Building a Real-Time ML System. Together

Name: Building a Real-Time ML System. Together
Price: 9 USD
Availability: InStock

48h 20m 35s

English

Paid

January 8, 2026

Course description

Learn to design, develop, deploy, and scale end-to-end real-time ML systems using Python, Rust, LLMs, and Kubernetes.

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#1: What's new in this cohort?

All Course Lessons (188)

#	Lesson Title	Duration
1	What's new in this cohort? Demo	07:24
2	How to install the tools	19:55
3	How to create the local Kubernetes cluster	08:26
4	How to open a Github issue	06:38
5	ML System design - Part 1	31:30
6	ML System design - Part 2	11:02
7	Dev and Prod environments - Say hi to Marius!	04:24
8	Bootstrap the uv workspace & Install Kafka in our dev environment	26:26
9	Install Kafka UI in our dev environment	14:37
10	Push fake data into Kafka	25:08
11	Push real trade data to Kafka - Part 1	31:11
12	Push real trade data to Kafka - Part 2	16:05
13	.gitignore	03:33
14	Why Docker?	04:23
15	Wrap up	03:05
16	3 questions for YOU	03:21
17	Working inside the devcontainer - Let's (re)create the dev cluster	15:38
18	Dockerfile for the trades service	10:49
19	How to deploy the trades service to the dev Kubernetes cluster	27:00
20	Debugging, debugging, debugging	30:36
21	Decouple config parameters from business logic code in the trades service	31:11
22	Let's recap	03:28
23	Pre-commits for automatic code linting and formatting	16:52
24	Candles service boilerplate code	26:10
25	Open question -> How to do a double port-forwarding?	01:38
26	Wrap up	01:57
27	Plan for today	04:36
28	Redeploy the trades service to the dev cluster	28:16
29	Add key=product_id to the trade messages - otherwise the candles service cannot process them	28:13
30	Deploy the candles service to the dev kubernetes cluster	14:21
31	Horizontaly scaling of the candles service - Kafka consumer groups to the rescue!	14:22
32	Build and push the docker image for the candles to the production Github Container Registry	30:26
33	Deploy the candles service to PROD cluster	27:07
34	Technical-indicators service boilerplate code	21:13
35	Wrap up	01:51
36	Quick recap before we start office hours	10:59
37	How to spin up a PROD Kubernetes cluster	28:41
38	How to Monitor a Kubernetes cluster	06:26
39	One of my candles servie replica is waiting for messages. Why?	06:49
40	A detour around ZenML and Flyte	06:59
41	What's the project repo structure of this course?	11:59
42	If you like it, please recommend us :-)	03:35
43	We want you Marius	13:48
44	How to inspect docker build logs - How to do port-forwarding of services	14:40
45	More port forwarding	19:39
46	How to push data into a Kafka topics?	08:58
47	Again: 1 kafka topic partition and 2 consumer replicas means one of them will be IDLE	16:20
48	Problems building the Docker image?	22:14
49	Wrap up	00:24
50	Goals for today	07:07
51	What are technical indicators and how to compute them in real time	10:18
52	Custom stateful transformation to compute indicators - Part 1	24:11
53	Other libraries for real time data processing	12:02
54	Custom stateful transformation to compute indicators - Part 2	34:35
55	Custom stateful transformation to compute indicators - Part 3	09:42
56	Write Dockerfile with talib library	26:31
57	Some homework for you	14:15
58	Deploying the technical indicators service to Kubernetes	28:37
59	Wrap up	01:46
60	Goals for today	04:46
61	Installing RisingWave in Kubernetes	20:48
62	Pull-based ingestion from Kafka topic to RisingWave	14:11
63	Pull-based vs Push-based data ingestion	28:16
64	Installing Grafana and adding RisingWave as a data source	14:57
65	Plotting candles with Grafana	20:40
66	Ingesting historical trades from Kraken REST API	39:01
67	Custom stateful transformation to compute indicators - Part 2 + Homework	24:19
68	Wrap up	03:19
69	Goals for today	07:05
70	Bash script to build and push Docker images to either `dev` or `prod` Kubernetes cluster	22:07
71	Bash script to deploy services to either `dev` or `prod`	17:15
72	Squashing 2 bugs in the trades service	21:55
73	Deploying the trades-historical to Kubernetes - Part 1	13:44
74	Deploying the trades-historical to Kubernetes - Part 2	04:36
75	Adding custom timestamp extractor in our candles service	04:08
76	Deploying the whole backfill pipeline	30:16
77	ConfigMap for our backfill pipeline	38:02
78	How to scale the backfill pipeline to process 100x volume of trades	09:07
79	Wrap up	01:43
80	Goals for today	11:56
81	Installing MLflow in Kubernetes cluster	28:50
82	Start building the training pipeline -> Load data from RisingWave	23:22
83	Adding the target column to the dataframe	08:44
84	Data validation with Great Expectations	14:01
85	Automatic Exploratory Data Analysis (EDA)	17:10
86	Instrumenting our training runs with MLflow	30:47
87	Build a baseline model	24:31
88	Question -> Do we need GPUs to train our model?	01:22
89	Wrap up	05:57
90	Goals for today	06:31
91	Finding a good model candidate with LazyPredict	42:48
92	Logging model candidate table to MLflow	22:07
