MLOps Zoomcamp 2025

Homework 4: Deployment Statistics

Distribution of scores and reported study time for this homework.

Submissions

241

Median total score

6

Average total score

6

Score distribution

All values are points.

Questions score

Min
2
Median
6.0
Max
6
Q1
5.0
Avg
5.5
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
5.0
Avg
6.2
Q3
6.0

Time distribution

All values are hours reported by students.

Lectures

Min
0.0
Median
4.0
Max
48.0
Q1
2.3
Avg
5.3
Q3
6.0

Homework

Min
0.5
Median
3.0
Max
48.0
Q1
2.0
Avg
4.4
Q3
5.0

Question breakdown

Correctness and answer distribution per question.

1. Notebook

231 / 241 correct (95.9%)

1 1.24 1 (0.4%)
2 6.24 231 (95.9%)
3 12.28 7 (2.9%)
4 18.28 1 (0.4%)

2. Preparing the output

221 / 241 correct (91.7%)

1 36M 4 (1.7%)
2 46M 4 (1.7%)
3 56M 11 (4.6%)
4 66M 221 (91.7%)

3. Creating the scoring script

241 / 241 correct (100.0%)

Answer Count
jupyter nbconvert --to script starter.ipynb 103
jupyter nbconvert --to script score.ipynb 10
jupyter nbconvert --to=script starter.ipynb 5
jupyter nbconvert --to script homework.ipynb 5
!jupyter nbconvert --to script starter.ipynb 5
jupyter nbconvert --to script notebook.ipynb 4
jupyter nbconvert --to python starter.ipynb 4
nbconvert 4
jupyter nbconvert 4
jupyter nbconvert starter.ipynb --to script 3
jupyter nbconvert --to script 3
jupyter nbconvert --to script homework/starter.ipynb 2
!jupyter nbconvert --to script homework04.ipynb --output scoring 2
jupyter nbconvert starter.ipynb --to python 2
jupyter nbconvert --to script starter.ipynb --output score 2
!jupyter nbconvert --to script homework.ipynb --output homework 2
jupyter nbconvert --to script hw4.ipynb 2
jupyter nbconvert --to script scoring.ipynb 2
jupyter nbconvert --to script HW4.ipynb 2
jupyter nbconvert Homework.ipynb --to python 2
See: starter.py via "Homework URL" below 1
jupyter nbconvert --to script homework4.ipynb 1
jupyter nbconvert --to script starter.ipynb --output batch_scoring 1
jupyter nbconvert --to script pipeline.ipynb 1
$ jupyter nbconvert --to script starter.ipynb homework.py 1
jupyter nbconvert --to script notebook_name.ipynb OR jupyter nbconvert --to python notebook_name.ipynb 1
!jupyter nbconvert --to=script starter.ipynb 1
python score.py yellow 2023 3 1
pyarrow 1
jupyter nbconvert --to script 04-deployment.ipynb 1
jupyter nbconvert --to script 04_notebook.ipynb 1
jupyter nbconvert --to home4.ipynb 1
jupyter nbconvert --to script ./starter.ipynb 1
Jupyter nbconvert –to script homework.ipynb 1
jupyter nbconvert --to script starter.ipynb --output starter.py 1
!jupyter nbconvert homework4.ipynb --to script 1
