There are many paths to failure in ML, and one of them is a lack of structure. For many ML projects, implementing the proper roadmap can make the difference between success and failure.
In this blog you will find a representative outline for each stage of a successful machine learning project, including planning, data collection and labeling, model exploration, model refinement, testing, and deployment. This process will ensure you have a strong foundation for your ML model.
Definition and Planning
With each project, the most important step is to define the scope, prioritize features, and set up an expected timeline for the team to follow. Not only does it ensure everyone stays in the loop, but also allows for unexpected delays to be accounted for while maintaining feasibility.
As a prerequisite, you should be clear on the problem you are trying to solve and the output you are looking to optimize. This will guide your planning decisions.
One of the most common tradeoffs in an ML model is accuracy versus speed. Popular ML models tend to prioritize speed and go with a model that has both a high validation score and a comparatively lower overhead.
It’s therefore in your best interest to utilize this stage and set up a concrete problem statement along with the approach you wish to take. Andrej Karpathy’s Software 2.0
is a great methodology to structure your approach.
High quality data is vital, and every data step is crucial to the success of your ML project. From data collection to data cleaning, sorting, labeling, and more – preparing the data to be analyzed fruitfully should be on the top of your list.
Machine learning pushes developers to use computer logic and drive value through applications. Since the cost of data labeling increases with the volume and scope of the project, it’s best to prioritize the features and correlations of your raw data based on the predicted outcomes.
According to this Cloud factory report
backed by analyst firm Cognilytica, almost 80% of an AI project’s time is spent on collecting, organizing, and labeling data. Keep these stats in mind when allocating time to this stage.
Exploration and Visualization
Data visualization can be a very useful tool. The goal with data visualization is to explore the model, discover correlations between variables, and further gain an understanding of the goal state for the project. Keeping initial requirements in mind, ML models today can easily be analyzed using graphs, charts, and quality checks to deem them fit before deployment.
When it comes to model exploration, the concept of data sufficiency draws the spotlight. Before beginning, make sure to establish performance baselines. The goal here is to have a running system in place, and you can further build it out and perfect it during the next phase of refining.
Typically, processed data includes both labeled and unlabeled datasets, so it’s important to go through existing documentation and literature surrounding similar models and do a rudimentary gap analysis before proceeding ahead.
For an application-based project, plotting data-oriented graphs can help you visualize correlations otherwise lost within bulky tables. Using simple bar graphs and pie charts, it becomes much easier to plot data points and explore the model, including any outliers.
Development and Refining
Since every ML project is built keeping the future in mind, it’s best to ensure that certain features of the dataset you have created live up to those standards. This includes the data’s validity, scalability, and complexity.
Before you move on to testing, make sure to schedule some time in and refine and debug the project in a way that best suits your needs. Data underfitting and overfitting is another issue you should address by performing an error analysis, adding or reducing regularization, and tuning the hyperparameters.
It’s no surprise that your ML model’s architecture is fundamentally one of the most important aspects that you should be looking to perfect, so refining steps like hyperparameter tuning
Debugging your model significantly improves performance, making it a great way to uncover failure modes and adjust for outlier data. Refining the project also ensures your model’s predictions are accurate.
Training and Evaluation
With this step, your aim should be a thorough evaluation of the trained model under real-life conditions to validate model quality. Monitoring your project’s production readiness can help you double-check the correlations of both offline and online metrics.
In this guide
, Jeremy Jordan lists down 5 subdivisions for which you should have a versioning system in place, including model parameters, model configuration, feature pipeline, training dataset, and validation dataset.
Splitting your data into training and testing sets allows you to predict your model’s accuracy to a great extent, and check to see if your model is scalable, repeatable, and upgradable. There are many tests you can run, based on the nature of the outcome and feature priority list you created during the planning phase.
When deploying your ML model, a great recommendation is to use “shadow mode”. Going beyond the question, “Does my model work?” to “Does my model work well enough?” allows you to further perfect the design, keeping in mind the future possibilities of an upgrade. This GitHub article
goes in-depth into shadow mode deployments and A/B testing for ML projects.
With the right framework, a dedicated team of developers, a thoughtful project roadmap, and specific mitigation strategies, you can extract the most potential out of your machine learning model.