Understanding the Machine Learning Project Lifecycle: A Comprehensive Guide
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Chapter 1: Introduction to the ML Project Lifecycle
Software development can be likened to entropy: it's elusive, intangible, and invariably increases according to the Second Law of Thermodynamics.— Norman Augustine
Photo by Noémi Macavei-Katócz on Unsplash
Organizing the Machine Learning (ML) project lifecycle is crucial for structuring the decisions and actions required for a successful project. This approach allows for a concentrated effort on vital aspects to ensure the system operates smoothly, while reducing unexpected challenges.
In this discussion, we will explore the necessary steps to develop an ML system alongside a relevant case study that illustrates how everything aligns. Let’s delve into the lifecycle of an ML project.
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Section 1.1: Defining Your Goals
What do you aim to achieve with your ML system? Is it addressing a regression or classification issue? What are your features and targets? This initial step is about clarifying your objectives and ambitions.
Section 1.2: Data Collection
Once your goals are set, the next step involves constructing a relevant dataset that aligns with your objectives. This phase consists of two parts: first, defining your data and establishing a baseline; second, labeling and organizing the data effectively.
Section 1.3: Model Development
With your dataset in place, you now move to model development. In this phase, selecting and training an ML model is essential. Conducting error analysis to evaluate your model's performance is also crucial.
Model development is often iterative; based on your error analysis, you might return to adjust earlier phases or even revisit data collection if the model’s performance isn’t satisfactory.
Section 1.4: Deployment and Monitoring
The deployment phase isn't the final step. It comprises two stages: first, deploying the model into production, and second, monitoring and maintaining the system.
Monitoring is vital; it informs your next actions. Observations post-deployment might lead you back to the scoping phase to reassess your initial objectives. Additionally, capturing live traffic to the model's endpoint and enhancing your datasets is important for future improvements.
Chapter 2: Case Study - Dog Breed Recognition System
In this case study, we will explore the phases of the ML lifecycle through the development of a Computer Vision (CV) system. Imagine a dog shelter requests a system capable of recognizing dog breeds to automate the cataloging of new arrivals.
Photo by Alvan Nee on Unsplash
The first phase is scoping. Here, you confirm this is a CV issue and estimate key metrics for your system. For instance, while accuracy is crucial, latency may not be a concern at this stage. Additionally, you will gauge the resources and set deadlines for your project.
Next, during the data phase, it's vital to ensure consistent labeling. For example, always label a "Yorkshire Terrier" correctly without variations. Other considerations might include image cropping or brightness adjustments to create a high-quality dataset for your model.
Upon dataset completion, the modeling phase begins. You will select a suitable ML model (such as a specific neural network architecture), train it, and conduct error analysis. In industry, it's common to utilize established model implementations (e.g., ResNet) while tweaking hyperparameters and datasets.
Error analysis will guide you on how to enhance model performance. You’ll need to determine if more data is required or if specific edge cases need to be addressed, and whether there are inconsistencies in your dataset.
Once the model is deemed satisfactory through error analysis, you can transition to the deployment phase. At this point, consider the architecture and infrastructure for your system. Should it run on an edge device like a smartphone, or be hosted on the Cloud with REST API capabilities?
Assuming you opt for a web server to make your model accessible via a web API, a mobile app could take a dog’s photo, crop it, and send it to the recognition API. A few seconds later, the app would receive the dog's breed and register it as a new shelter member.
Finally, the monitoring and maintenance phase must be addressed. Changes in lighting conditions or the arrival of puppies can affect model performance, leading to what is known as data drift. When real-world data distributions shift, it's essential to capture this change and update your model accordingly.
Conclusion
Strategically planning the Machine Learning (ML) project lifecycle is instrumental in organizing the decisions and actions required for successful outcomes. This overview provided insights into the steps involved in an ML project, supported by a case study to enhance understanding.
This narrative marks the beginning of a series on MLOps, detailing how to transition an ML model from concept to production. Stay tuned and subscribe to my newsletter!
About the Author
I’m Dimitris Poulopoulos, a machine learning engineer at Arrikto. I’ve developed and implemented AI and software solutions for clients including the European Commission, Eurostat, IMF, the European Central Bank, OECD, and IKEA.
For more insights on Machine Learning, Deep Learning, Data Science, and DataOps, connect with me on Medium, LinkedIn, or Twitter @james2pl.
The views expressed here are my own and do not reflect those of my employer.
The second video titled "2. Lifecycle - ML Projects - Full Stack Deep Learning" offers further insights into the various stages of ML project lifecycles, providing a comprehensive understanding of full-stack deep learning.