Machine Learning in Production

Name: Machine Learning in Production
Price: 12.75 USD
Availability: InStock

14h 2m 54s

English

Paid

June 25, 2026

Machine Learning in Production is a 120-lesson 14 hours 2 minutes self-paced course by Kyryl Truskovskyi. This course shows you how to run machine learning systems in the real world.

Course facts

Lessons: 120
Duration: 14 hours 2 minutes
Level: All levels
Language: English
Updated
Instructor: Kyryl Truskovskyi
Price: Premium

This course shows you how to run machine learning systems in the real world.You learn the full path from setup to deployment.

What You Learn

You start with the basics of ML infrastructure. You then build and ship models in a safe way. You also track, test, and watch your models after they go live.

The goal is simple. You learn how to support an ML system from the first idea to daily use.

How the Course Works

The course runs for eight weeks. You move from model work to full ML engineering. You learn step by step and build real skills as you go.

You can join a live cohort. You can also learn at your own pace if that fits your schedule better.

Who This Course Helps

This course helps you if you know ML models but want to ship them. It also helps you if you want to work on ML systems in a real team.

You end the course ready to build, deploy, and care for ML models in production.

Who teaches Machine Learning in Production? Kyryl Truskovskyi

I am Kirill, an experienced specialist in the field of machine learning and a venture fund expert, working at the intersection of engineering and business. I have collaborated with fast-growing startups, large companies, and VC funds, helping more than 40 companies increase ROI through the implementation of practical ML solutions. My approach combines technical depth and business expertise, ensuring that learning is as practical and relevant as possible. My course "Machine Learning in Production" has helped hundreds of data scientists and ML engineers advance their careers by mastering the full lifecycle of ML—from idea to production and further operation. Despite publications at leading conferences such as ACL, CVPR, and EACL, my main focus is on real business applications that deliver measurable results.

What lessons are included in Machine Learning in Production?

0:00

#1: A message from the instructor

All Course Lessons (120)

#	Lesson Title	Duration
1	A message from the instructor Demo	02:13
2	Agenda	02:05
3	Why: Motivation	11:41
4	MLOps Stages	10:35
5	MLOps assessment	03:35
6	Design document	05:10
7	Practice	04:37
8	Practice implementation example	22:39
9	Takeaways	00:52
10	Agenda	00:45
11	Why: Motivation	05:10
12	Docker	09:51
13	Kubernetes	09:38
14	Costs & CI/CD	09:54
15	Practice	02:45
16	Practice implementation example	49:59
17	Takeaways	00:44
18	Agenda	00:46
19	Why: Motivation	02:35
20	Storage	11:19
21	RAG	03:04
22	Formats	06:23
23	Practice	02:26
24	Practice implementation example	48:13
25	Takeaways	00:35
26	Agenda	00:37
27	Why: Motivation	01:15
28	Labeling	13:30
29	Versioning / Validation	05:08
30	Practice	03:15
31	Practice implementation example	25:31
32	Takeaways	00:43
33	Agenda	00:38
34	Why: Motivation	02:30
35	Project structure	09:48
36	Experiment management	06:48
37	Experiment running	10:29
38	Practice	02:31
39	Practice implementation example	28:52
40	Takeaways	00:38
41	Agenda	00:46
42	Why: Motivation	01:37
43	Testing	18:12
44	CI/CD	02:26
45	Practice	02:09
46	Practice implementation example	21:20
47	Takeaways	00:38
48	Agenda	01:01
49	Why: Motivation	04:49
50	Orchestration idea	08:25
51	Kubeflow	06:01
52	AirFlow	07:10
53	Practice	02:31
54	Practice implementation example	48:46
55	Takeaways	01:05
56	Agenda	01:29
57	Why: Motivation	03:03
58	ML project journey	04:16
59	Dagster	07:25
60	Practice	02:36
61	Practice implementation example	20:54
62	Takeaways	01:09
63	Check in	00:30
64	Agenda	00:48
65	Why: Motivation	04:31
66	Pre-serving	05:48
67	Custom web server	05:45
68	Inference server	09:11
69	Practice	02:06
70	Practice implementation example	36:05
71	Takeaways	00:56
72	Agenda	00:56
73	Why: Motivation	02:45
74	Serving platforms	08:23
75	Serving patterns	08:01
76	Serving LLMs	05:13
77	Practice	02:14
78	Practice implementation example	49:17
79	Takeaways	00:43
80	Agenda	00:41
81	Why: Motivation	01:46
82	Deployment	07:56
83	Advanced features	17:19
84	Benchmarking	08:22
85	Practice	02:22
86	Takeaways	00:39
87	Agenda	02:43
88	Why: Motivation	02:58
89	Scaling infra	20:51
90	Scaling model	16:46
91	Practice	02:21
92	Takeaways	00:47
93	Agenda	01:12
94	Why: Motivation	05:09
95	Not-ML systems	08:55
96	ML systems	09:49
97	Drift cases	04:55
98	Practice	03:39
99	Takeaways	00:39
100	Agenda	01:11
101	Why: Motivation	02:30
102	ML monitoring tools	07:35
103	LLMs & Data Moat	06:43
104	ML for monitoring	02:39
105	Practice	02:43
106	Takeaways	00:40
107	Agenda	00:56
108	Why: Motivation	01:01
109	Platforms for ML	08:09
110	AWS SageMaker	12:01
111	GCP Vertex AI	08:44
112	Practice	02:24
113	Takeaways	01:08
114	Agenda	00:45
115	Why: Motivation	02:32
116	Up-to-date	03:13
117	Summary	04:55
118	Practice	01:10
119	Takeaways	00:35
120	Congrats! Here's what's next...	00:44

