CF

ML Project Blueprint

2h 55m 37s
English
Paid

ML Project Blueprint is a 16-lesson 2 hours 55 minutes self-paced course by Timur Bikmukhametov. ML Project Blueprint is a practical course that demonstrates the full cycle of developing an ML system: from raw data to deployment in the cloud.

Course facts

Lessons
16
Duration
2 hours 55 minutes
Level
All levels
Language
English
Updated
Instructor
Timur Bikmukhametov
Price
Premium

ML Project Blueprint is a practical course that demonstrates the full cycle of developing an ML system: from raw data to deployment in the cloud.

You will not only train a model but also build a production-ready solution, understanding each stage of the process:

  • how to turn code from a notebook into a real ML service
  • how to build an end-to-end pipeline
  • how to organize code and architecture at an industry level
  • how to deploy a model and run it in real time

During the course, you will receive:

  • a ready-made ML project template that can be reused
  • complete system code and a professional GitHub repository
  • experience working with Docker, API, and cloud deployment
  • an interactive dashboard for model demonstration

The course bridges the main gap between "knowing ML" and "being able to create real ML products".

Who teaches ML Project Blueprint? Timur Bikmukhametov

Timur Bikmukhametov thumbnail

Timur Bikmukhametov — Principal Data Scientist and author of educational programs in the field of Machine Learning with over 8 years of experience. Founder of ML Academy, where he teaches building end-to-end ML systems — from working with data to production deployment. He specializes in the practical application of ML in real business tasks and career development in Data Science.

What lessons are included in ML Project Blueprint?

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#1: Introduction
All Course Lessons (16)
#Lesson TitleDurationAccess
1
Introduction Demo
01:09
2
Video 1 - Overview of end-to-end ML development cycle
04:11
3
Video 2 - Overview of the Data Scientist role in ML application development
03:27
4
Video 3 - Course ML Solution Overview
14:55
5
Video 4 - Installing Conda and Docker
03:04
6
Video 5 - Overview of Application Development Steps
02:14
7
Video 6 - Overview of the ML Project Structure
06:51
8
Video 7 - EDA Analysis of the Data
22:23
9
Video 8 - Building an ML Model
25:22
10
Video 9 - Building Training Pipeline
18:22
11
Video 10 - Building Inference Pipelines
15:24
12
Video 11 - Building the UI App with Dash
14:33
13
Video 12 - Introducing and running Docker
19:41
14
Video 13 - Installing git and pushing code to GitHub
06:31
15
Video 14 - Deploying application on cloud
08:58
16
Video 15 - Summing up and next steps
08:32
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Books

Read Book ML Project Blueprint

#TitleTypeOpen
1Installation Guides PDF
2ML Blueprint Slides PDF

What courses are similar to ML Project Blueprint?

Frequently asked questions

What prerequisites are needed before enrolling in this course?
Before enrolling in this course, students should have a foundational understanding of machine learning concepts and some experience in programming. Familiarity with tools such as Conda, Docker, and Git would be beneficial, as the course involves installing and using these tools during the ML project development cycle.
What will I build during the course?
During the course, you will build a complete machine learning system, starting from raw data and progressing to a production-ready solution. This includes creating an end-to-end pipeline, deploying the model as a real ML service, and developing an interactive dashboard for model demonstration. The course also provides a reusable ML project template.
Who is the target audience for this course?
The target audience for this course includes individuals who have a basic understanding of machine learning and seek to bridge the gap between theoretical knowledge and practical application. It is especially suitable for those looking to transition from knowing ML to being able to create real ML products.
How does this course compare in depth and scope to other ML courses?
Unlike many ML courses that focus primarily on model training, this course covers the entire ML development cycle. It includes organizing code and architecture at an industry level, deploying models in real-time using Docker and cloud services, and building a UI app with Dash, offering a comprehensive overview of creating production-ready ML applications.
What specific tools and platforms will I learn to use?
The course involves using several specific tools and platforms including Conda for environment management, Docker for containerization, and Git for version control. You will also learn to build UI applications with Dash and deploy ML applications on cloud platforms.
What topics are not covered in this course?
The course does not cover the theoretical aspects of machine learning algorithms in depth, nor does it focus on data science topics like data cleaning and feature engineering. It emphasizes practical implementation over theoretical exploration.
What is the expected time commitment for completing this course?
The course consists of 16 lessons, each focusing on different aspects of ML project development. While there is no specified runtime, students should allocate enough time to thoroughly engage with each lesson, practice the concepts, and build the project components, which could vary depending on individual pace.