Data collection is the single most important step in solving any machine learning problem. However, it is also a critical roadblock for many researchers and data scientists. An inordinate amount of time is usually spent on data collection, which largely consists of data acquisition, data labeling, and improvement of existing data or models. Teams that dive head first into projects without considering the right data collection process often don’t get the results they want. Fortunately, there are many data collection tools to help prepare training datasets quickly and at scale.

The best data collection tools are easy to use, support a range of functionalities and file types, and preserve the overall integrity of data. In this article, we outline the best data collection tools for machine learning projects.

Raw Data Collection Tools

The first obstacle for many data science projects is obtaining enough relevant, raw data. For more on how to obtain raw data for machine learning, check out our article on the topic.

Listed below are tools that enable users to quickly source large volumes of raw data.

Data Scraping Tools

Web scraping describes the automated, programmatic use of an application to extract data or perform actions that users would usually perform manually, such as social media posts or images. The following companies offer an tools to extract data from the web:

Octoparse: A web scraping tool that lets users obtain public data without coding.

Mozenda: A tool that allows people to extract unstructured web data without scripts or developers.

Synthetic Data Generators

Synthetic data can also be programmatically generated to obtain large sample sizes of data. This data can then be used to train neural networks. Below are a few tools for generating synthetic datasets:

pydbgen: This is a Python library that can be used to generate a large synthetic database as specified by the user. For example, pydbgen can generate a dataset of random names, credit card numbers, company names and more.

Mockaroo: Mockaroo is a data generator tool that lets users create custom CSV, SQL, JSOn and Excel datasets to test and demo software.

Data Augmentation Tools

In some cases, data augmentation may be used to expand the size of an existing dataset without gathering more data. For instance, an image dataset can be augmented by rotating, cropping, or altering the lighting conditions in the original files.

OpenCV: This Python library includes image augmentation functions. For example, it includes features for bounding boxes, scaling, cropping, rotation, filters, blur, translation, and etc.

scikit-image: Similar to OpenCV, this is a collection of algorithms for image processing available free of charge and free of restriction. Scikit-image also has features to convert from another color space to another, resizing and rotating, erosion and dilation, filters, and much more.

Open-Source Datasets

Another way to obtain raw data for machine learning is to obtain pre-built, publicly available datasets on the internet. There are thousands of publicly available datasets spanning a wide range of industries and use cases. Here at Lionbridge, we have spent hours combing the web for the best open-source datasets, available for download here.

Data Collection Tools & Services

The majority of algorithms require data to be formatted in a very specific way. As such, datasets usually require some amount of preparation before they can yield useful insights. After you’ve collected enough raw data, you’ll still need to preprocess it before it’s useful for training a model. In addition to a data collection platform, the following companies also provide data labeling services:

Lionbridge AI provides an open platform for users to design and manage their own data collection projects. With over 20 years of hands-on experience creating custom data for the world’s largest technology companies, Lionbridge AI has built the most intuitive data collection platform on the market. The tool works for all major file types, with unique features to handle text, audio, image & video data.

The tool features an easy-to-use UI for engineers, researchers and PMs to easily manage workflow and quality. Furthermore, users can invite their own workers or hire from Lionbridge’s network of over 1,000,000 qualified contributors.

Amazon Mechanical Turk (also known as MTurk) is a crowdsourcing marketplace commonly used for data collection projects. As a requester on the platform, you can design, publish, and coordinate a wide range of data collection tasks (called HITs), such as surveys, transcriptions, and more. Amazon Mechanical Turk is a useful tool that allows you to define tasks, specify consensus rules, and define your own pricing structure.

Although it is one of the cheapest data collection tools on the market, there are several drawbacks to using the MTurk platform. For one, it lacks key quality control features. Unlike companies like LionbridgeAI, MTurk offers very little in the way of quality assurance, worker testing, or detailed reporting. Furthermore, MTurk places a heavy project management burden on requesters to design tasks and recruit workers themselves.

LabelBox is a collaborative data tool for machine learning teams. The platform provides one place for data labeling, data management, and data science tasks. LabelBox’s features include bounding box image annotation, text classification, and more.

This human-in-the-loop data platform provides its customers with services through a globally distributed contributor base. They have multiple processes embedded into their workflow to ensure accuracy and quality.

Ultimately, the success of any algorithm depends on the underlying data. A solution like Lionbridge simplifies and accelerates the data collection process, allowing machine learning teams to focus on core development. Over the past two decades, Lionbridge has collected custom training data for the world’s largest companies. Whether you need help gathering audio data for speech recognition or handwritten samples for OCR systems, Lionbridge can deliver the quality you need in over 300+ languages.