Machine Learning Workflow | Process, Steps, and Examples

The journey from raw data to actionable insights is often complex and multifaceted. Without a well-defined and structured machine learning workflow, data scientists may grapple with inefficiencies, inconsistencies, and missed opportunities throughout the project. This is where the importance of a machine-learning workflow comes into play. A well-designed ML workflow not only streamlines the entire project lifecycle but also ensures the reproducibility and scalability of the built models.

Having said this, it is clear that behind every successful machine learning project lies a systematic and well-structured machine learning workflow. So, check out this guide to understand a machine learning workflow and gain valuable insights into applying it effectively in your next project. From data collection and preprocessing to model selection, training, and evaluation, we'll underline each phase of the machine learning workflow. So, let's get started! 

What is a Machine Learning Workflow?

A machine learning workflow is a systematic and structured approach data scientists follow to develop, deploy, and maintain machine learning models effectively. It consists of interconnected steps, each serving a specific purpose in the data science pipeline. It follows a structured approach to harnessing data to make predictions or decisions without explicit programming instructions.

Machine Learning Workflow Diagram 

Source: Google Cloud 

This diagram illustrates the stages of a machine-learning workflow. Each stage is outlined, and where applicable, the managed services and APIs provided by AI Platform are indicated in blue boxes. The workflow is depicted as iterative, emphasizing the potential need to revisit and reevaluate previous steps. 

What are the Steps of a Machine Learning Workflow? 

The machine learning workflow provides a structured approach to developing, deploying, and managing machine learning models in production environments. Here's a brief description of each step in the machine-learning workflow:- 

Step 1: Source and Prepare Your Data

This first step involves collecting relevant data from various sources and preparing it for analysis. This includes data collection, cleaning, and preprocessing to ensure the data is structured and suitable for training a machine learning model. 

Step 2: Develop Your Model

This phase focuses on designing the architecture of the machine learning model. This involves selecting appropriate algorithms, defining features, and designing the model's overall structure to address the problem at hand. 

Step 3: Train an ML Model on Your Data

Once the model architecture is defined, it must be trained using the prepared data. This stage involves feeding the training data into the model and adjusting the model parameters iteratively to minimize the error and improve performance. Additionally, this step includes evaluating the model's accuracy and tuning hyperparameters to optimize its performance further.

Step 4: Deploy Your Trained Model

Once your model is trained and performing well, it can be deployed into a real-world environment. This means setting it up to handle predictions on new data. You'll need to create the infrastructure to host your model and make it accessible through APIs so that it can be used to make predictions. 

Step 5: Send Prediction Requests to Your Model

Once deployed, the model can receive prediction requests in real-time (online prediction) or in batches (batch prediction). Online prediction involves sending individual data points to the model for immediate inference, while batch prediction involves processing extensive data offline.

Step 6: Monitor Predictions on an Ongoing Basis

After deployment, it's essential to monitor the model's predictions continuously. This involves tracking key performance metrics, detecting anomalies, and ensuring the model's predictions remain accurate.

Step 7: Manage Your Models and Model Versions

The final step is managing your model, and effectively managing the different versions of the model is crucial. This includes versioning the models, tracking changes, and managing dependencies to ensure reproducibility and maintainability. 

Machine Learning Workflow Examples 

Machine learning has a diverse range of applications across various domains. For instance, in healthcare, machine learning is employed for predictive analytics to forecast patient diagnoses or treatment outcomes. This involves data preprocessing to clean and standardize medical records, followed by model selection and training using algorithms like decision trees or neural networks. Another example is e-commerce, where recommendation systems utilize collaborative filtering techniques to suggest products based on user behavior and preferences, requiring data collection, feature engineering, and model evaluation. Similarly, fraud detection systems in finance leverage anomaly detection algorithms to identify suspicious transactions, necessitating continuous monitoring and model refinement. 

How do you prepare a machine learning workflow in Python? 

A robust machine learning workflow involves sequential steps from data preprocessing to model evaluation to ensure efficiency and accuracy in predictive analytics. Check out the steps below to learn how to prepare a comprehensive machine-learning workflow in Python to ensure a smooth execution from data preprocessing to model evaluation. 

Step 1 - Import the library

    from sklearn import datasets

    from sklearn.preprocessing import StandardScaler

    from sklearn.linear_model import Perceptron

    from sklearn.model_selection import train_test_split

    from sklearn.metrics import accuracy_score, confusion_matrix

We have only imported datasets, perceptron, confusion_matrix, accuracy_score, train_test_split and standardscaler which is needed.

Step 2 - Setting up the Data

We have imported an inbuilt iris dataset to use test_train_split. We have stored data in X and target in y.  

    iris = datasets.load_iris()

    X = iris.data

    y = iris.target

Step 3 - Splitting the Data

So now we are using test_train_split to split the data. We have passed test_size as 0.33 which means 33% of data will be in the test part and the rest will be in the train part. Parameter random_state signifies the random splitting of data into the two parts.

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

Step 4 - Using StandardScaler

StandardScaler removes the outliers and scales the data by making the mean of the data 0 and the standard deviation as 1. So, we are creating an object std_scl to use standardScaler. We have fitted the train data and transformed train and test data from standard scalers. Finally we have printed the first five elements of test, train, scaled train and scaled test.

    sc = StandardScaler(with_mean=True, with_std=True)

    sc.fit(X_train)

    X_train_std = sc.transform(X_train)

    X_test_std = sc.transform(X_test)

Step 5 - Using Perceptron

We have used perceptrons with different parameters like alpha, class_weight, fit_intercept , etc. We have fixed it and predicted the output for it.

    ppn = Perceptron(alpha=0.0001, class_weight=None, eta0=0.1,

                     fit_intercept=True, n_iter=40, n_jobs=4,

                     penalty=None, random_state=0, shuffle=True,

                     verbose=0, warm_start=False)

    ppn.fit(X_train_std, y_train)

    y_pred = ppn.predict(X_test_std)

    print("y_pred: ", y_pred)

    print("y_test: ", y_test)

We are printing the Accuracy and Confusion Matrix for the test and predicted target value.

    print("Accuracy: %.2f" % accuracy_score(y_test, y_pred))

    print("Comfusion Matrix:

", confusion_matrix(y_test, y_pred))

As an output we get

y_pred:  [1 2 2 2 0 2 2 1 2 2 2 1 1 1 0 2 2 0 0 1 2 2 0 1 0 0 0 0 1 2 0 0 0 1 0 0 2

 0 1 1 1 1 2 2 2] 

y_test:  [1 1 2 2 0 1 2 0 2 1 2 1 0 1 0 2 2 0 0 1 2 2 0 1 0 1 0 0 1 2 0 1 1 1 0 0 2

 0 0 0 2 1 2 2 2]

Accuracy: 0.76

Confusion Matrix:

 [[12  4  0]

 [ 3  8  3]

 [ 0  1 14]]

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