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Life Cycle of a Machine Learning Model

  • Ashesh Anand
  • Apr 19, 2022
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Machine learning (ML) is becoming increasingly popular, resulting in an influx of novices to the field.


Many of these newbies believe that once they get the data and computer resources needed to start training models, an ML project will be quite simple. Every stage of machine learning model development, deployment, and performance monitoring is covered by the machine learning lifecycle. 


This comprises everything from the initial creation of the model as a solution to a problem faced by an organization to the continual optimization required to make a model accurate and effective. Machine learning models can deteriorate over time as a result of a variety of reasons, such as the data's external context moving. 


To achieve a continual cycle of improvement, a model should be rebuilt and optimized on a regular basis to resolve observed model shifts or biases.


The process of developing a machine learning model is lengthy, but the journey does not end after the model is implemented. The machine learning model lifecycle encompasses more than just the first stages of model research and implementation. 


It should also entail tracking the health and performance of the model once it has been implemented in a real setting. Steps to embed the model in the larger organization, as well as crucial features like model governance and management, are all things to think about.


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What is a Life Cycle?


A project's steps (or phases) are described using a life cycle. In other words, a team that follows the life cycle will have a standardized vocabulary for describing the work that has to be done.


While data scientists and machine learning engineers can usually explain the steps in a project, they may not use the same language or even define the same number of phases. The team can better ensure that they do not "skip a step" by using uniform terminology.


While you might expect that experienced team members would be aware of the procedures and would not skip them, teams can simply skip them. 


When a team has a deadline, for example, we've frequently observed that the team finishes one model and then moves on to trying to construct a different model without first examining how well the previous model functions. This could be due to tight deadlines or the team's desire to "play with the data" by exploring a variety of models. 


Aside from guaranteeing that the team does not skip a phase and having a consistent vocabulary, there is another advantage to using a life cycle, that non-technical persons, such as a product owner or a senior management, can better comprehend the work that has to be done and how long it will take to complete the project.


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Machine Learning Model Lifecycle


This guide delves into the fundamentals of the machine learning model lifecycle, discussing the many stages and their implications. 


  1. Define the Project's Objectives


The emphasis and scope of the project should be defined and planned in the early stages of the machine learning model lifecycle. The first step should be to clearly define the problem that a machine learning model will assist in solving. 


Models are increasingly being used to tackle commercial and organizational problems in a variety of settings. Machine learning will be the best solution for the problem if the objectives are clearly specified. Otherwise, the issue could be resolved with less resource-intensive methods.


Understanding the system environment in which a model will be integrated, as well as the data that is accessible within the organization, will be useful at this stage. 


A machine learning model, like any other system or programme, will need to be mapped within the organization's network to understand potential cybersecurity risks or dependencies. Because machine learning relies so heavily on data, the source and type of data should be specified as well. 


The sort of machine learning model chosen and implemented will be influenced by the project's overall goal and the type of data available. All decisions should be carefully documented so that everyone in the organization understands the risks and benefits of constructing a machine learning model. 


Defining the project's objectives and goals early on will assist keep the project on track and define model success once it's deployed.


  1. Data Collection


The initial phase in the machine learning life cycle is data collection. This step's purpose is to identify and collect all data-related issues.


We must first identify the numerous data sources, as data can be obtained from a variety of places, including files, databases, the internet, and mobile devices. One of the most crucial stages in the life cycle. The output's efficiency will be determined by the quantity and quality of the data collected. The more data there is, the more accurate the prediction will be.


The following tasks are included in this step:


  1. Determine the various data sources.


  1. Gather information


  1. Combine information from various sources


We obtain a cohesive set of data, also known as a dataset, by completing the aforementioned task. Regardless of the type of machine learning model chosen, high-quality data is an essential component of a successful machine learning model. 


To learn and train, models rely on vast amounts of high-quality data. Because data sets are required to both train and assess a model's effectiveness, high-quality data is essential. The first step is to ensure that a dependable data source is available. 


The type of data provided should have been determined earlier in the process, as it has a direct impact on the machine learning technique that is required. The level of preparation needed will depend on the machine learning algorithm selected.

The image depicts the life cycle of a Machine Learning Model and the steps involved are Gathering Data, Data Preparation, Data Wrangling, Analyzing Data, Training the model then testing it, and at last deploying the model.

Steps involved in the Life Cycle of a ML Model


  1. Data Pre-Processing


It is quite usual to undertake a set of data preparation steps after data has been picked and compiled. Dealing with artifacts like duplicate data, missing values, normalization, augmentation, and quality control are all part of this. 


