Data is essential for making safe and calculated decisions. The times are precarious and the uncertainty can be dealt with by analyzing enormous amounts of data. More the data more arduous the analysis and the more accurate the predictions. That in turn results in efficient prescriptions and helps secure a path toward a stable future. The data volumes we are discussing are impossible to handle and make sense just by humans. Automated analysis tools based on machine learning and deep learning models are thus deployed in abundance. And for building and optimizing these tools the same data comes in handy.
The data we need is available in an abundance, and the same can be obtained with ease in 2022. Ethically, from paid and inexpensive open sources. However, data acquisition in machine learning is not a short process. The acquisition is followed by cleaning, normalizing, and processing the data to make it useful in model building. After the same is readied, it is used for developing analytical models and training machine learning tools. This article will concentrate on the same and accompany data in its journey through the entire lifecycle.
Table of Contents
1.Determination of the collection approach
Data for optimization and training of machine learning tools must be obtained from relevant sources and by ethical means. The damage resulting from a lack of prudence while data acquisition in machine learning is irreversible and unmitigable. Therefore, before embarking on a data collection operation the operator must understand the goal of the operation and decide upon the data acquisition approach. And determine the sources, from which, ample data can be collected and cultivated without breaching ethical and legal restrictions.
2.Data acquisition in machine learning
After the goal is decided upon, data collection commences. This phase involves collecting data from various relevant sources, both by investing and using inexpensive methods. The accuracy and efficacy of machine learning tools, depend upon the volume and relevance of data that is used for training machine learning tools. Therefore, while collecting data, a data scientist makes sure that the sources and reliable and the collected data is relevant to the task at hand.
3.Data processing
Data collected for developing and training machine learning tools can be noisy and inconsistent. Therefore, during this step,
- Repetitions in a dataset are mitigated
- Any inconsistency is addressed.
- Missing values are inferred.
- Outliers and exceptional values are omitted.
- Biases are removed.
- Variables and dimensionality of the data are reduced.
- Features and incepted or simply made prominent.
- False positives are marked.
- And the data is normalized following a plethora of paradigms.
4.Model building
Algorithms for machine learning are selected and deployed based on the goals and features of a data set. Following the analysis needs, multiple layers of algorithms can be applied. Or a single entity can be deployed for simpler applications.
5.Data formatting
Depending on the readied model and the format requirement of involved machine learning tools, the data set is edited into a suitable format. Machine learning, deep learning, and other automation entities can recognize a limited number of formats. And to deploy them for training or analysis the data set should be formatted accordingly. Today, it is possible to automate this arduous process by deploying dedicated formatting tools. Entities, that are trained with huge volumes of relevant data.
6.Deployment and validation
After the model is ready the same is deployed for the operation. Primarily, known, labeled, and normalized datasets are introduced for analysis that is already being done. As the result is already known the slightest errors in analysis can be addressed by fixing and tuning the analysis tool in question. Also after the tuning and optimization are done the model can be validated by both known and unknown datasets.
7.Visualization and documentation
After the model is prepared the same is used for a long time unless there are any relevant changes introduced to the goals and analysis approach. Therefore, it is important to keep records and document the process of deployment and optimization. Therefore, the entire process till visualization is documented for ensuring reproducibility.