93	What is model hyper-parameter tuning?	10:56
94	Hyperparameter tuning with Optuna	27:02
95	Fixing things	28:06
96	Scaling the inputs for our regularised linear model (HuberRegressor) - Part 1	15:05
97	Scaling the inputs for our regularised linear model (HuberRegressor) - Part 2	16:01
98	Wrap up	01:57
99	Goals for today	09:02
100	Refactoring the model selection step of the training pipeline	19:06
101	Checking and dropping NaN values	34:22
102	Validating and pushing the model to the registry	15:34
103	Extracting training inputs into a TrainingConfig	14:04
104	Homework -> Add data and model drift reports to each training run using Evidently	09:13
105	Dockerfile for the training-pipeline	14:34
106	What is Kustomize?	18:57
107	Job manifest for the training-pipeline	18:55
108	Debugging the Job (wout success so far)	13:22
109	Wrap up	01:26
110	Goals for today	04:23
111	Deploying the training pipeline as a CronJob	16:52
112	Adjust the deployment script to use kustomize build if there is a kustomization.yaml	05:55
113	Building the prediction generator - Part 1 - Bug fixing the name of the model in the registry	27:26
114	Building the prediction generator - Part 2 - Prediction handler	29:08
115	Building the prediction generator - Part 3 - Loading the model signature	22:49
116	Building the prediction generator - Part 4 - Saving predictions to RisingWave table	35:58
117	Building the prediction generator - Part 5 - Deploying the prediction generator	24:40
118	Wrap up	03:00
119	Goals for today	04:05
120	Fixing a bug in the prediction_handler	11:14
121	Question -> Where to log what?	03:16
122	Rebuild docker image and deploy prediction generator to Kubernetes	15:02
123	Let's build the prediction API in Rust - Part 1 - The tools you need	11:22
124	Let's build the prediction API in Rust - Part 2 - REST API skeleton	21:19
125	Let's build the prediction API in Rust - Part 3 - Unwrapping the unwrappable	16:21
126	Let's build the prediction API in Rust - Part 4 - Predictions endpoint	14:36
127	Let's build the prediction API in Rust - Part 5 - Connecting to PostgreSQL	21:37
128	Let's build the prediction API in Rust - Part 6 - Debugging	43:48
129	Let's build the prediction API in Rust - Part 7 - Squashing the bug	02:57
130	Wrap up	03:35
131	5/14/2025 11:02 AM CEST recording	01:21
132	5/14/2025 11:04 AM CEST recording	31:34
133	5/14/2025 11:35 AM CEST recording	05:22
134	5/14/2025 11:41 AM CEST recording	06:07
135	5/14/2025 11:48 AM CEST recording	15:02
136	5/14/2025 12:03 PM CEST recording	06:23
137	5/14/2025 12:10 PM CEST recording	08:10
138	5/14/2025 12:18 PM CEST recording	06:35
139	5/14/2025 12:34 PM CEST recording	35:50
140	5/14/2025 1:10 PM CEST recording	07:47
141	5/14/2025 1:18 PM CEST recording	15:00
142	5/14/2025 1:34 PM CEST recording	09:10
143	5/14/2025 1:43 PM CEST recording	09:03
144	5/14/2025 1:52 PM CEST recording	04:17
145	5/14/2025 1:57 PM CEST recording	01:42
146	Before we begin...	07:00
147	Goals for today	08:03
148	Questions	06:31
149	Adding the PgPool to the app State (so we don't need to recreate every time we get a request)	22:48
150	Creating a RisingWave materialized view with the latest predictions for each coin	16:14
151	Custom config objet to load and hold env variable values	17:36
152	Adding the Config to the app State	09:48
153	Adding some logging	11:41
154	Dockerizing our Prediction API Rust service	23:09
155	Mad scientist experiment to reduct the Docker image size with a scratch layer	07:04
156	Deploying to Kubernetes	27:15
157	Plan for the last 3 sessions	03:42
158	Wrap up	01:44
159	Goals for today	08:12
160	How to download crypto news from a REST API (Cryptopanic)	26:21
161	Load Cryptopanic API using env variables and pydantic-settings	07:12
162	Custom Quixstreams Stateful Source to ingest news into Kafka	27:10
163	Inspecting the news messages - Kafka UI	18:18
164	News sentiment service - iteration 1	21:56
165	Unpacking sentiment scores as N kafka messages	21:28
166	BAML to build LLMs with structured output (like the sentiment-extractor we want to build!)	26:55
167	Testing our BAML function	05:39
168	Wrap up	02:02
169	A question I forgot to answer!	01:09
170	6.4.2025 11:03 AM CEST recording	04:01
171	6.4.2025 11:08 AM CEST recording	31:30
172	6.4.2025 11:40 AM CEST recording	10:01
173	6.4.2025 11:50 AM CEST recording	33:31
174	6.4.2025 12:35 PM CEST recording	24:07
175	6.4.2025 1:00 PM CEST recording	04:38
176	6.4.2025 1:05 PM CEST recording	09:07
177	6.4.2025 1:14 PM CEST recording	27:14
178	6.4.2025 1:42 PM CEST recording	17:16
179	6.4.2025 1:59 PM CEST recording	04:20
180	Goals for today	05:22
181	Implement the evaluation metric	22:00
182	Manual prompt improvement	07:06
183	Automatic prompt optimization	11:21
184	How to use open-weights LLMs with Ollama	34:36
185	Kubernetes manifests to deploy news and news-sentiment services	30:16
186	MARIUS -> How to set up a GPU node in a production Kubernetes cluster	44:51
187	Time to say (see you later)!	03:29
188	Wrap up	04:36

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