At the core of any AI project lies a great deal of annotated data for machine learning. Whether the end product is a customer service chatbot or a sentiment analysis engine, anybody building machine learning models eventually requires access to a vast amount of training data.

Capturing enough accurate, quality data at scale is a common challenge for individuals and businesses alike. In this article, we outline four ways to source raw data for machine learning, and how to go about conducting data annotation.

1. Use Open-Source Datasets

The internet contains thousands of publicly available datasets ready to be used, analyzed and enriched. We at Lionbridge have personally spent hours combing the web for the best open-source datasets, available for download here.

The trouble with using public datasets is finding ones that are actually useful or suitable for your model. While public sources contain a seemingly unlimited amount of rich, detailed data, they might not be very useful for your specific end goal. Even if you obtain the right data, you will likely need to be clean and edit it before it’s anywhere near ready for input into your system.

2. Scrape Web Data

Web scraping describes the automated, programmatic use of an application to extract data or perform actions that users would usually perform manually. These tools look for new data automatically, fetching any new or updated data and storing them for future access. Web scraping presents considerable opportunities for individuals, researchers, businesses and governments to make sense of large amounts of information.

However, there are obvious and growing concerns regarding privacy and legality, especially for cases where data is collected without the knowledge of individuals.

3. Build Synthetic Datasets

Particularly useful in the absence of adequate real world data, programmatically generated data is used to obtain large enough sample sizes of data to train neural networks. One of the key benefits of using synthetic data is that you are able to clearly define a number of features, such as the scope, format, and amount of noise within the dataset. Developing an environment in which a reinforcement learning algorithm can operate can generate unlimited data streams based on the model’s actions.

The potential for synthetic data usage is obvious, but it is by no means a universal fix-all. While it is a good approach in some cases, it may not be the most viable or optimal one in terms of time and effort. As such, creating synthetic environments is oftentimes a huge engineering burden, especially when done in house.

Furthermore, while using synthetic data eliminates the risk of any copyright infringement or privacy issues, you run the risk of introducing bias in your data. Successful machine learning systems need to be able to operate in very complex real world environments. As the technology currently stands, artificial data alone is not enough to train advanced machine learning algorithms.

4. Take Advantage of Internal Data

Internal unstructured data available represents a huge opportunity for large organizations, encompassing anything from CRM to customer support tickets. This potential goldmine of information can be used to develop useful machine learning applications. Machine learning models based on internal data can be very helpful in streamlining business processes and increasing productivity.

However, using internally sourced data raises many concerns around privacy, especially when it involves customer personally identifiable information (PII). Another reason why organizations don’t take advantage of internal data is the inherent complexity of extracting and formatting data. As in the case of open-source datasets, internal data requires a great deal of preprocessing in order to make it usable for machine learning.

In-house vs. Outsourced Data Annotation

After you’ve captured enough raw data, you need to annotate it before it’s anywhere useful to train an algorithm. You can choose to annotate in-house or outsource to a firm that specializes in data annotation services.

In taking on annotations internally, you’ll need to invest in the annotation process itself. This can be anything from developing annotation tools to creating onboarding materials for annotators. Ultimately, you don’t want to handle the process in-house if you lack the bandwidth or engineering capabilities. Working with an experienced data annotation partner can make all the difference in helping you achieve maximum return on investment.

When you’re ready to gather custom annotated data for machine learning, check out Lionbridge’s data annotation services. We designed out platform to improve data quality, whether it’s sourcing the right annotators or conducting quality checks. We cover a wide range of services including linguistic annotation, audio analysis and much more. With a pool of 500,000+ contributors on our platform, we process large datasets quickly and at low cost. Alternatively, check out our guide to training data for more on building quality datasets from scratch.