The full cycle of a machine learning project involves for main steps.

  1. Scope the project
  2. Define and collect data
  3. Train and evaluate the model
  4. Deploy the model

Process Overview 

flowchart LR
    A[Scope project] --> B[Collect data]
    B --> C[Train model]
    C --> D[Deploy model]
    C -->|depending on diagnosis| B
    D -->|improve model| C
    D -->|generated more data| B

Scope Project

The first step in a machine learning project is to scope the project. This involves defining the problem to be solved, the data available, and the success criteria. The scope of the project will determine the approach to be taken and the resources required.

Collect Data

The next step is to define and collect the data. This involves identifying the data sources, collecting the data, and preprocessing the data. The data should be cleaned, transformed, and split into training, validation, and test sets.

Train Model

The next step is to train and evaluate the model. This involves selecting the model architecture, training the model, and evaluating the model performance. The model should be optimized to achieve the best performance on the validation set.

This step is iterative and may involve multiple rounde of training and evaluation.

Deploy Model

The final step is to deploy the model. This involves integrating the model into the production environment, monitoring the model performance, and updating the model as needed.

Depending on the needs or detected issues, the model may need to be improved, more data may need to be collected, or the model may need to be retrained.

Data Collection

Data collection is a critical step in a machine learning project. The quality and quantity of the data will determine the performance of the model.

There are multiple ways to get more data to improve the model.

More Data

Adding more data is the most common way to improve a machine learning model. If the model was diagnosed to be weak on some subset, adding more data to that subset can help improve the model.

Data Augmentation

Data augmentation is a technique to artificially increase the size of the training set. This can be done by applying transformations to the existing data, such as rotation, scaling, or flipping.

Data augmentation can help to improve the model by increasing the diversity of the training set. The transformations should represent real-world variations of the data.

Examples of data augmentation:

  • Image data: rotation, scaling, flipping
  • Audio data: pitch shifting, time stretching, adding noise
  • Text data: synonym replacement, word deletion, word insertion
  • Time series data: time shifting, time warping

Synthetic Data

Synthetic data is data that is generated artificially. This can be done by using generative models, such as GANs or VAEs, or it can be done manually.

Example for synthetic data:

  • Image data: generate new images using GANs, create screenshots of letters in different fonts for character recognition
  • Audio data: generate new audio samples using VAEs, create new audio samples by mixing existing samples
  • Text data: generate new text using GANs, create new text by combining existing text

Transfer Learning

Transfer learning is a technique to use a pre-trained model for a new task. This can be done by fine-tuning the pre-trained model on the new data or by using the pre-trained model as a feature extractor.