Career studio / Machine Learning
Build evidence for the Machine Learning Engineer role
A support classifier needs a repeatable training and evaluation workflow before anyone considers deployment. Design the data split, versioning, evaluation gate and monitoring plan using a local synthetic case.
Your outcome: Reproducible ML experiment specification and release-readiness dossier.
Start with the right expectations
Who this is for
Python, SQL, basic statistics and a completed small modeling project.
This path develops a demonstrable work sample for the named role. It does not replace employer-specific experience, professional qualifications or supervised practice.
Plan your study time
Use a flexible six-week project schedule: one week for each of the first four stages and two weeks for the portfolio and review. Allow roughly 3–5 hours per week for the case work, in addition to course study. This is a planning suggestion, not a measured course duration or a promise of job readiness.
Read the course access notes before starting. Free viewing or reading does not automatically include certificates, graded assessments, software licences or paid cloud resources. Core case work can be documented with a free editor and spreadsheet; optional implementation tools may have additional requirements.
Your course sequence
Start in the order below. Use the course outline to select the sections relevant to the stage tasks, and complete foundation exercises before moving on. Full provider courses can be substantially longer than this project schedule. These are course resources, not a claimed count of individual lessons.
Resource 01 / DataTalks.Club
Machine Learning Zoomcamp: Free Course
Purpose: Build the foundations. Connect your notes to the case tasks below rather than only collecting completion marks.
- Frame a machine learning problem, prepare data and establish a validation approach before selecting a model.
- Develop regression and classification examples and interpret the effect of features and model choices.
- Compare evaluation methods and tree-based or neural approaches in the context of the course projects.
Access: Free Video
Certificate: Free video viewing; certificate not included
Resource 02 / DataTalks.Club
MLOps Zoomcamp: Free Course
Purpose: Develop the role-specific method. Connect your notes to the case tasks below rather than only collecting completion marks.
- Connect experiment tracking, model management and a registry with reproducible machine learning work.
- Understand pipeline and deployment approaches presented for web, streaming and batch use cases.
- Use monitoring and data-quality concepts to reason about the behaviour of an operating model.
Access: Free Video
Certificate: Free video viewing; certificate not included
Resource 03 / Google
Machine Learning Crash Course
Purpose: Strengthen practical application. Connect your notes to the case tasks below rather than only collecting completion marks.
- Understand prediction models: explore regression, loss, gradient descent and classification.
- Evaluate classification results: consider thresholds and measures such as precision and recall.
- Prepare machine learning data: work through numerical and categorical data concepts.
Access: Public learning materials are free. Programming exercises use free Colab with a Google account; free computing resources have usage and availability limits.
Certificate: Free module badges after at least 80% on the relevant end-of-module quiz. Google does not offer formal certification for this course.
Learn, make, check
Keep a decision log as you work. For each stage, capture the assumption, your work product, a check and the change you made after that check. The acceptance criterion is the stopping condition for the stage.
Stage 1 / suggested project week 1
Define the prediction service
Understand: An ML system includes data, model and operating constraints.
Make: Specify input schema, output labels, prediction timing and fallback behavior.
Check before moving on: The fallback remains usable when confidence is low.
Study companion: Machine Learning Zoomcamp: Free Course. Use its relevant lessons and examples; the independent WikiFree task is not a provider assessment.
Save as evidence: a dated work product and a short note explaining one choice, one alternative and one remaining uncertainty.
Stage 2 / suggested project week 2
Track the experiment
Understand: Reproducibility needs data and configuration versions.
Make: Record dataset checksum, split rule, baseline, model settings and evaluation command.
Check before moving on: Two runs can be compared without relying on memory.
Study companion: MLOps Zoomcamp: Free Course. Use its relevant lessons and examples; the independent WikiFree task is not a provider assessment.
Save as evidence: a dated work product and a short note explaining one choice, one alternative and one remaining uncertainty.
Stage 3 / suggested project week 3
Set release gates
Understand: A deployment decision requires more than a higher aggregate score.
Make: Define minimum precision/recall, latency and subgroup review requirements.
