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Artificial Intelligence Career Checklists

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Artificial Intelligence Career Checklists (Linux + Bash Edition)

AI careers can feel like a maze of buzzwords, repos, and rabbit holes. The fastest way through? A checklist you can run from your terminal. This post gives you a practical, Bash-first set of checklists to go from “curious” to “shipping models,” with copy-pasteable commands for Debian/Ubuntu (apt), Fedora/RHEL (dnf), and openSUSE (zypper).

You’ll:

  • Install a minimal, professional AI dev stack on Linux

  • Bootstrap a reproducible project in minutes

  • Train and evaluate a model from the command line

  • Leave with a clear, repeatable path forward

Why this works:

  • Checklists reduce cognitive load and context switching

  • Bash makes your work reproducible and automatable

  • Hiring managers love clean repos, scripts, and logs they can run

Checklist 1 — System-Ready: Install a Minimal AI Dev Stack

These are your day-one tools: Git for version control, Python for ML, build tools for native wheels, and a container runtime (Podman) for reproducibility.

  • Debian/Ubuntu (apt):
sudo apt update
sudo apt install -y \
  git git-lfs \
  python3 python3-pip python3-venv \
  build-essential python3-dev \
  jq curl wget \
  cmake make gcc g++ \
  podman \
  tmux graphviz
  • Fedora/RHEL (dnf):
sudo dnf makecache
sudo dnf install -y \
  git git-lfs \
  python3 python3-pip \
  gcc gcc-c++ make cmake \
  python3-devel \
  jq curl wget \
  podman \
  tmux graphviz
  • openSUSE (zypper):
sudo zypper refresh
sudo zypper install -y \
  git git-lfs \
  python3 python3-pip \
  gcc gcc-c++ make cmake \
  python3-devel \
  jq curl wget \
  podman \
  tmux graphviz

Then initialize Git LFS:

git lfs install

Tip:

  • Use Podman for rootless containers on all major distros.

  • If you have an NVIDIA GPU, install the proprietary driver from your vendor and follow PyTorch/TensorFlow’s official GPU instructions. Start CPU-first to keep things simple.

Checklist 2 — Environment & Frameworks: Create a Clean, Reusable Setup

Use Python’s built-in venv to isolate dependencies per project.

Create and activate a virtual environment:

python3 -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip wheel

Install core data science packages:

pip install numpy pandas scikit-learn matplotlib seaborn jupyterlab

Install PyTorch (CPU-only to start; it’s stable across machines):

pip install --index-url https://download.pytorch.org/whl/cpu torch torchvision torchaudio

Sanity check your environment:

python - <<'PY'
import sys, sklearn, torch, pandas as pd
print("Python:", sys.version.split()[0])
print("pandas:", pd.__version__)
print("sklearn:", sklearn.__version__)
print("torch:", torch.__version__, "| CUDA available:", torch.cuda.is_available())
PY

If you get “CUDA available: False” but have a GPU, don’t panic—CPU is fine for learning and interviews. Switch to GPU wheels later when you need speed.

Checklist 3 — Reproducibility: Bootstrap a Project That Looks Professional

This 1-minute scaffold gives you a clean repo with a Makefile, pinned deps, and a first training script.

Create a new directory and run:

mkdir -p ai-starter && cd ai-starter
git init
git lfs install
python3 -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip wheel
pip install numpy pandas scikit-learn matplotlib seaborn jupyterlab

Add a .gitignore:

cat > .gitignore <<'EOF'
.venv/
__pycache__/
.ipynb_checkpoints/
*.pyc
data/
models/
outputs/
.DS_Store
EOF

Add a simple Makefile with helpful targets:

cat > Makefile <<'EOF'
VENV=. .venv/bin/activate &&
PY=$(VENV) python

.PHONY: help venv deps train eval clean
help:
    @echo "Targets: venv, deps, train, eval, clean"

venv:
    python3 -m venv .venv
    $(VENV) python -m pip install --upgrade pip wheel

deps:
    $(PY) -m pip install numpy pandas scikit-learn matplotlib seaborn

train:
    $(PY) src/train.py --out models/model.pkl --seed 42

eval:
    $(PY) src/eval.py --model models/model.pkl --report outputs/report.txt

clean:
    rm -rf models/ outputs/
EOF

Create minimal training and eval scripts:

mkdir -p src models outputs
  • src/train.py
#!/usr/bin/env python
import argparse, os, joblib
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import numpy as np
import random

def set_seed(seed: int):
    np.random.seed(seed)
    random.seed(seed)