jupyter nbconvert --to script <file_name>.ipynb 1
python score.py 1
jupyter nbconvert --to script starter.py 1
jupyter nbconvert --to=script starter.ipynd 1
python scoring.py 2023 3 1
!jupyter nbconvert --to script solution.ipynb 1
jupyter nbconvert --to python batch.ipynb 1
jupyter nbconvert --to script Untitled1.ipynb 1
jupyter nbconvert --to script h4_answers.ipynb 1
!jupyter nbconvert homework-week4.ipynb --to script 1
jupyter nbconvert --to script starter.ipynb --no-prompt 1
jupyter nbconvert --to python 1
python predict.py 1
jupyter nbconvert --to=script your_notebook.ipynb 1
jupyter nbconvert ./homework-4.ipynb --to script 1
nbdev_export homework.ipynb 1
jupyter nbconvert ./homework_4.ipynb --to script 1
jupyter nbconvert --to script filename.ipynb 1
jupyter nbcovert --to script scrore.ipynb 1
jupyter nbconvert --to python starter.ipynb --output main.py 1
jupyter nbconvert --to python homework.ipynb 1
jupyter nbconvert --to script homework.ipynb --output=score 1
jupyter nbconvert --to script <filename_to_convert> 1
!jupyter nbconvert --to script starter(1).ipynb 1
3. jupyter nbconvert --to script starter.ipynb 1
jupyter nbconvert --to script 04.ipynb --TemplateExporter.exclude_markdown=True 1
jupyter nbconvert --to python notebook.ipynb 1
jyputer nbconvert --to script <file_name> 1
jupyter nbconvert scoring.ipynb --to python 1
jupyter nbconvert --to python <ipynb-file> 1
jupyter nbconvert --to script predict.py 1
jupyter nbconvert --to script 04-deployment/starter.ipynb 1
jupyter nbconvert starter.ipynb --to=python 1
!jupyter nbconvert --to script homework_week_4.ipynb 1
jupyter nbconvert --to=script .\starter.ipynb 1
jupyter nbconvert --to script 04-deployment/homework.ipynb 1
jupyter nbconvert --to script hw_4.ipynb 1
jupyter nbconvert --to script {hw_answer_04}.ipynb 1
the required command is : jupyter nbconvert --to script .ipynb where, in this case, file_name is Homework.ipynb 1
python -m jupyter nbconvert --to=script starter.ipynb 1
python -m batch.py 1
jupyter nbconvert --to script your_notebook.ipynb 1
jupyter nbconvert --to script <file.ipynb> 1
python scoringScript.py yellow 2023 3 1
jupyter nbconvert --to script ride_duration_prediction.ipynb 1
jupyter nbconvert starter.ipnyp --to=python 1
jupyter nbconvert --to script deployment.ipynb 1
`jupyter nbconvert --to script starter.ipynb` 1
jupyter nbconvert --to script ./description_homework/starter.ipynb --output ../trans_from_starter_ipynb 1
python score.py --year 2023 --month 3 1
jupyter nbconvert --to script starter_notebook.ipynb 1
jupyter nbconvert --to script name.ipynb 1
"jupyter nbconvert --to script starter.ipynb" 1

