The Lifecycle of a Machine Learning Model
This article delves into the lifecycle of a machine learning (ML) model, highlighting the unique aspects of testing and development that are crucial for startups venturing into the tech landscape.
Unlike traditional software development, creating a machine learning model is a distinct process that necessitates a tailored approach. At its core, a machine learning model is an application of artificial intelligence (AI) that enables automatic learning and improvement from experience, without explicit human intervention. The primary aim is to leverage AI and ML algorithms to drive competitive advantages for businesses.
Understanding the Business Context
The journey of any ML project should kick off with a thorough business analysis. This phase is about aligning with your team to identify the business challenges the model aims to solve. It involves understanding project stakeholders, funding sources, and decision-makers, as well as exploring existing solutions and their limitations.
The key objective here is to grasp the main business variables the model will predict, termed as the model's key performance indicators (KPIs). Determining which metrics will measure project success is crucial. For instance, predicting the likelihood of customers switching service providers can inform strategies to reduce churn. By project end, the goal might be to reduce customer churn by a specific percentage. Crafting offers to minimize churn utilizes these insights, with metrics designed around SMART principles.
Assess the Business Value of the ML Project
The ultimate goal for any business is to increase revenue or enhance customer service quality, which in turn, boosts profits. Based on this fundamental understanding, convince your leadership and stakeholders that the ML project under consideration is a worthwhile investment.
Ideally, provide rough estimates on how the ML model can elevate company revenues, enhance user engagement, or streamline request processing. Approach this creatively, sidelining perfectionism, and don't hesitate to seek input from colleagues in finance and marketing.
Remember, these metrics will later serve as benchmarks for evaluating the project, so maintain a realistic outlook in your projections.
Resource Assessment and Risk Management
Evaluating the resources required throughout the project's duration is essential. This includes hardware availability, data storage solutions, access permissions, the need for additional external data, and the availability of client expertise for consultations.
Identifying potential project risks and devising mitigation strategies are crucial steps. Common risks include project delays, financial uncertainties, inadequate or poor-quality data, or the absence of discernible patterns in the data, rendering the model uninteresting to stakeholders.
Gather Requirements
Once you've established the necessity of the ML model, begin collecting requirements. Given that each industry and project has its unique traits, there's no exhaustive checklist to rely on. Instead, trust your experience and collaborate with your peers.
Here's a helpful tip: start with a list of generic questions and refine them through discussions. Here are some questions I typically ask:
How much data do we have, and how will it be labeled?
What's the expected latency of the model?
Where will the model be deployed—cloud or on-premises? What are the specifications?
Are there any data privacy and model explainability requirements?
Just because a task can be solved with ML doesn't mean it should. At this juncture, reevaluate the situation. Sometimes, a software solution or a basic rule-based approach might be more appropriate.
Remember, there's no ML without data. It sounds obvious, yet, through years of experience, I've seen many organizations overlook this, aspiring for AI with inadequate, unclean, or feature-deficient datasets. Monica Rogati's insightful piece, "The AI Hierarchy of Needs," suggests envisioning AI as the pinnacle of a pyramid whose base is data collection, storage, and cleaning.
Start Small and Abandon Unpromising Ideas Swiftly
Even if your vision is to build an ML system serving millions daily, it's wise to start small:
Proof of Concept (PoC). Extract data manually, proceed through rapid iterations with a couple of algorithms in Jupyter Notebook, and prove (or disprove) your hypothesis that your existing data can train an ML model with satisfactory accuracy. The PoC stage also sheds light on deployment and scaling needs.
Minimum Viable Product (MVP). If the PoC is a success, move on to creating and releasing a product with just the core features. In ML terms, this means deploying the model to a user segment and evaluating its business value.
If an idea doesn't pan out, abandon it without guilt and move on. It's much easier to do this before investing years of work and substantial funds. The low cost of failure is key to project success.
Prepare Project Documentation
In software development, project documentation outlines the system's architecture, overall structure, individual components, and their interactions. This documentation can vary in form, structure, formality, and detail level, as determined by the team. During implementation, project documentation acts as a blueprint for developers.
This is a best practice in development, and as mentioned earlier, it applies equally well to ML projects. I personally appreciate project documentation for several reasons:
It triggers the thought process. Drafting project documentation is akin to conceptualizing the project: you're not coding yet but are already making decisions about data, algorithms, and infrastructure. You're considering all scenarios and weighing compromises, which means future time and cost savings by avoiding dead ends.
It facilitates team sync and collaboration. Everyone has access to the document, allowing them to review the system design and engage in necessary discussions. This ensures everyone is on the same page, contributing their insights.