Cloud computing can help with preprocessing by facilitating the creation and execution of pipelines or workflows for more transparency, reproducibility, and resilience. Many workflow solutions support declarative analytic pipeline specifications and have the ability to run workflows on both public and private clouds. 


Preprocessing processes can be specified in a Notebook that is deployed on cloud resources, and this method has the added benefit of providing visual interpretations. Preprocessing becomes reproducible, portable, and scalable when all preprocessing stages are automated and run in the cloud.


The process of cleaning and turning raw data into a usable format is known as data wrangling. It is the process of cleaning the data, selecting the variable to utilize, and changing the data into a suitable format for analysis in the following phase. It is one of the most crucial steps in the entire procedure. To overcome the quality issues, data must be cleaned.


  1. Model Training and Evaluation


This stage is all about building a model from the data you've provided. A portion of the training data is utilized at this stage to discover model parameters such as polynomial coefficients or weights in machine learning, which help to minimize the error for the current data set. 


The model is next tested using the remaining data. These two procedures are usually done several times in order to improve the model's performance. Machine learning models learn using training data, which is usually collected offline or locally. 


The training stages for various machine learning algorithms will differ. The model learns from unlabeled data in unsupervised machine learning, which is typically used to cluster data or detect patterns. A model will learn from a labeled data set generated by a data scientist, containing labeled input and output data, using supervised machine learning. 


Typically, the supplied data will be divided into training and testing datasets. The model will be trained on the larger data set before being tested on the previously unseen data. This is how cross validation works. Cross validation is an important aspect of evaluating a model's generalizability before deploying machine learning. 


A key goal of the training process is to improve a model's ability to function with new and unknown input. Although a model may achieve high levels of accuracy on training data, it may not be as accurate when deployed to a real environment with fresh and unknown data. 


Because the model was overfit on the training data, it may be unable to detect patterns in fresh and unknown data. Other potential model performance issues are addressed before deployment, such as:


  • Excessive Resource Requirements


The model can use a lot of memory or take a long time to process. To improve the model's performance, software developers and data scientists can collaborate on the challenge.


  • Inadequate Performance


The cost of implementing the approach may surpass its business benefits. As a result, The model may be unable to reliably determine the accuracy of its own predictions. If false positives are costly to the process, this may necessitate human review of all predictions or the model may not be very accurate, resulting in limited benefits.



  1. Deploy the Model in a Real-World Environment


The model's deployment environment is important since it may usually be scaled to analyze less or more data based on the resources assigned to it. For this reason, containers have become a popular approach to install machine learning models. 


Even though the containers may be drawing resources from a variety of settings and systems, a containerized strategy can create a consistent and scalable environment for the model. It's also easier to update different elements of the model with this method. 


Another factor to consider when deploying a model is ensuring that it is appropriately integrated into the organization. This could entail launching a successful communications campaign to inform the rest of the organization about the deployment.


The final considerations are similar to those that apply to any software deployment. Because model deployment is frequently handled by a different team than model development, code must be explained in a clear 'read me' file to help deployment. Before going live, the code should be cleaned and tested to ensure that it is legible outside of the training environment.


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Obstacles Faced in managing the Machine Learning Lifecycle


  1. Manual Labor


Every stage, as well as the transition between them, is done by hand. Data scientists must manually collect, evaluate, and process data for each application. 


They must study their earlier models in order to build new ones, which must be fine-tuned manually each time. To avoid performance degradation, a significant amount of effort is devoted to model monitoring.


  1. Disconnection between teams


Machine learning models can be built by data scientists on their own. However, according to a 2020 Algorithmia analysis, 55 percent of organizations using machine learning models have yet to put one into production. 


This is because deploying a machine learning model for a commercial use case successfully necessitates collaboration between data scientists, business professionals, designers, software engineers, and other teams. The deployment process becomes more complicated as a result of this partnership.


  1. Scalability


It gets more difficult to manually oversee the entire process as the size of the data or the number of installed machine learning models grows. 


To create, maintain, and monitor each model, several teams of data scientists may be required. As a result, an organization's ability to scale up its machine learning applications while relying on human processes has a limit.


To sum up, we've gone over the major stages of machine learning projects and attempted to provide a high-level picture of each. We are convinced that a thorough understanding of these stages, as well as the parties involved in each, can lead to a more healthy and successful ML solution development process. 


Only a small percentage of businesses that try to incorporate machine learning (ML) into their operations succeed in putting a model into production. 


An ML model's lifespan isn't simple; it necessitates iterations between data and annotation enhancements, model and training pipeline development, and sample-level evaluation. Fortunately, a plethora of tools have been developed to assist in each phase of the process.

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