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  • Writer's pictureHerve Blanc

Software projects versus AI projects

Updated: Jan 9

AI project daily

Software projects and AI projects share many similarities, but there are also important differences between the two that are worth highlighting.

One key difference is the level of complexity involved in AI projects. AI projects often require specialized knowledge in fields such as signal processing, data science, machine learning, deep learning, and natural language processing, which may not be necessary for traditional software development. Additionally, AI projects often require large amounts of data for training models, which can present unique challenges in terms of data storage, processing, and management.

This is why AI projects tend to be more research-oriented, with a greater emphasis on experimentation and innovation. This can make AI projects more unpredictable and less deterministic than traditional software projects, which are often focused on delivering a specific set of features within a predetermined timeframe. Despite these differences, many of the core principles of software development still apply to AI projects. Both require careful planning, documentation, testing, and maintenance to ensure their success over the long term.

(Software projects versus AI projects) Similarities

AI project coding

Key similarities between AI and software projects are the following:

  • Both require careful planning:

Whether you're building a software application or developing an AI model, careful planning is essential. This includes defining requirements, determining the scope of the project, and creating a development roadmap.

  • Both involve designing and developing complex systems:

Software projects and AI projects both involve designing and developing complex systems, although the specific nature of the complexity may differ.

  • Both involve coding and testing:

Both software projects and AI projects require coding and testing to ensure that the system is functioning correctly and meets the desired requirements.

  • Both require ongoing maintenance:

Once a software application or AI model is deployed, ongoing maintenance is required to keep it running smoothly and to fix any bugs or issues that arise.

  • Both can benefit from agile development methodologies:

Agile development methodologies, such as Scrum or Kanban, can be used for both software and AI projects to promote collaboration, flexibility, and continuous improvement.

  • Both leverage good practices like CI/CD continuous integration and continuous deployment

These are crucial practices in ML model deployments because they allow faster and more efficient model updates and enhancements. CI/CD ensures that models are thoroughly tested and validated before they are deployed to a production environment.

  • Both can be based on open-source tools and libraries:

Open-source tools and libraries are widely used in both software and AI development to increase productivity, improve quality, and reduce costs.

After all, AI is software so it is understandable software product development lifecycle is part of the AI product lifecycle, often referred to as MLOps.

(AI projects) Specificities

3D visualization

Yet AI projects have key specificities we don’t see in software projects

  • Data collection:

This involves gathering relevant and diverse data sources that can be used to train and test the ML models.

  • Data annotation:

This involves labeling or tagging the data with meaningful information that can be used by the ML algorithms to learn patterns and features.

  • Data preprocessing:

This involves cleaning, transforming, scaling, encoding and splitting the data into training, validation and testing sets.

  • Model selection:

This involves choosing the best ML algorithm or architecture for the given problem and data.

  • Model tuning:

This involves finding the optimal hyperparameters or configuration for the chosen ML model using techniques such as grid search or random search.

  • Model evaluation:

This involves measuring the performance or accuracy of the ML model on unseen data using metrics such as precision, recall, F1-score or ROC curve.

It will be necessary to convert the metrics into business impact: it is indeed key to understand what the impact of a model prediction error is, and to ensure with the business lines that its performance/impacts are acceptable.

  • Model deployment:

This involves deploying the trained ML model as a web service or an API that can be accessed by other applications or users. The jargon is a little different: DevOps in the case of purely software projects, MLOps or LLMOps for AI projects.

  • Model monitoring:

Once the model has been deployed, it must be ensured that it works. The inference of the model made available behind an API must meet its SLA (Service License Agreement) requirements with, for example, 99.9% availability. In addition to this, it is necessary to verify that the statistical distribution of the production data is similar to that used in training the model, otherwise it will be necessary to update the model.

I will take the time in future posts to zoom in some those AI projects specificities.

Until then please feel free to reach out to me and ask questions you’d like to be addressed on this blog.

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