Once the business objectives are clearly defined, it's vital to articulate the problem in machine learning terms. This includes deciding on the metrics for model evaluation (such as accuracy, precision, recall, MSE, MAE, etc.) and establishing the criteria for model success (for example, considering an accuracy of 0.8 as the minimum acceptable level and 0.9 as optimal).
Data Analysis and Preparation: A Foundation for Machine Learning Success
In the realm of machine learning, the initial phase is dedicated to analyzing, collecting, and preparing all necessary data for model application. The paramount objective at this stage is to cultivate a processed, high-quality dataset governed by identifiable patterns. This involves a systematic approach divided into four crucial steps: data analysis, data collection, data normalization, and data modeling, each tailored to guide budding IT startups through the complex landscape of machine learning.
Data Analysis
The initial step is to dissect the available data to recognize its strengths and shortcomings, gauge its adequacy, and brainstorm potential applications while gaining a deeper understanding of the client's business operations. A thorough examination of all data sources the client grants access to is essential. Should there be a shortfall in internal data, acquiring additional data from third parties or orchestrating new data collection efforts may be necessary. Identifying the types of data at hand—whether proprietary, third-party, or "potential" data requiring active collection—is vital. Detailing the data across sources, including tables, keys, row and column counts, and storage volume, sets the groundwork. Utilizing tables and graphs to hypothesize how the data can address the challenge at hand is crucial. Ensuring data quality before progressing to modeling is imperative, as inaccuracies at this juncture could detrimentally impact the project's trajectory. Common data pitfalls include missing values, inaccuracies, typos, and inconsistent encoding of values, such as disparate representations of "w" and "women."
Data Collection
Data collection is the orchestrated gathering of information relevant to the research variables, enabling hypothesis testing and outcome evaluation. Proper data collection is instrumental in maintaining the integrity of research efforts. Selecting appropriate data collection tools and adhering to clear guidelines for their use minimizes the risk of errors. Since predictive models are only as robust as the data they are built upon, establishing a solid data collection practice is critical for developing high-performance models. The data must be devoid of errors and relevant to the research question.
Data Normalization
This next step is where data analysts and engineers spend a significant portion of their time: cleaning and normalizing "dirty" data. Frequently, this requires making informed decisions based on partially understood data, such as how to handle missing or incomplete data and outliers. Furthermore, aligning this data with the corresponding unit of analysis, such as a specific customer, can be challenging. For instance, predicting whether a singular client will churn cannot rely on data from disparate sources. Data engineers are tasked with preparing and amalgamating all sources into a format interpretable by machine learning models.
Data Modeling
Data modeling is a sophisticated process of creating a logical representation of data structure, aimed at forecasting. A well-constructed data model should accurately reflect the domain, aligning with all user data perceptions. This phase also involves blending and aggregating data from various sources, such as web, mobile applications, and offline data. Engineers integrate diverse data into a unified dataset, for example, combining existing feature data into a comprehensive set.
Feature Engineering
Feature engineering involves the evaluation, statistical processing, and transformation of data to select features for the model. Understanding the underlying mechanisms of the model, assessing the relationship between components, and discerning how machine learning algorithms will utilize these components is crucial. This stage demands a creative fusion of experience and insights garnered from the data exploration phase. Balancing feature engineering is essential—identifying and incorporating informative variables without introducing extraneous, unrelated features. Informative features enhance model outcomes, while non-informative features add unnecessary noise. Selection of features must consider all new data acquired during the model training phase.
Model Training and Evaluation
In the realm of machine learning, model training is a pivotal phase that unfolds iteratively. This process involves experimenting with various models, fine-tuning hyperparameters, comparing metric outcomes, and ultimately selecting the best combination for your project.
Selecting the Right Algorithm
The initial step is to determine which models will be utilized, a decision that hinges on the specific problem being addressed, the features in use, and the model's complexity requirements. For example, certain models might not be suitable for further deployment in applications like Excel, making alternatives like Decision Trees or AdaBoost inappropriate choices. Key factors to consider include:
Data Sufficiency: Complex models generally require substantial amounts of data.
Missing Values Handling: Some algorithms can't process missing values without prior treatment.
Data Format: Certain algorithms might require data conversion.
Planning for Testing
Next, it's crucial to establish which data will be used for training the model versus testing it. The conventional approach divides the dataset into three parts—training, validation, and testing—in a 60/20/20 ratio. This strategy helps avoid overfitting, with the training set used for model learning and the validation and testing sets for metric evaluation without bias. More sophisticated model training strategies may employ various cross-validation techniques. At this stage, planning for hyperparameter optimization—including deciding between grid search or random search methods and determining the number of iterations needed for each algorithm—is essential.
The Model Training Cycle
With the groundwork laid, the training cycle begins. Each iteration's results are meticulously recorded, offering insights into each model's performance and the effectiveness of the hyperparameters used. For models exceeding the minimum acceptable metric threshold, particular attention should be paid to:
Unusual Patterns: For instance, a model's 95% prediction accuracy being attributable to a single feature warrants further investigation.