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Books

Read Book Machine Learning in Production

#	Title	Type
1	Module-1-lecture-1	PDF
2	Module-1-lecture-2	PDF
3	Module-2-lecture-1	PDF
4	Module-2-lecture-2	PDF
5	Module-3-lecture-1	PDF
6	Module-3-lecture-2	PDF
7	Module-4-lecture-1	PDF
8	Module-4-lecture-2	PDF
9	Module-5-lecture-1	PDF
10	Module-5-lecture-2	PDF
11	Module-6-lecture-1	PDF
12	Module-6-lecture-2	PDF
13	Module-7-lecture-1	PDF
14	Module-7-lecture-2	PDF
15	Module-8-lecture-1	PDF
16	Module-8-lecture-2	PDF

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Frequently asked questions

What prerequisites are required for this course?

This course is designed for individuals who have a foundational understanding of machine learning models. It is ideal for those who wish to advance their skills by learning how to deploy and manage these models in production environments. Familiarity with basic ML concepts and experience in model building is recommended before enrolling.

What kind of projects will I work on during the course?

Throughout the course, you will engage in practice implementation examples that cover a variety of topics such as MLOps stages, Docker, Kubernetes, and CI/CD processes. The practical projects are designed to help you build real skills, moving from basic model work to full ML engineering tasks, and preparing you for real-world deployment scenarios.

Who is the target audience for this course?

The course is aimed at practitioners who understand machine learning models but are looking to enhance their skills in deploying these models into production. It is also suitable for professionals who aspire to work in teams managing machine learning systems. By the end of the course, participants will be equipped to support ML systems from design to daily operation.

How does this course compare to other machine learning courses in terms of scope?

Unlike many introductory courses focused on model building, this course delves into the operational aspects of machine learning systems. It covers infrastructure setup, model deployment, monitoring, and management in real-world environments. Key topics include MLOps stages, orchestration with Kubeflow and AirFlow, and experiment management, providing a comprehensive look at the lifecycle of ML products in production.

What specific tools and platforms will be covered in the course?

The course covers a range of tools and platforms essential for ML operations, including Docker, Kubernetes, Kubeflow, and AirFlow. These tools are integral to setting up, deploying, and orchestrating machine learning models in production. Additionally, lesson topics include CI/CD processes, testing, and experiment management, ensuring a holistic understanding of the deployment pipeline.

What topics are not covered in this course?

The course does not focus on the basics of machine learning model development or the theoretical underpinnings of machine learning algorithms. Instead, it assumes prior knowledge in these areas and concentrates on the practical aspects of deploying and managing models in a production environment.

What is the expected time commitment for completing this course?

The course is structured to be completed over eight weeks, with a total of 120 lessons. Participants can choose to join a live cohort or proceed at their own pace, allowing flexibility to accommodate different schedules. The lessons are designed to progressively build skills, from basic infrastructure setup to comprehensive ML engineering practices.