def main(out, seed):
    set_seed(seed)
    X, y = load_iris(return_X_y=True, as_frame=False)
    Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, random_state=seed, stratify=y)
    pipe = Pipeline([
        ("scaler", StandardScaler()),
        ("clf", LogisticRegression(max_iter=1000))
    ])
    pipe.fit(Xtr, ytr)
    os.makedirs(os.path.dirname(out), exist_ok=True)
    joblib.dump(pipe, out)
    acc = pipe.score(Xte, yte)
    print(f"Saved model to {out}; test accuracy={acc:.3f}")

if __name__ == "__main__":
    p = argparse.ArgumentParser()
    p.add_argument("--out", default="models/model.pkl")
    p.add_argument("--seed", type=int, default=42)
    args = p.parse_args()
    main(args.out, args.seed)
  • src/eval.py
#!/usr/bin/env python
import argparse, os, joblib
from sklearn.datasets import load_iris
from sklearn.metrics import classification_report

if __name__ == "__main__":
    p = argparse.ArgumentParser()
    p.add_argument("--model", default="models/model.pkl")
    p.add_argument("--report", default="outputs/report.txt")
    args = p.parse_args()

    model = joblib.load(args.model)
    X, y = load_iris(return_X_y=True, as_frame=False)
    yhat = model.predict(X)
    rep = classification_report(y, yhat)
    os.makedirs(os.path.dirname(args.report), exist_ok=True)
    with open(args.report, "w") as f:
        f.write(rep)
    print("Wrote", args.report)

Run the whole pipeline:

make venv deps train eval

Commit your work:

git add .
git commit -m "Initial AI project scaffold with training and eval"

Result: a runnable repo you can share with interviewers or use as a base for real projects.

Checklist 4 — “Career Signals”: Ship, Measure, Automate

Hiring managers look for consistent, reproducible outputs. Make these habits visible:

  • Automate runs

    • Use Make targets (make train eval) so others can reproduce results without guessing.
    • Add a SEED variable to Makefile to show determinism.

  • Track artifacts

    • Save models to models/, reports to outputs/, and dataset snapshots to data/.
    • Use Git LFS for large files: git lfs track "models/*" "data/*"

  • Document quickly

    • Add a short README with how to run:
cat > README.md <<'EOF'
# AI Starter
Reproducible Iris classifier demo.

Quick start:

python3 -m venv .venv . .venv/bin/activate pip install -r requirements.txt # or: make venv deps make train eval

Artifacts: models/model.pkl, outputs/report.txt
EOF
  • Containerize when needed (Podman)
    • Build and run reproducibly without polluting your system:
cat > Containerfile <<'EOF'
FROM python:3.11-slim
WORKDIR /app
COPY . /app
RUN pip install --no-cache-dir numpy pandas scikit-learn matplotlib seaborn joblib
CMD ["bash", "-lc", "python src/train.py --out models/model.pkl && python src/eval.py --model models/model.pkl --report outputs/report.txt"]
EOF

podman build -t ai-starter:latest .
podman run --rm -v $PWD/models:/app/models -v $PWD/outputs:/app/outputs ai-starter:latest

These practices demonstrate engineering maturity, not just modeling skills.

Real-World Example: One-Liner Career Sprint

  • New role? New machine? New project?

  • Run this once and start modeling in under 10 minutes.

For Debian/Ubuntu:

sudo apt update && sudo apt install -y git git-lfs python3 python3-pip python3-venv build-essential python3-dev jq curl wget cmake make gcc g++ podman tmux graphviz
git lfs install

For Fedora/RHEL:

sudo dnf makecache && sudo dnf install -y git git-lfs python3 python3-pip gcc gcc-c++ make cmake python3-devel jq curl wget podman tmux graphviz
git lfs install

For openSUSE:

sudo zypper refresh && sudo zypper install -y git git-lfs python3 python3-pip gcc gcc-c++ make cmake python3-devel jq curl wget podman tmux graphviz
git lfs install

Then:

git clone https://github.com/your-username/ai-starter.git
cd ai-starter
python3 -m venv .venv && . .venv/bin/activate
pip install --upgrade pip wheel
make deps train eval

Conclusion / Call To Action

A good AI career is 10% algorithms and 90% reproducible engineering. You now have:

  • A cross-distro Linux checklist for AI work

  • A ready-to-run project scaffold

  • Commands to train, evaluate, and containerize

Your next steps: 1) Save this post, and turn the checklists into scripts in your dotfiles. 2) Fork the scaffold, push to GitHub, and add one real dataset. 3) Schedule a weekly “shipping hour” to produce a small, documented result.

If you want a follow-up, ask for:

  • A GPU-accelerated variant of this setup

  • A Bash script that provisions a cloud VM and deploys this project

  • A CI workflow (GitHub Actions) to run make train eval on every push

Keep it simple. Keep it scripted. Keep shipping.

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