AI/ML Introduction: Episode #12: What is a Machine learning life-cycle?
Machine Learning is a major driving force behind much of the technological progress we are making today. It is an advanced form of artificial intelligence that uses algorithms to recognize patterns, make educated decisions, and improve over time.
While many people are familiar with its capabilities, far fewer understand the process and life cycle it takes to create a successful machine learning system.
So what is a machine learning life cycle?
Put simply, it’s a set of structured stages designed to allow organizations to efficiently develop, deploy and refine AI-driven applications — all while minimizing operational risk.
In this blog, we’re going to look at the end of end machine learning life cycle methodology, as well as the different stages it involves.
The machine learning life cycle is the cyclical process that any data science projects follow.
They broadly come under two category, Data and Model and each of them have a series of steps that we need to take when building a machine learning model.
Any machine learning project starts of with:
#1: Defining the requirements of the model:
Defining the requirements of the model is an essential part of the machine learning cycle, as this allows us to identify the exact problem we are trying to solve and ensure that what we are setting out to do is in line with our main business objectives.
It’s important to recognize that it’s not just about solving the problem efficiently, but also doing so effectively — meaning that all of our efforts should be focused on achieving tangible results from our models.
To ensure this, we need to establish success metrics around key performance indicators (KPIs) which will help us evaluate how well a specific model performs against defined goals.
#2: Data Collection:
Next comes the data collection. Data collection is an essential part of the machine learning cycle, as it forms the basis for all subsequent analysis.
This process involves gathering data from various sources and translating them into a format that can be readily used for further processing and analysis. Specifically, it involves selecting relevant data sources such as databases, websites, APIs, and third-party services; querying those sources to extract data; and transforming the raw data into a usable format
#3: Data Cleaning:
Once we have collected the data, we need to perform data cleaning.
Data cleaning is the third step in the machine learning cycle. It involves a process of data wrangling and transforming to prepare the data for modeling. This involves several steps including deduplication, validity checks and outlier detection.
Deduplication is important to ensure that all records are unique and no duplicates exist in the dataset. This can be done by comparing values across columns, removing redundant rows and merging similar records together.
Validity checks are then performed to make sure that all data points are valid and not corrupted or missing any information. Missing values should also be identified and handled appropriately depending on the situation such as filled with an average value, dropped or interpolated. In addition, outliers should be detected and dealt with accordingly as they can potentially lead to inaccurate results when used in training models.
#4: Data Labeling:
Once our data is clean then the next most important step is:
Data Labeling process involves assigning labels to data points which have been identified as relevant for classification and prediction tasks. Labels are ultimately used to inform the model of which class or category a particular data point belongs to.
Data Labeling can be done manually or using automated methods such as machine learning algorithms and Natural Language Processing (NLP).
#5: Feature Engineering:
Apart from data labeling, we also need to create features.
Feature engineering is an important part of the machine learning cycle, as it helps create a better model and can even lead to improved performance.
The process of feature engineering starts with selecting relevant features from the dataset, which should be done carefully considering which information is most useful for predicting the target variable.
During this step data visualization techniques such as correlation plots or decision trees can be helpful to understand which features have more influence on the outcomes.
So far we focussed on the data part, now we move into the modeling part.
#6: Model Training:
Model training is an important part of the machine learning cycle. It involves feeding the algorithm with data to enable them to learn and make predictions on unseen data using the knowledge gained from the training data.
To do this, it requires collecting input data (features) as well as sample output data (labels). The features are used to construct a model based on the available information while labels are used as ground truth for measuring accuracy of predictions made by the model.
During training, the model’s parameters are adjusted and optimized until it has learned enough to accurately predict new outcomes. The process of optimization can be supervised or unsupervised, depending on the type of learning algorithm used
#7: Model Evaluation:
Model Evaluation is an important part of the machine learning cycle, which helps to optimize the performance of a model and determine its accuracy.
Model evaluation is performed after the training phase, when a model has been built and tested on training data. The main purpose of model evaluation is to ensure that the model accurately predicts outcomes on unseen data.
Once your chosen model has been evaluated, it’s important to check whether overfitting/underfitting problems exist by comparing it against multiple metrics such as precision-recall curves, ROC curves, confusion matrix etc., This would help us to discover how well our model generalizes and performs outside of our training dataset.
#8: Model Deployment:
Model deployment is an essential part of the machine learning cycle; it is the process that turns a trained model into a working application. Model deployment consists of several steps, including deploying model artifacts and integrating them with an existing application stack.
The first step is to package the model in an appropriate format, such as Python pickle, JSON or TensorFlow SavedModel.
Once this step is complete, the next step would be to deploy the model artifact to a cloud-based environment such as Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure.
Once deployed, integration with existing systems must occur through APIs or SDKs.
After these steps are complete AI application will be ready for production use!
#9: Model Monitoring:
Model monitoring is an important step in the machine learning cycle and should be conducted on a regular basis.
This process involves tracking the performance of models in production to ensure that they remain accurate and effective over time. This can be done through monitoring metrics such as accuracy, precision, recall, and F1 score, as well as other key indicators such as false positive rate, false negative rate, etc.
In addition to performance metrics, model monitoring also needs to track the changes in data over time. By closely tracking these changes alongside performance metrics, it will allow teams to work quickly if any sudden dip in accuracy is detected
Moreover, it is important that teams are able to detect any bias or unfairness present in their models.
Finally, once all of the above steps have been completed teams should be sure to document every step of their model monitoring process including all relevant datasets used for analysis and corresponding reports generated from each stage of monitoring process so that it can be tracked over time and iteratively improved upon when needed.
Machine learning life cycle is important for the success of any AI-driven project.
It gives us a framework on how an entire data science project should be structured in order to provide real, practical value to business.
These steps include data collection, preprocessing, model selection, training, evaluation and deployment as well as monitoring.
Following these steps can help teams create robust models that are capable of accurate predictions over time.
Failing to accurately execute on any one of these steps will result in misleading insights which might become detrimental to business.