Course 6 of 12

Data Pipelines for ML

Feature stores, Airflow, Data validation, and more.

Yiga AI

Intermediate en

Intermediate · Course · 3 weeks at 5 hrs/week

Duration: 3 weeks
Effort: 5 hrs/week
Lessons: 16
Hands-on exercises: 16
Projects: 1
Language: en

What you’ll learn

Skills you can put to work.

Machine Learning Foundations

Deep Learning with PyTorch

Computer Vision

NLP with Transformers

Syllabus

Every module, step by step.

4 modules · 21 lessons · 14 hours · 1 graded project

840 minutes of guided lesson time.

Module 1 · 5 lessons · 3 hoursFeature storesModule 1 of 4 in Data Pipelines for ML.

Overview and set-up
Overview and set-up — Feature stores in Data Pipelines for ML.
Lesson
40 min
Core concepts
Core concepts — Feature stores in Data Pipelines for ML.
Lesson · Locked
40 min
Worked example
Worked example — Feature stores in Data Pipelines for ML.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Feature stores in Data Pipelines for ML.
Lesson · Locked
40 min
Module quiz: Feature stores
Check what you learned in Feature stores before moving on.
Quiz · Locked
20 min

Module 2 · 5 lessons · 3 hours LockedAirflowModule 2 of 4 in Data Pipelines for ML.

Overview and set-up
Overview and set-up — Airflow in Data Pipelines for ML.
Lesson · Locked
40 min
Core concepts
Core concepts — Airflow in Data Pipelines for ML.
Lesson · Locked
40 min
Worked example
Worked example — Airflow in Data Pipelines for ML.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Airflow in Data Pipelines for ML.
Lesson · Locked
40 min
Module quiz: Airflow
Check what you learned in Airflow before moving on.
Quiz · Locked
20 min

Module 3 · 5 lessons · 3 hours LockedData validationModule 3 of 4 in Data Pipelines for ML.

Overview and set-up
Overview and set-up — Data validation in Data Pipelines for ML.
Lesson · Locked
40 min
Core concepts
Core concepts — Data validation in Data Pipelines for ML.
Lesson · Locked
40 min
Worked example
Worked example — Data validation in Data Pipelines for ML.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Data validation in Data Pipelines for ML.
Lesson · Locked
40 min
Module quiz: Data validation
Check what you learned in Data validation before moving on.
Quiz · Locked
20 min

Module 4 · 6 lessons · 5 hours LockedSchedulingModule 4 of 4 in Data Pipelines for ML.

Overview and set-up
Overview and set-up — Scheduling in Data Pipelines for ML.
Lesson · Locked
40 min
Core concepts
Core concepts — Scheduling in Data Pipelines for ML.
Lesson · Locked
40 min
Worked example
Worked example — Scheduling in Data Pipelines for ML.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Scheduling in Data Pipelines for ML.
Lesson · Locked
40 min
Module quiz: Scheduling
Check what you learned in Scheduling before moving on.
Quiz · Locked
20 min
Graded project: Data Pipelines for ML
Apply everything from Data Pipelines for ML in one graded, portfolio-ready build.
Graded project · Locked
120 min

Applied learning project

Make the learning visible.

Use Graded project: Data Pipelines for ML to apply this course to a practical challenge. You’ll finish with a clear solution and the reasoning behind your choices.

Apply the course’s core skillMake and explain practical decisionsCreate evidence you can share

Your instructor

Mariam K.

Software & cloud

Helps new developers build reliable products through clear explanations and guided practice.

“I use what I learned in my work now, not in some distant future project.”
Diana Nsiiza
Data analyst, Entebbe

Continue with

Your next best step.

Following the order of Machine Learning Engineer.

See the full chain

Next lesson

Overview and set-up

Data Pipelines for ML

40 min · Resume

Course 7 in the track

Edge & Mobile ML

TensorFlow Lite, Quantisation, On-device inference, and more.

Advanced · Course · 3 weeks at 5 hrs/week

Course 8 in the track

Machine Learning Foundations

Linear/logistic regression, Gradient descent, Regularisation, and more.

Intermediate · Course · 4 weeks at 5 hrs/week

Course 9 in the track

Project: Swahili Speech-to-Text Model

Dataset, Fine-tune Whisper, Evaluate, and more.

Advanced · Course · 4 weeks at 5 hrs/week

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