MLOps Zoomcamp 2025

Homework 3: Training Pipelines Statistics

Distribution of scores and reported study time for this homework.

Submissions

321

Median total score

6

Average total score

6

Score distribution

All values are points.

Questions score

Min
2
Median
6.0
Max
6
Q1
6.0
Avg
5.6
Q3
6.0

Learning in public score

Min
-
Median
0.0
Max
7
Q1
0.0
Avg
0.7
Q3
0.0

Total score

Min
2
Median
6.0
Max
14
Q1
6.0
Avg
6.4
Q3
6.0

Time distribution

All values are hours reported by students.

Lectures

Min
0.0
Median
3.0
Max
72.0
Q1
2.0
Avg
5.7
Q3
6.0

Homework

Min
1.0
Median
4.0
Max
100.0
Q1
2.0
Avg
5.8
Q3
6.0

Question breakdown

Correctness and answer distribution per question.

1. Tool you use

321 / 322 correct (99.7%)

Answer Count
Prefect 85
Mage 65
Airflow 45
prefect 27
Apache Airflow 19
airflow 11
mage 10
Kestra 5
Dagster 4
mage-ai 4
mlflow 4
zenml 2
MAGE 2
MLflow 2
Mage.ai 2
Mage AI 2
Mage (mage-ai) 1
Mage Ai in Docker 1
0.9.76 1
mage.ai 1
AirFlow 1
Kubeflow 1
Mage Orchestrator 1
Mage. 1
ZenML 1
Pefect 1
Apachi Nifi 1
Kestra (Q1 -> Q5) / Prefect (Q6) 1
AIRFLOW 1
astro cli (airflow) 1
Airflow (with docker) 1
1 1
Apache airflow 1
mlflow + docker 1
Databricks CLI for Asset Bundles and Jobs 1
dagster 1
ariflow 1
temporal 1
kestra 1
Mage AI, MLFlow 1
MLFlow 1
mage ai 1
Aparche Airflow 1
Mlflow 1
API Python MLflow SQLite DictVectorizer 1
Jupyter Notebook 1
Airlfow 1
conda airflow 1
MageAI 1

2. Version

321 / 322 correct (99.7%)

Answer Count
3.0.1 45
3.4.4 45
3.4.5 38
0.9.76 33
v0.9.76 14
v0.9.73 13
3.4.3 10
2.9.1 9
0.9.73 9
2.10.5 4
2.22.0 4
3.4.1 3
0.22.9 3
2.9.0 3
0.83.0 3
2.8.1 3
2.12.1 3
3.4.6 3
3.0.0 3
1.10.19 3
2.10.21 2
0.9.70 2
0.22.12 2
0.9.71 2
2.7.1 2
2.14.10 1
3.4.0 1
1.10.17 1
airflow:3.0.0 1
v.0.9.76 1
2.20.19 (pip install "prefect<3.0.0") 1
55 1
3.2.14 1
2.20.18 1
v0.9.71 1
v0.9.70 1
3.0 1
V.0.9.73 1
0.9.72 1
Version: 0.9.76 1
2.13.0 1
2.7.3 1
v2.9.1 1
version 0.9.76 1
2.8.3 1
0.9.27 1
API version: 0.8.4 Python version: 3.10.16 MLflow: 2.x Database: SQLite Vectorizer: DictVectorizer 1
2.4.0 1
3.0.2 1
Kestra v0.20.7 / Prefect 3.4.4 1
1.10.18 1
1.34.0 1
Mage - v0.9.73 1
2.4.2 1
3.44 1
Mage Version: v0.9.76 1
1 1
v0.9.68 1
v2.7.3 1
v0.254.0 1
Version:3.4.4 API version: 0.8.4 Python version:3.10.16 MLflow: 2.x Database:SQLite Vectorizer:DictVectorizer (Scikit-learn) 1
latest (V. 0.22.0) 1
2.9.3 1
mlflow 2.22.90 1
2.20.19 1
2.8.2 1
1.27.2 1
'3.0.1' 1
0.9.32 1
0.9.63 1
v0.9.x 1
2.9 1
0.9.51 1
v0.20.7 1
2.13.4 1
2.10.8 1
Prefect version: 3.4.5 1
2.22.1 1
v 3.4.5 1
2.8 1
0.9.15 1
3.1.0 1
0.9.68 1
8.6.3 1
0.9.0 1
mage-ai -> 0.9.76 1

3. Creating a pipeline

306 / 322 correct (95.0%)

1 3,003,766 4 (1.2%)
2 3,203,766 7 (2.2%)
3 3,403,766 308 (95.7%)
4 3,603,766 2 (0.6%)

4. Data preparation

308 / 322 correct (95.7%)

1 2,903,766 2 (0.6%)
2 3,103,766 8 (2.5%)
3 3,316,216 309 (96.0%)
4 3,503,766 1 (0.3%)

5. Train a model

284 / 322 correct (88.2%)

1 21.77 19 (5.9%)
2 24.77 285 (88.5%)
3 27.77 12 (3.7%)
4 31.77 0 (0.0%)

6. MLFlow

257 / 322 correct (79.8%)

1 14,534 24 (7.5%)
2 9,534 28 (8.7%)
3 4,534 258 (80.1%)
4 1,534 2 (0.6%)

Calculated: 16 June 2025, 11:29