7 Steps to Leverage Machine Learning for Software Development

7 Steps to Leverage Machine Learning for Software Development
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Machine learning (ML) is a subfield of artificial intelligence (AI) that enables computers to learn and make decisions without explicit programming. The more relevant data the computer is exposed to, the more readily it can make predictions regarding related scenarios. While AI tools such as ChatGPT have become valuable assistants for software developers, you can go beyond ChatGPT and train a specialist ML model for your own development purposes.

By training your own ML model, you can focus on the specific challenges and patterns relevant to your projects. You’ll also improve accuracy, maintain data privacy and gain the flexibility to experiment with different algorithms, techniques and hyperparameters.

Machine learning model development requires diligence, experimentation and creativity. Although it may seem intimidating — even for those with experience — the methodology for data-centric projects is similar. Let’s walk through the seven-step process to successfully realize your project objectives.

How Software Developers Can Leverage Machine Learning

The software development process involves a range of activities from designing and coding to testing, deployment and maintenance. By employing machine learning models, developers can greatly enhance productivity and improve code quality, bug detection and continuity among other aspects.

Machine learning algorithms are trained on large datasets of code to learn patterns and make predictions about code quality or identify potential bugs. This can save developers time and effort by automating tasks that would otherwise be done manually. In addition, as your software development projects evolve and new data becomes available, you can retrain your ML model to adapt to changing conditions. This enables your model to continuously improve and stay up-to-date with the latest trends and patterns.

Step 1: Identify the Problem You Want to Solve

To begin any ML project, get to grips with the problem you are trying to solve by clarifying the project’s aims and requirements. Convert this knowledge into a suitable problem definition, then devise a preliminary plan to accomplish the project’s goals.

Key questions to bear in mind include:

    • What is the business objective that necessitates a cognitive solution?
    • What are the determined criteria of success for the project?
    • How can the project be split into smaller phases?
    • Do any special requirements exist for transparency or bias reduction?
    • Are there ethical considerations?
    • What are the expected levels of accuracy, precision and confusion matrix values?
    • What are the anticipated inputs and outputs?
    • How will the team assess the benefits of the model?
    • What existing shortcuts or heuristics exist for solving the problem, and how much better does the model need to be in comparison?

Although there are many questions to answer in the initial stage, these insights can go a long way toward achieving the preferred project outcomes.

To increase ROI from a machine learning project, set measurable, achievable goals related to business objectives. In addition to including ML measures such as precision, accuracy and recall in the metrics, business-related KPIs are crucial.

Step 2: Collect Data for Your Model

Once you have a solid grasp of the business requirements, it’s time to put the spotlight on data collection. All ML models are built on training data. The model then applies the acquired knowledge to never-before-seen information to make predictions and carry out its purpose. Not only do you need a large amount of data for adequate training but the data also needs to be of a good quality and in the correct form.

This is the time to identify the project’s data needs and collect the relevant information. Consider the following to help you clarify the quantity and quality — not to mention the type — of data required for a successful model:

    • What sources can you access to provide the data required to train the model?
    • What quantity of data is necessary for the project?
    • How much training data is available and how good is its quality?
    • How is the test data and training data split?
    • Can you utilize pre-trained models?
    • Are there special requirements to acquire real-time data on edge devices in remote places?
    • Will model training happen in real time, once-off or in iterations with periodic deployment of versions? Real-time training may not be feasible for all setups due to specific data needs.

During this phase of the project, think about whether there are differences between real-world data and training data — or test data and training data — and how you’ll validate and evaluate the model performance.

Step 3: Preprocess and Clean the Data

To prepare your data for training a model, you first need to shape it. This involves collecting, cleansing, aggregating, augmenting, labeling, normalizing and transforming your structured, unstructured and semi-structured data.

Data preparation procedures require that you:

    • Standardize data formats across all sources
    • Check and replace any data that is not correct
    • Augment data and enhance it with third-party sources
    • Add extra dimensions with pre-calculated values and bundle data as necessary
    • Remove unnecessary and duplicated info, as well as irrelevant data, for better results
    • Reduce noise and ambiguity
    • Look at anonymizing information
    • Sample from large datasets and select features that identify the most important dimensions, reducing these if necessary.
    • Split the collected and optimized data into training, testing and validation sets.