Check before moving on: Thresholds are proposed for this case, not universal industry rules.
Study companion: Machine Learning Crash Course. Use its relevant lessons and examples; the independent WikiFree task is not a provider assessment.
Save as evidence: a dated work product and a short note explaining one choice, one alternative and one remaining uncertainty.
Stage 4 / suggested project week 4
Plan monitoring
Understand: Drift is a signal to investigate, not proof of model failure.
Make: Draft data-quality, latency and delayed-outcome monitoring with rollback ownership.
Check before moving on: Separate data drift, prediction drift and measured outcome degradation.
Study companion: Machine Learning Crash Course. Use its relevant lessons and examples; the independent WikiFree task is not a provider assessment.
Save as evidence: a dated work product and a short note explaining one choice, one alternative and one remaining uncertainty.
Stage 5 / suggested project weeks 5–6
Present your portfolio
Assemble the four work products into Reproducible ML experiment specification and release-readiness dossier. Write a README explaining the problem, your assumptions, the decisions you made, tests actually performed and what remains incomplete.
Give a five-minute walkthrough: one minute for the problem, two for the work and evidence, one for trade-offs and one for the next improvement. If you have not implemented a proposed system, label it a design or prototype rather than a deployed solution.
Acceptance: a reviewer can locate each work product, reproduce the key reasoning and identify the limits without a private explanation from you.
The portfolio brief
A support classifier needs a repeatable training and evaluation workflow before anyone considers deployment. Design the data split, versioning, evaluation gate and monitoring plan using a local synthetic case.
Deliver: Reproducible ML experiment specification and release-readiness dossier.
| model | accuracy | latency ms | limit ms |
|---|---|---|---|
| Baseline | 0.82 | 120 | 200 |
| Candidate | 0.85 | 240 | 200 |
Use these small inputs to make the reasoning visible. They are not a representative dataset, a real client engagement or evidence of production performance. Where the brief requires additional sample records, create them yourself, label them synthetic and explain why you added them.
The ZIP includes the case CSV, four stage worksheets, course sequence, decision log, test log, portfolio README template, review rubric and reference reasoning. It is a starter kit for your work, not a completed application, professional report or proprietary project file.
Check your reasoning
A new model improves accuracy but doubles response time beyond the stated limit. Is it automatically better?
Write your answer and assumptions before opening the reference.
Read the reference reasoning
No. Evaluate it against the complete acceptance criteria, including latency and relevant errors. Keep the baseline or revise the design with explicit approval.
Portfolio review rubric
| Criterion | Points | Evidence required |
|---|---|---|
| Problem and scope | 4 | A clear user, need, boundary and assumptions |
| Role-specific work | 8 | 2 points for each of the four stage acceptance checks |
| Reasoning and validation | 4 | Independent checks, actual results and uncertainty |
| Communication and reproducibility | 4 | A usable README, source links and reviewable work products |
Aim for at least 16/20 as a self-review target, with no missing stage acceptance check. This is a WikiFree practice rubric, not an industry credential or a hiring assessment.
Interview practice
What evidence would make you roll back a model release?
Answer using the case: context, your action, evidence and limitation. Describe this honestly as a self-directed project. Do not present fictional organizations, invented interviews or practice results as paid work experience.
Your progress
Use this checklist as a personal study aid. Progress is stored in this browser when local storage is available; it is not an account record or a certificate.
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Questions before you start
Will this path qualify me for a job?
It helps you study relevant foundations and produce a work sample. Employers may require additional skills, qualifications and experience. Review actual role requirements before applying; no job or certification is guaranteed.
Are all tools and certificates free?
The linked learning routes were selected for free viewing or reading. Follow the separate access and certificate notes on each course. Paid tools and cloud deployment are optional; document a local design when implementation resources are unavailable.
Can I follow this on a phone?
You can read the path and many learning resources on a phone. Coding, detailed spreadsheet work, design tools and mobile-app development are easier or require a suitable computer. The page is designed to reflow; that is not a claim that every physical device has been tested.
Questions & discussion
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