Understanding the Machine Learning Life Cycle
The Machine Learning (ML) life cycle represents a strategic methodology in undertaking a successful ML project. The primary goal is to formulate efficient solutions to identified problems. The key to the entire process is the understanding and acknowledgment of the problem before initiation. This comprehension ultimately determines the success of the end results. Developing a machine learning model is the fundamental step in addressing the issue throughout the life cycle. The model construction entails exercising a method known as "training".
The Stages of the ML Project
The ML project features seven primary stages.
Data Collection is the first milestone in the ML life cycle. The aim is to identify and address related data issues. The step requires recognising multiple data sources as the data could be sourced from diverse channels such as documents, databases, web, and mobile gadgets. This stage's importance can't be overstated in the life cycle. The data quality and volume that's procured directly impact the efficiency of the final output. Having substantial data improves precision. The essential tasks in this phase include identifying multiple data sources, gathering, and integrating data to form a collective dataset, which then comes handy in subsequent stages.
Preparation of Data
After the data acquisition, it's primed for additional processing. It involves positioning the data correctly and preparing it for machine learning training. Initially, the data is aggregated, and then its sequence is randomized. This step typically includes data classification and pre-processing.
Data Manipulation (Data Wrangling)
Data Manipulation, or data wrangling, is about converting raw data into a format that's ready for usage. It involves cleaning up the data, deciding which variable to use, and transforming the data into a format suitable for analysis in the forthcoming stage. Sometimes, the acquired data may contain missing values, duplicates, or invalid data noise, which could negatively impact the ML model's quality. Therefore, detecting and eliminating such issues is crucial.
The Data Examination stage involves forwarding the cleaned and prepared data to the analysis process. It includes selecting analytical methodologies, building models, machine learning model monitoring, and eventually generating a machine learning model. The model analyzes data using various analytical techniques and reports the findings. To obtain relevant information and construct the model, various ML techniques are employed.
Training the Model
The Training process equips the ML model for better problem-solving. Using datasets and diverse ML techniques, the model is trained to understand patterns, rules, and characteristics.
Testing the Model
In the Testing stage, the accuracy of the model is validated against the project requirements. It helps to determine the model's correctness percentage.
Implementation and Real-world Deployment
Finally, during Implementation, the trained model is deployed in a real-world system, marking the completion of an ML project. The model is only launched when it fulfills the mandatory requirements and yields accurate results at an acceptable pace. However, despite the real-world deployment, it's critical to continue evaluating its performance using available data. This final stage is akin to the completion of a project's final report.