Model Development and Model Operations in Machine Learning.

The same importance as the initial model design by selecting the appropriate dataset is the management of the machine learning models and producing a high-performing model. Machine learning operations (MLOps), which support the data science teams in making highly performing models, are based on the ideas of model retraining, model versioning, deployment, and monitoring.
In enterprise data analytics applications, the use of machine learning to extract meaningful information from corporate data has increased dramatically. An ecosystem is essential for creating, testing, deploying, and overseeing enterprise-grade machine-learning models in practical environments. Gathering data from several trustworthy sources, processing it to make it suitable for building a model, selecting an algorithm, building the model, calculating performance metrics, and choosing the best-performing model are all necessary.

Model Development

The term "machine learning" (ML) model lifecycle" describes the entire process, from identifying the source data to developing, deploying, and maintaining the model. All operations may be broadly categorised into two groups, like ML Model Development and ML Model Operations.

• Developing an ML model involves several processes, broadly categorised as data exploration, model creation, tuning model hyperparameters, and selecting the best-performing model, as stated in The ML model development lifecycle steps.

• Model properties, such as feature importance and correlation matrix, show which features are most closely associated with the target variable. These techniques identify cases of collinearity—two variables that exhibit strong correlation and share information about variance within a dataset—between them. The data of the Variance Inflation Factor can be used to identify multicollinearity in models containing three or more positively linked variables.

• To create an ML model, data must be split into two sets, a testing set and a training set, using an 80:20 or 70:30 ratio. A range of supervised (for labelled data) and unsupervised (for unlabelled data) algorithms are available for selection, depending on the type of input data and business consequence to be predicted.

• It is advised to use CNN for image recognition and recurrent neural networks (RNN) for speech recognition and natural language processing (NLP). For other undeveloped (voice/image) data types, artificial neural network (ANN) approaches are also recommended. To make predictions, a training dataset is utilised, and the model is constructed using a test dataset.

• Regression models (ML models) are better than deep learning (neural network) models because the latter provides an additional layer of non-linearity through the use of an activation function (AF).

• Computation of Model Performance is the next logical step to choose the suitable model. The model performance metrics will decide on the final model selection, which includes computation of accuracy, precision, and recall; for classification models, the addition to the confusion matrix and coefficient of determination for regression models.

• It is not recommended to use accuracy to determine the performance of classification model training with unbalanced or skewed datasets; instead, it is advised to compute precision and recall to select the right classification model.

Similarly, to prevent underfitting, increase model complexity such as moving from linear to non-linear, adding more hidden layers (epochs) to the neural network or adding more features that introduce hidden patterns. However, more data volume is needed to solve the problem of underfitting. Rather, it hampers the model's performance.

Model Operations

Machine Learning (ML) Model Operations is used to describe the process of putting in place measures to keep the ML models in production settings. The typical enterprise situation has the frequent difficulty of ML models developed in a lab setting, sometimes remaining in the proof-of-concept stage. When a model is put into production, it gets stale since source data changes frequently and new models must be created.
Monitoring model performance along with corresponding features and hyperparameters used for retraining the model is essential as the models are retrained several times. The ML model operations lifecycle process, depicted seamlessly, encompasses the stages of model development, deployment, and performance monitoring.
The model stage transfer, for example, from staging to production to archive, is made more accessible by the model metadata store. The model is trained in one environment and then deployed to other environments, where the model file path must be specified to achieve model inference. Model experiments are tracked and compared for their performance using the model metadata store. The training data set version and links to training runs and experiments are included in the model metadata.

Model deployment:

The models can be used in online or batch inference modes for prediction in real-world settings. By setting up a job to run at a specific time interval and emailing the findings to the specified recipients, batch inference can be accomplished. By exposing the model as a web service and using frameworks like the Python Flask library or the Streamlit library to create interactive web applications, it is possible to accomplish online inference. The model may then be invoked via its HTTP endpoint.

Packaging/Distribution Model:

To distribute the learned machine learning model for deployment into test and production settings, it is serialised into multiple forms. For ML or deep learning models developed in Python, the most popular format is pickle; for deep learning models, ONNX (Open Neural Network Exchange format) is used; and model containerisation can be accomplished by creating a docker image that contains training and inference code, as well as the required training and testing data and the model file for future predictions after a crucial ML model has been generated and packaged with the docker file.

Model performance monitoring:

This is an important job that involves regularly comparing the expected value (such as the anticipated sale price of an item) to the actual value (the actual sale price). It is advised to ascertain how the end user receives the final forecasts. In certain situations, it is recommended to maintain the old and new models operating simultaneously to comprehend the differences in performance between the two models (model validation).
In machine learning, version control of the model is essential since it must be updated often to reflect modifications to the underlying source data or to satisfy audit and compliance requirements. Within the code repository, versioning occurs for the source data, model training scripts, model experiments, and trained models. For model version control, Git is the underlying code repository in several open-source applications, such as Data Version Control (DVC) and AWS CodeCommit.

Roles in ML Project Development:

Implementing ML model development and operations involves multiple responsibilities. Data engineers examine company data from various sources and ensure proper and up-to-date data is accessible at the required granularity and cost-effectively. Data Scientists look at the data, perform data preprocessing and feature engineering, model building, and choose a suitable model that best fits the predictive/prescriptive requirements of a business. Usually, Data Scientists take a hypothesis-based approach to select the model that fits the requirements.

ML Model Deployment Best Practices:

The suggested model deployment best practices are as follows:

• Use DevOps tools to automate the necessary steps for ML model development and deployment, allowing for additional time for model retraining;

• Carry out continuous model testing, performance monitoring, and retraining following production deployment to ensure the model remains current and relevant as source data changes to predict the intended outcome(s);

• Put logging into practice while making ML models available as APIs; this includes recording input features and model output (to monitor model drift), application context (to troubleshoot production errors), and model version (if several retrained models are used in production);

• Keep track of all model metadata in a single repository.