An ML development team undergoes a specific set of steps known as the machine learning model workflow to prepare, train, and implement a model for practical use.
The phases of this workflow include collecting and preparing data, building training datasets, refining model parameters, and evaluating model performance post-deployment.
However, machine learning workflows are never rigid procedures. They can adapt to the goals and circumstances of a particular project, and wise ML teams know how to choose a flexible workflow that can scale effectively to production standards.
Diving into the General Machine Learning Workflow Steps
The exact steps an ML development team takes vary depending on the project, but a few essential components are always present.
Gathering and Preparing Training Data
Machine learning relies heavily on the quality of its initial datasets. The data you feed into a model directly impacts its accuracy, relevance, and performance when out in the field. An ML team always starts by:
- Identifying data sources and aggregating them into training datasets. Data can come from anywhere, including open source datasets, IoT sensors, media files, and many others.
- Preparing the data by undergoing pre-processing procedures. Teams must remove duplicate, irrelevant, or otherwise flawed data from the sets. If multiple sources are involved, it’s essential to align them all with the same data format for consistent processing.
- Separating the data into datasets. Machine learning uses three primary sets: training, validation, and testing sets.
The algorithm initially trains itself on the training set. Teams tune the model parameters so that the resulting algorithm can make sense of the data. They then validate model accuracy with the validation set and finally test performance with the testing set. This final step allows for last-minute adjustments to cut down on mistakes in deployment.
Choosing and Training a Model
ML developers choose models based on project needs. These factors include necessary performance and how easy it is to interpret the results. Timeframe is another consideration, as it potentially restricts the size of the training datasets and the time allowed for training.
Once you have a model, it’s time to start training. Use the training set so that the model can find appropriate parameters and features. The validation set then refines the model by allowing it to experiment with its variables and parameters. Developers also work with the model’s hyperparameters to control the learning process.
Publishing and Evaluating an ML Deployment
At this stage, developers settle on a set of parameters and switch to the testing dataset to verify model accuracy. If the results are sufficient, model deployment follows. Otherwise, teams may further adjust model settings or retrain it entirely.
Evaluation occurs by studying the model’s accuracy, precision, and recall capabilities. Accuracy is a straightforward analysis of the model’s predictions. Precision looks at the model’s positive predictions and determines how correct they actually are. And recall ensures that the model identifies positive predictions completely.
How to Choose the Right Machine Learning Project Workflow
Keep in mind that machine learning development workflows must be flexible to adapt to changing implementations. Choosing the right workflow from the start ensures your teams are all on the same page.
The exact workflow depends on the goals of the project, the team’s intended approach, and the use case of the finished product.
Defining Your Goals
How do you make sure an ML model is adding value to your project and not functioning redundantly? Define your goals clearly before you start.
Look at the current process that you intend to bolster with machine learning. How can the model effectively cover the role? What does it need to accomplish, and what success criteria will you be using to verify performance?
Finding the Right Approach
Look at how the current process works without machine learning. Borrow any methods or best practices and build them into your training methodology to give your model a head-start. In other words, use what you already know to guide your training and testing phases.
Following Through with the Solution
What can you do to ensure your machine learning model becomes a functional product at the end of the workflow?
A/B testing is an invaluable resource for comparing current process performance with the new ML model performance. This test verifies whether the model is adding value to your organization.
If you intend to sell your model as a service, think about adding a machine learning API so that it can communicate with other data sources and services, even those you don’t plan to support initially.
Documentation also helps in this regard to provide users with the code and methods needed to leverage the model effectively.
Improving Development Efficiency By Weaving In Machine Learning Workflow Automation
Automation in machine learning workflows, known as autoML, allows teams to craft models more efficiently and eliminate many of the repetitive tasks involved. It’s not uncommon for current ML models to contribute to the development of new ML models.
While you can’t automate everything, autoML effectively:
- Improves developer speed and efficiency
- Reduces the amount of necessary human intervention
- Allows for unsupervised machine learning and deep learning workflows
Automation can apply to multiple steps in the workflow, from data ingestion to training. For instance:
- Hyperparameter optimization aims to find the hyperparameters ideal for minimizing errors during the model validation stage. Testers often go through multiple combinations of pre-defined parameters, and automation helps immensely in this regard by applying algorithms like grid search and Bayesian methods.
- Model selection helps developers choose the right model by running test datasets through multiple options and seeking the best combination of default hyperparameters. Automated testing can go through multiple test runs quickly.
- Feature selection assists developers with finding the parts of the dataset most relevant to the prediction variable, or the output the ML model aims to optimize.
Machine learning development can also hit the ground running by working with autoML frameworks. Examples include the open-source Featuretools and tsfresh as well as proprietary options like DataRobot.