<ol><li>Formatting: The data is spread in different formats. Formatting will bring it together in one sheet. For example, customer data can come with different currencies, languages, etc. These need to be compiled under one format.</li><li>Labeling: Labeling is done to ensure the data set works for your model. For example, a self-driving car will need data labeled as pictures of cars, pedestrians, street signs, footpaths etc.</li><li>Data Cleaning: Unwanted characters are removed and missing values are dealt with.</li><li>Feature extraction: A number of features are analyzed and optimized. Features that are important for prediction are selected for quicker computation and less memory consumption.</li></ol>

Steps

Testing data is used to test the validity of the training data set. Training data is not used for testing because it will produce the expected output. The testing data set comprises of 20 percent of the total data.

Testing Data

The process of curating datasets for machine learning starts well before availing datasets. Here’s what we suggest:<ul><li>Identify the goal of AI</li><li>Identify what dataset you will need to solve the problem</li><li>Make a record of your assumptions while selecting the data</li><li>Aim for collecting diverse and meaningful data from both external and internal resources</li><li>Build a dataset that is hard for your competitors to copy</li></ul>

How to start curating

If you have a small dataset, using a model pre-trained on large datasets can be a good idea. You can use your small dataset to fine-tune it.

Small Dataset = use pre-trained model

Data is the new oil - and just as oil needs the right refining to come into perfect usage, data too needs curing. The power of your machine learning models will greatly depend on the quality of your data.

Start with Datasets

The training data set is used to train an algorithm, apply concepts, learn, and give results. Around 60 percent of data is training data.

Training Data

Validation tests are used to identify and tune the ML model.

Validation Data

ML engineers depend on data during each step of their AI journey – from model selection, training, and tuning to testing. These datasets usually fall under three categories:<ol><li>Training sets</li><li>Testing sets</li><li>Validation sets</li></ol>

Types of Datasets for Machine Learning

As AI integration across industries picks greater pace, <a href="https://www.artiba.org/certification/artificial-intelligence-certification">ML engineers </a>are confronted with a sad reality - once stakeholders identify a use case with proven ROI, they are eager to jump onto the AI ship, and data curation is not given its due importance. In a survey of 150 machine learning engineers at large companies, 41 percent say their data is too siloed.

Mistake - Don’t prioritize data curation

A dataset alone can ensure the success or failure of machine learning models. By using these data curation tips, ML engineers can prepare it to serve the exact purpose they want it to.