Data preparation and cleansing will take up the majority of the ML project’s time. In fact, it may constitute as much as 80% of the time dedicated to your project. When it comes to training your model, the saying, “garbage in, garbage out” couldn’t be more apt. Because the success of your model is highly dependent on training data, the time spent preparing and cleansing is well worth it.

Step 4: Choose a Model Architecture and Train It

With all your data optimized, it’s time to train the model. This phase requires the following actions:

    • Select the most suitable algorithm based on your data requirements and the anticipated learning outcomes. Machine learning algorithms are the instructions followed to record experience and formulas that guide learning over time. Depending on the type of ML approach you adopt, some algorithms will offer better results than others.
    • Tune and configure hyperparameters to achieve optimal performance.
    • Determine whether your model requires explainability or interpretability.
    • Identify the desired features for optimized results.
    • Develop ensemble models and test various versions for performance.
    • Determine the model’s operational and deployment requirements.

Step 5: Evaluate Performance and Establish Benchmarks

Test your model on new, unlabeled data and tune parameters to optimize model performance. Model evaluation is fundamentally the quality assurance of ML. Effectively evaluating its performance against relevant metrics and requirements will establish how well the model operates in real-world applications. If it doesn’t perform well, you’ll need to reevaluate your data or try a different model architecture.

From an AI perspective, the evaluation process comprises:

    • Model metric evaluation using a validation dataset
    • Calculating confusion matrix values to evaluate and fix classification problems
    • Identifying methods for k-fold cross-validation
    • Optimizing performance by further tuning hyperparameters
    • Evaluating your machine learning model against the baseline model or heuristic

Step 6: Operationalize Your Model

Once you’ve validated your machine learning model for real-world application, it’s time to transition it into operation and assess its efficacy. As such, you’ll need to:

    • Implement the model and regularly track its performance
    • Establish a standard of comparison to evaluate future versions of the model
    • Repeat the process of improving the model in different areas to enhance its overall performance

Model operationalization may cover deployment across various scenarios — from cloud to edge, on-premises or a controlled group. Considerations include versioning, deployment, monitoring and staging for development and production. Depending on the requirements, this can range from a basic report to complex, multi-endpoint deployment.

Step 7: Iterate and Adjust

To successfully implement technologies, you need to start small and reiterate often to achieve the bigger goal. While the bulk portion of the work might be done, monitoring your model’s performance over time is key to ensuring that the project remains relevant and useful. If your model’s performance degrades over time, you may need to retrain it or collect more data. As the requirements of business and technology evolve, new demands may arise, creating the need to deploy the model in different systems.

While you continue to monitor and adjust your model, determine:

    • The next requirements to improve and optimize functionality
    • How to expand model training to achieve greater capabilities
    • How to improve your AI tools’ operational performance and accuracy
    • Any varying operational requirements for multiple deployments
    • Solutions to counteract model and data drift arising from changes in real-world data

The bottom line is to analyze what has worked well in your model, identify areas for improvement and take ongoing progress into account. Here, machine learning model building can help you maximize the potential for a positive outcome. In addition, keep looking for opportunities to refine your approach. Also, be sure to stay up to date with changing requirements and superior methods to adapt to evolving business needs.

Let Digital Transformation Be Your Competitive Edge

Machine learning allows computers to learn from data on their own, recognizing patterns and adapting as they receive more input. ML development employs algorithms to enhance software quality by automatically detecting and correcting code errors. Before you license or develop a software application or new technology, it’s vital to carry out careful assessment and analysis. A dependable, impartial partner who can offer an independent opinion and constructive direction is an invaluable asset, after all.

Whether you’re focusing on internal applications or an acquisition, GAP delivers independent software and technology reviews to provide the insights required for strategic decision-making. We’ll deliver a timeline and roadmap for the recommended course of action, equipping you with the necessary information for all stages of execution—and ultimately, for project success.

With GAP, you can scale smarter by providing software solutions that generate business results. Ready to get started? Let us help your digital transformation become your competitive edge.