Training Speed: If a model is slow to train, consider switching to a more efficient algorithm or reducing the size of the training set.
Data Issues: For example, the presence of missing values in the test set might result in incomplete metric calculation, skewing the model evaluation.
Evaluating the Results
After compiling a list of suitable models, a thorough analysis is necessary to select the top performers. The final output should be a list of models ranked according to objective and/or subjective criteria. This step involves conducting a technical analysis of model quality (using metrics such as ROC, Gain, K-S, etc.), assessing readiness for integration into corporate data systems, checking if the quality criteria are met, and analyzing the results from a business objective standpoint. If the success criteria (selected metric) are not met, it may be necessary to improve the current model or explore other options. Before moving to deployment, ensure the modeling results are understandable and logical. For instance, a customer churn prediction model with a GAIN metric of 99% might be too good to be true, prompting a reevaluation of the model.
Solution Evaluation
Upon completing the prior phase, the outcome is a machine learning model equipped with identified patterns. This stage involves assessing the project's results.
Previously, the focus was on evaluating the modeling outcomes from a technical perspective. Now, the assessment shifts towards evaluating the results in terms of meeting business objectives. For instance, how effectively does the developed model address the business challenges it was intended to solve? Additionally, it's crucial to identify any new, useful information uncovered during the project that merits highlighting. A project retrospective to outline its strengths and weaknesses is next, answering questions like:
Which project stages could have been executed more efficiently?
What mistakes were made, and how can they be avoided in the future?
Were there any hypotheses that did not pan out? Should they be revisited?
Did any implementation steps result in surprises, and how can these be anticipated in the future?
If the model meets the client's needs, the next steps are either to deploy the model or improve it further if there are opportunities for enhancement. When multiple suitable models exist, select the one for deployment.
Model Deployment
Deploying a machine learning model into production means making the model accessible to other business systems. By integrating the model, these systems can send data to and receive predictions from it, which are then utilized across the company's systems. This enables the full utilization of the developed machine learning model. The primary goal at this stage is to operationalize the model, deploying it and the associated data pipeline into a production or production-like environment for application access. Having developed an efficiently functioning model, the task is to bring it into operation for interaction with the company's other systems. Depending on business requirements, the model performs predictions in real-time or batch mode. Deployment involves exposing the model via an open API interface, simplifying its use across various applications, such as:
Websites.
Spreadsheets.
Business application dashboards.
Server applications.
Additionally, companies must decide whether to use Platform as a Service (PaaS) or Infrastructure as a Service (IaaS). PaaS can be beneficial for prototyping and companies with lower traffic volumes. However, as business or traffic grows, transitioning to IaaS might become necessary due to its increased complexity. Various solutions are offered by major companies (e.g., AWS, Google, Microsoft). If applications are containerized, deployment across most platforms/infrastructures becomes simpler. Containerization also facilitates using a container orchestration platform to scale the number of containers efficiently as demand increases. Finally, ensure deployment occurs through a Continuous Deployment platform, streamlining updates and maintenance.
Testing and Monitoring
At this juncture, the focus shifts to testing, monitoring, and controlling the model to ensure its effectiveness and reliability. Machine learning model tests typically fall into several categories:
Differential Tests
This involves comparing the outcomes provided by the new model against those of the previous model using a standard set of test data. The sensitivity of these tests should be adjusted based on the model's application scenario. They are crucial for identifying a model that appears functional but isn't, such as when an outdated dataset was used for training or the model wasn't trained on all relevant features. Such issues, inherent to machine learning, won't necessarily result in errors during standard testing.
Benchmark Tests
These tests compare the time taken for training or making predictions from one model version to another. They help prevent inefficient code additions in machine learning models, something challenging to detect with conventional testing methods (though some static code analysis tools can offer assistance).
Load/Stress Tests
While not unique to machine learning models, given the unusually high CPU/memory requirements of some models, these tests are particularly recommended.
A/B Testing
Another popular testing method is A/B testing, also known as split testing. It allows for the quantitative assessment of two model variants and comparison between them. Ensuring statistically significant results requires careful isolation of the models to prevent cross-influence.
These tests are significantly easier to implement with containerized applications, as it simplifies the setup of a realistic production stack.
Monitoring and Alerting
Especially critical during model deployment, monitoring and alerting become increasingly important as the system grows more complex. These tools are essential for signaling when predictions for a specific system deviate from the expected range. Monitoring and alerting can also identify indirect issues, such as a new convolutional neural network exhausting the monthly AWS budget in 30 minutes. Dashboard tools that allow for quick checks of deployed model versions are also indispensable.
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