get the most out of the course, whether you prefer a more intensive or a more relaxed learning approach.

  • What if I get stuck on my homework?

    Don't worry! Our course comes with a supportive Discord community channel dedicated to homework help. Here, you can ask questions, seek guidance, and collaborate with fellow learners and the instructor. Additionally, for each homework assignment, we provide an example implementation to guide you through the process.

    For live cohort participants, there is additional support through weekly live sessions and a dedicated group chat with Kyryl and fellow learners, offering real-time assistance and a more interactive learning environment.