The pivotal role of data in AI models and the growth of Machine Learning (ML) is undeniable. Access to data has made ML algorithms an invaluable asset to businesses rather than a mere byproduct of their operations. Businesses have always channelized data in their decision-making processes, blending aspects like customer purchases, item appeal, and business flow trends. Now, however, the advent of ML datasets necessitates data organization.
Types of Data in ML
ML effectively utilizes two kinds of data; the training and the testing data.
- Training Data: The training set, the larger of the two, hones a neural network's understanding of feature prioritization, which ultimately helps in reducing errors in the final results. The parameters, generally encoded in tensors, form what is loosely termed the model. Exhaustive training of a neural network ensures maximum learning from these parameters.
- Testing Data: The testing set, your final seal of approval, is examined only after data training and tuning are over. Checking the neural network against this sample should confirm the network's accuracy in image detection or recognition up to a given percentage. If the results are unsatisfactory, revisit your training set and reassess the network's hyperparameters, the quality of your data, and your pre-processing procedures.
Data Transformation and Acquisition
Transforming raw data into usable datasets involves several steps. They include data collection from various available open-source datasets, the internet or artificial data producers. But it's not a haphazard collection. The data must be relevant to your business goals. After collection, preprocessing and annotation are carried out to make the data suitable and understandable for machine processing.
Choosing the Right Dataset Source
The source for your dataset depends on various factors including your business size, financial standing, and specific task at hand. One of the best strategies is to gather data directly relevant to your business goals, though this might prove to be heavy on resources. Alternatives like free, downloadable ML training datasets or automated datasets for unsupervised learning may come with their own set of challenges but are often the first choice for startups and SMEs due to cost-effectiveness.
The Magnitude of Data Acquisition
Contrary to what it might seem, the acquisition of data for your AI project is not a minor task that can be conducted in the backdrop. Gathering and handling data might be the most time-consuming part of your project due to the massive volume of the task. Therefore, understanding datasets in ML, the method of data collection, and the characteristics of a good dataset is pivotal for successful project execution.