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MLL as the key to success in a world supported by AI

12 June 2024

There is no doubt that Artificial Intelligence (AI) has become a cornerstone of innovation and competitiveness in many industries. To fully harness the potential of AI, it is crucial to understand and effectively manage the Machine Learning Lifecycle (MLL). So, what exactly is the MLL?

The Machine Learning Lifecycle encompasses all stages of model development, from data collection, through model training and evaluation, to production deployment and quality monitoring. Understanding and optimizing each of these stages is key to success in the rapidly evolving world of Artificial Intelligence (AI).

„MLL allows not only the creation of accurate and efficient models, but also their continuous improvement, which is essential in the context of rapidly changing business and technological requirements.

says Marek Mirowski, ML Team Leader at 3Soft.

Stages of the Machine Learning Lifecycle (MLL)

Machine Learning Lifecycle (MLL) is a comprehensive process consisting of several key stages. Each of them plays an important role in building, training and deploying models that indirectly gain the ability to adapt to changing external conditions through the MLL process and can bring real benefits.

Graphic 1. Stages of the Machine Learning Lifecycle (MLL)

1. Problem definition

The first stage of MLL is to understand and define the problem to be solved. This requires understanding the business context, project goals and stakeholder expectations. It is critical to determine what data will be needed and what metrics of success will be used. Examples include predicting customer behaviour, optimizing manufacturing processes, or detecting failures.

2. Data collection

Data is the foundation of any machine learning model. The data collection process involves identifying data sources, collecting the data, and verifying its quality. Data can come from a variety of sources, such as databases, CRM systems, social media, or IoT devices.

3. Data preparation

Once the data has been collected, the data preparation stage follows. This includes cleaning, transforming and structuring the data to make it suitable for analysis. Common techniques include removing duplicates, dealing with missing values, and standardizing the scale of variables. Through this stage, potential problems such as missing values or unusual observations can be identified.

4. Data analysis

Data analysis is the process of understanding data properties and patterns. This uses visualization techniques, such as graphs and histograms, and descriptive statistics. This step helps to identify important features that may affect the model, and to understand the structure of the data.

5. Model building

The model-building stage involves selecting appropriate algorithms, training the model on a training data set, and validating it. It is also important to tune hyperparameters to optimize the quality of the model. Different algorithms can be tested to find the best solution for a given problem.

6. Model evaluation

Once the model is built, it is evaluated. This uses various metrics to make sure that the model meets certain criteria and is ready for implementation.

7. Model implementation

Deployment is the process of moving the model into the production environment, which requires integration with existing systems. This stage enables the actual use of the model to solve the previously defined business problem.

8. Monitoring, optimizing, and maintaining the model

Once the model is implemented, continuous monitoring and maintenance is required. This includes tracking the quality of the model over time, identifying performance degradation, and updating the model in response to changing data and business conditions. Regular optimization of the model is key to maintaining its effectiveness. This includes re-training the model on new data, tuning hyperparameters, and making modifications based on performance analysis.

Machine Learning Lifecycle and its benefits

Machine Learning Lifecycle brings numerous benefits that translate into higher quality, efficiency and reliability of machine learning-based solutions:

  • Structure and organization – MLL introduces a clearly defined structure and steps that lead from data acquisition to model implementation, which minimizes chaos and increases efficiency.
  • Better model quality – thanks to MLL’s systematic approach, each phase – from initial data analysis to model validation – is carefully planned and executed. This makes models more accurate and reliable, resulting in better business results.
  • Increased efficiency – a structured approach allows for better management of resources and minimization of waste. This makes it easier to identify and eliminate unnecessary activities, leading to time and cost savings.
  • Scalability – MLL allows projects to scale more easily. Clearly defined procedures and standards enable faster implementation of new models and adaptation to the growing needs of the organization.
  • Flexibility – MLL’s regular model monitoring and maintenance enables rapid response to changes in input data or production environment, so models can be continuously updated and adapted to new conditions.
  • Replicability and change tracking – documenting and managing versions at each stage of the lifecycle makes it easy to track changes and replicate results, allowing the process to be replicated in the event of a need for an audit or further model development.
  • Ensuring operational continuity – with MLL, organizations can better manage operational continuity in the event of model failures or changes in IT infrastructure. By documenting the process and procedures, and using quality control practices, system performance can be quickly restored to normal.
  • Security and compliance – MLL allows security and regulatory compliance standards to be implemented at every stage of the process, so that it can be operated in accordance with current legal and regulatory guidelines.

Graphic 2. Benefits of Machine Learning Lifecycle

MLL or off-the-shelf solutions?

The decision between using off-the-shelf Machine Learning models and using full lifecycle ML models depends on various factors.

Off-the-shelf ML models are very attractive because of their speed of implementation and ease of use. They are ideal for rapid testing and deployment, especially in situations where time is critical. Many platforms offer APIs for direct use of the models, which greatly simplifies integration, even for those with limited technology knowledge. In addition, using off-the-shelf models can be cost-effective: it eliminates the need to invest in advanced computing infrastructure and hire highly skilled professionals.

Despite their many advantages, ready-made models also have their limitations. They may not be properly tailored to the specific needs and data of a particular project, which in turn can affect their effectiveness. Instead, depending on the area of application, a dedicated model can take into account numerous variables, such as, but not limited to: ambient temperature, pressure, weather, changes in currency exchange rates, availability of raw materials and many others. And all this in any combination.

What’s more, with off-the-shelf models, users, even those with more advanced technological knowledge, have limited ability to modify and optimize models, which can be a challenge in more advanced projects.

„The use of models based on the full ML lifecycle allows better adaptation of models to the specific needs of the organization. It gives full control over every stage of the model lifecycle – from data collection and processing, to model training, to implementation and monitoring. This makes it possible to optimize and improve models in response to changing requirements and data. However, a full ML model lifecycle requires more time and resources for development and implementation. It also requires a team of specialists and advanced technical infrastructure, which can be a barrier for some organizations, so choosing the right approach should depend on the specific needs, resources and goals of the organization.

says Marek Mirowski.

Is investing in MLL worth it?

Understanding and effectively managing each stage of the Machine Learning Lifecycle is key to success in AI projects. Organizations that invest in the development of MLL competencies can expect better business performance, greater innovation and competitive advantage. Correctly defined and managed MLL can lead to better quality models, faster implementation and more consistent results. Organizations that adopt MLL can more effectively anticipate trends, optimize operations and improve customer satisfaction.

MLL’s systematic approach enables better management of resources, minimizing the risk of errors and responding more quickly to changes in data or the business environment. All this makes MLL a key element in achieving success in Artificial Intelligence and machine learning projects.