4. Virtual environment. Hash for Scikit-Learn

241 / 241 correct (100.0%)

Answer Count
sha256:057b991ac64b3e75c9c04b5f9395eaf19a6179244c089afdebaad98264bff37c 66
sha256:0650e730afb87402baa88afbf31c07b84c98272622aaba002559b614600ca691 33
057b991ac64b3e75c9c04b5f9395eaf19a6179244c089afdebaad98264bff37c 25
"sha256:057b991ac64b3e75c9c04b5f9395eaf19a6179244c089afdebaad98264bff37c" 17
sha256:014e07a23fe02e65f9392898143c542a50b6001dbe89cb867e19688e468d049b 10
0650e730afb87402baa88afbf31c07b84c98272622aaba002559b614600ca691 9
sha256:08ef968f6b72033c16c479c966bf37ccd49b06ea91b765e1cc27afefe723920b 9
"sha256:08ef968f6b72033c16c479c966bf37ccd49b06ea91b765e1cc27afefe723920b" 5
"sha256:0650e730afb87402baa88afbf31c07b84c98272622aaba002559b614600ca691" 4
sha256:1d0b25d9c651fd050555aadd57431b53d4cf664e749069da77f3d52c5ad14b3b 3
sha256:5b8780a8407c1b2ad441a13054f33ed0a0a58df4a69ddada2e30321d287e4f87 2
sha256:0402638c9a7c219ee52c94cbebc8fcb5eb9fe9c773717965c1f4185588ad3107 2
014e07a23fe02e65f9392898143c542a50b6001dbe89cb867e19688e468d049b 2
sha256:0828673c5b520e879f2af6a9e99eee0eefea69a2188be1ca68a6121b809055c1 2
sha256:065e9673e24e0dc5113e2dd2b4ca30c9d8aa2fa90f4c0597241c93b63130d233 2
sha256:065e9673b24e0dc5113e2dd2b4ca30c9d8aa2fa90f4c0597241c93b63130d233 1
f8c17d4267c3dc8bd4e43b8fcd3d802d 1
sha256:057b991ac64b3e75c9c04b5f9395eaf19a6179244f9cd494e748d2773d8065d0 1
"sha256:014e07a23fe02e65f9392898143c542a50b6001dbe89cb867e19688e468d049b", 1
"scikit-learn": { "hashes": [ "sha256:0650e730afb87402baa88afbf31c07b84c98272622aaba002559b614600ca691", "sha256:0c8d036eb937dbb568c6242fa598d551d88fb4399c0344d95c001980ec1c7d36", "sha256:1061b7c028a8663fb9a1a1baf9317b64a257fcb036dae5c8752b2abef31d136f", "sha256:25fc636bdaf1cc2f4a124a116312d837148b5e10872147bdaf4887926b8c03d8", 1
sha256:038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff 1
77be960475a8855d65c32215eea35ff0a2a096221433ca80f6f2cd75b1e77972 1
sha256:0e8102d5036e28d08ab47166b48c8d5e5810704daecf3a476a4282d562be9a28 1
789e3db01c750ed6d496fa2db7d50637857b451e57bcae863bff707c1247bef7 1
'sha256:057b991ac64b3e75c9c04b5f9395eaf19a6179244c089afdebaad98264bff37c' 1
494d5b4f482f0ef471f49afe28f00ec1a2ff75da2ce65060d8cabaeb3da2f100 1
hash = "sha256:789e3db01c750ed6d496fa2db7d50637857b451e57bcae863bff707c1247bef7" 1
this hash: sha256:0650e730afb87402baa88afbf31c07b84c98272622aaba002559b614600ca691 1
702ad05de9bc9de99a4807c8dde1686f31e0041d7b5f6f6b74861195a52110f5 1
08ef968f6b72033c16c479c966bf37ccd49b06ea91b765e1cc27afefe723920b 1
4. "sha256:057b991ac64b3e75c9c04b5f9395eaf19a6179244c089afdebaad98264bff37c" 1
"sha256:0828673c5b520e879f2af6a9e99eee0eefea69a2188be1ca68a6121b809055c1" 1
c01e869b15aec88e2cdb73d27f15bdbe03bce8e2fb43afbe77c45d399e73a5a3 1
dab1d738ce06576e30c23a7cfb3095e5e58cd1557282d4d3ba52cf96a9a1d753 1
"sha256:014e07a23fe02e65f9392898143c542a50b6001dbe89cb867e19688e468d049b" 1
"sha256:3c8c2ca06c3d0ec3452e8d6a367f903c0b46a144d2bb5ad4ee323ec370821f38" 1
hash = "sha256:b4fc2525eca2c69a59260f583c56a7557c6ccdf8deafdba6e060f94c1c59738e" 1
sha256:73e6c5a 1
sha256:0834e4cec2a2e0d8978f39cb8fe1cad3be6c27a47927e1774bf5737ea65ec228 1
sha256:017367484ce5498445aade74b1d5ab377acdc65e27095155e448c88497755a5d 1
"1b94d6440603752b27842eda97f6395f570941857456c606eb1d638efdb38184" 1
"505aef8e3312efc1daaf6cf3888476c343cdfa4bc82bea007d1b06a72db09856" 1
043ebc70 1
8ce37793ba506d53b53af0cea4db7b01fe47057cb740309f9de14e944281d7ad 1
0402638c9a7c219ee52c94cbebc8fcb5eb9fe9c773717965c1f4185588ad3107 1
7cb81758eee11d05ff00b78a568cd4183ca369 1
daa1c471d95bad080c6e44b4946c9390a4842adc3082572c20e4f8884e39e959 1
"2781451e8c683e398151f1d8c3d80084214c5cb95e0d9a8227dc69bc2f3c2ffe" 1
sha256:76bfb30e91e24dbb8ad2731ad108be09cb9fc9db60bba0b5d918a92a5e650bfa 1

5. Parametrize the script

225 / 241 correct (93.4%)

1 7.29 3 (1.2%)
2 14.29 225 (93.4%)
3 21.29 6 (2.5%)
4 28.29 2 (0.8%)

6. Docker container

159 / 241 correct (66.0%)

1 0.19 159 (66.0%)
2 7.24 11 (4.6%)
3 14.24 56 (23.2%)
4 21.19 6 (2.5%)

Calculated: 25 June 2025, 07:16