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Machine Learning Workflow: What It Is & Why It Matters

These days, many people are talking about machine learning (ML). The global ML market is growing – in 2021, it was already valued at $15.44 billion US.

Machine learning (ML) comes from artificial intelligence (AI), which is a machine’s ability to imitate intelligent human behavior. But what is it really used for?

In this article, we’ll talk about what machine learning is, and then go even deeper into the workflows that make ML projects happen. What is a machine learning workflow, and why do they matter? 

What Is Machine Learning?

Machine learning (ML) is a branch of AI and computer science. It uses data and algorithms to copy the human learning process, continuously bettering its accuracy. ML is an important part of the ever-expanding field of data science.

By using statistical methods, algorithms are taught to make predictions and insights in data mining projects. These insights then influence decision-making in applications and businesses – and, hopefully, create business growth.

The market demand for data scientists continues to increase with the growth of big data. Data scientists use ML to identify the most important business questions – and find the data to answer them.

In recent years, data has become an important currency. This is because a lot of valuable intelligence can be gleaned from big, captured datasets which are used to make crucial business decisions. 

But machine learning goes far beyond simply storing data. It’s about capturing, preserving, accessing, and transforming data to interpret it and find its meaning – and ultimately, its value.

Machine learning algorithms are usually developed by using frameworks like TensorFlow and PyTorch.

What Is a Machine Learning Workflow?

Machine learning workflows determine which phases are used during a machine learning project. An ML workflow describes the steps of a machine learning implementation. 

Typically, the phases consist of data collection, data pre-processing, dataset building, model training and evaluation, and finally, deployment to production.

What Is the Goal of a Machine Learning Workflow?

The goal of machine learning is to teach computers how to behave by using the data that you input. Instead of writing code to tell a computer what to do, your code presents an algorithm that adapts to resemble examples of correct behavior.

To attain the goal of a general machine learning workflow, the first step is defining the project, and the next step is finding a method that works. 

It is not recommended to try fitting the model into a workflow that is too rigid. Instead, it is best to make a flexible workflow. This lets you start small, then step up to a “production-grade” solution (meaning you have the capacity to handle frequent use in commercial or industrial settings). 

While ML workflows change from project to project, these are the typical machine learning phases.

The 7 Steps in a Machine Learning Workflow

1. Collect Data

Collecting data begins with defining the issue. An understanding of the issue is crucial for identifying the requirements – and the best solutions.

For example, a machine learning project that uses real-time data needs an IoT system that uses various data sensors. The first datasets can be gathered from different sources like a database, file, or sensor.

2. Prepare the Data

This means cleaning and formatting the raw data. Raw data can’t be used for training the machine learning model. Also, machine learning models can only handle numbers, so ordinal and categorical data must be converted to numeric features.

3. Choose an ML Model

Considerations when choosing a model include performance (the quality of the model’s results) and explainability (how easy it is to interpret the model’s results). 

Other considerations include dataset size (which affects how data is processed and synthesized) and training time and cost (of training a model).

4. Train the ML Model

There are 3 main steps to training the machine learning model:

  1. Start with existing data
  2. Analyze the data to find patterns
  3. Make predictions

5. Evaluate the ML Model

There are three main ways to evaluate the model: 

  1. Accuracy (the percentage of correct predictions for the test data)
  2. Precision (the applicable cases that were predicted to belong to a particular class) 
  3. Recall (the cases that were predicted to belong to a class concerning all of the examples that legitimately belong in that class)

6. Perform Hyper Parameter Tuning

Hyperparameters define the model architecture, so the process of trying to find the ideal model architecture is called hyperparameter tuning.

7. Deploy the ML Model To Make Predictions

A prediction model is a container for the different versions of your ML model. To deploy a model, you first make a model resource in AI Platform Prediction (which runs your models in the cloud).

Next, develop a version of that model, and finally, link the model version to the model file that is stored in the cloud.

What Can We Automate in a Machine Learning Workflow?

A machine learning workflow can be used much more efficiently if parts of it can be automated. So, what elements of the workflow are possible to automate? 

Data Ingestion

Automating data ingestion means freeing professionals up to be more productive at activities that need more manual attention.

This increased productivity allows processes to be optimized and resources to be managed more efficiently.

Model Selection

Model selection means experimenting with various combinations of numbers and text data, plus various text processing methods – all done automatically.

Feature Selection

This means automatically selecting the features of a dataset that are most valuable to your prediction variable – or most valuable to the output you are seeking.

Hyperparameter Optimization

The goal of this is to find the hyperparameters with the lowest errors on the validation set so that these results generalize to the testing set.

Top Machine Learning Workflow Tools

Currently, machine learning professionals prefer the following three tools to make a machine learning pipeline:

  • ClearML
  • MLflow 
  • Kubeflow

Machine Learning is a rapidly expanding field with a large demand for ML professionals. So, where can you learn how to do it? 

Machine Learning Workflow FAQ

Here are the answers to some machine-learning frequently asked questions.

What Is a Machine Learning Model?

A machine learning model is a file that is taught to recognize certain kinds of patterns. A model can be trained over a data set, giving it an algorithm that it learns from.

Once trained, the model can use its knowledge to process data that it has never seen and make predictions about that data. For example, you want to build an app that recognizes users’ emotions from their facial expressions.

So, you train a model by inputting pictures of faces that are tagged with corresponding emotions. Next, use that model in an app that can recognize any user’s emotions.

What Is a Machine Learning Pipeline?

A machine learning pipeline helps automate ML workflows. 

They work by setting up a data sequence to transform and connect in a model. That model can then be tested to see what the outcome is – whether good or bad. 

All steps of an ML pipeline are repeated to continuously improve the model’s accuracy, and this is all to achieve a successful algorithm. 

What Is Automated Machine Learning?

This is the process of automating the application of machine learning to real-world problems. Automated machine learning, or AutoML, includes every phase from starting with a raw dataset to creating an ML model ready for use. 

Learn Data Science at Coding Dojo

You can always start by reading about how to learn In week science, but perhaps you want to take things one step further. 

Over 8000 students have learned at Coding Dojo, where anyone can learn to code regardless of their professional background. That’s why the admissions process does not include a coding challenge or technical assessment. Here, you can learn machine learning best practices and other data science skills.

For the student’s convenience, Coding Dojo offers an online data science bootcamp that can be taken part-time. 

This bootcamp is appropriate for beginners and more advanced learners and takes between 16 to 20 weeks to complete (when learning for roughly 20 hours per week).

This data science and Python machine learning bootcamp teaches the foundation of data science and machine learning. It’s a deep dive into the end-to-end data science process that includes data prep, analysis, and visualization.

You will also learn how to correctly apply ML algorithms to various tasks. In the end, you will have a portfolio of projects that showcase your data science certification to potential employers.

So, what will your studies look like? 

In week one, you’ll learn the Python basics that are required for data science. Week two will teach you how to collect, clean, and manipulate data using the Python library Pandas. 

In week three, you’ll learn how to build visualizations to support exploratory data analysis (EDA). Week four, you will use Python to make graphs to share with stakeholders and communicate your findings.

This data science bootcamp was created to prepare you for the real world. Every week, you will be given weekly exercises to learn new topics so you can learn the skills you need to get a job using ML.