コースの概要と深層学習の再現性危機

マイルストーンプロジェクト1に必要な複雑なマルチステージアーキテクチャへ移行する中で、単純かつ自己完結型のモデルから段階的に進化します。これにより、スプレッドシートやローカルファイルで重要なパラメータを手動で追跡することはもはや持続不可能になります。この複雑なワークフローは、開発の整合性に対して深刻なリスクをもたらします。

1. 再現性のボトルネックを特定する

深層学習のワークフローは、最適化アルゴリズム、データサブセット、正則化技術、環境の違いなど多数の変数が関与するため、本質的に高いばらつきを伴います。体系的な追跡がなければ、デバッグや展開済みモデルの改善に不可欠な過去の特定結果を再現することは、多くの場合不可能です。

何を追跡すべきか？

ハイパーパラメータ： All configuration settings must be recorded (e.g., Learning Rate, Batch Size, Optimizer choice, Activation function).

環境状態： Software dependencies, hardware used (GPU type, OS), and exact package versions must be fixed and recorded.

アーティファクトと結果： Pointers to the saved model weights, final metrics (Loss, Accuracy, F1 score), and training runtime must be stored.

The "Single Source of Truth" (SSOT)

Systematic experiment tracking establishes a central repository—a SSOT—where every choice made during model training is recorded automatically. This eliminates guesswork and ensures reliable auditability across all experimental runs.

TERMINALbash — tracking-env

> Ready. Click "Run Conceptual Trace" to see the workflow.

EXPERIMENT TRACE Live

Simulate the run to visualize the trace data captured.

Question 1

What is the root cause of the Deep Learning Reproducibility Crisis?

PyTorch's dependence on CUDA drivers.

The sheer number of untracked variables (code, data, hyperparameter, and environment).

The excessive memory usage of large models.

The computational cost of generating artifacts.

Question 2

In the context of MLOps, why is systematic experiment tracking essential for production?

It minimizes the total storage size of model artifacts.

It ensures that the model achieving the reported performance can be reliably reconstructed and deployed.

It speeds up the training phase of the model.

Question 3

Which element is necessary to reproduce a result but is most often forgotten in manual tracking?

The number of epochs run.

The specific versions of all Python libraries and the random seed used.

The name of the dataset used.

The time the training started.

Challenge: Tracking in Transition

Why the transition to formal tracking is non-negotiable.

You are managing 5 developers working on Milestone Project 1. Each developer reports their best model accuracy (88% to 91%) in Slack. No one can reliably tell you the exact combination of parameters or code used for the winning run.

Step 1

What immediate step must be implemented to halt the loss of critical information?

Solution:
Implement a mandatory requirement for every run to be registered with an automated tracking system before results are shared, capturing the full hyperparameter dictionary and Git hash.

Step 2

What benefit does structured tracking provide to the team that a shared spreadsheet cannot?

Solution:
Structured tracking allows automated comparison dashboards, visualizations of parameter importance, and centralized artifact storage, which is impossible with static spreadsheets.