Labeled data is a group of samples that have been tagged with one or more labels. labeling typically takes a set of unlabeled data and augments each piece of it with informative tags.
Labels can be obtained by asking humans to make judgments about a given piece of unlabeled data. Labeled data s significantly more expensive to obtain than the raw unlabeled data.
Labeling training data acts as the first step in the machine learning development cycle under Computer Vision. Consider we need to train a machine learning model to identify the specified category of objects from the collection of data.
We would need to collect representation data samples which has to be classified and analyzed along with a Machine Learning algorithm for handling each sample.
Data Quality & Accuracy
Data is generally considered high quality if it is “fit for intended uses in operations, decision making, and planning”. People’s views on data quality can often disagree, even when discussing the same set of data used for the same purpose. Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing activities such as removing outliers, missing data interpolation etc. to improve the data quality.
Data accuracy is its trueness and precision. For any Digital Enterprise, data accuracy is the backbone. More and more enterprises are now seeing data as not only a tool for success but data as an asset. They understand the importance of managing their data effectively to produce much better results with more accuracy and quality.
Model performance is often derived by the quality of the data that is being used. The major factors that can affect the data labeling quality are context, agility, relationship, and communication. The more the data is worked on, the more context is established, and a better understanding of a data model is derived.
While the Data Labeling service is outsourced, it is in the best interest of the outsourcing team to communicate effectively with data labelers. By doing so, your internal team can always have a heads up on updates and it will be much easier for your team to quickly incorporate changes or iterations to data features being labeled.
It is mandated for the outsourcing firms to collaborate and work with the offshore team. This will help both the teams to actively manage and deliver higher skill levels, engagement, accountability, and quality. Making the offshore teamwork more and more on the data that is provided to them helps to improve the understanding of the data model, increase efficiency and improve overall output quality. The more the quality of data is, the more accuracy of the output will be.
Scalability is one of the important aspects to look for while selecting an offshore team. Picking a team/partner that offers flexibility and elasticity in providing resources helps you big time if there is a surge in need of resources or during a cool off period where few resources are not be actively involved in the team.
The flexibility with resources, time zone, expertise will be of a greater asset in meeting the deadlines without compromising on data quality.
A dedicated team of members is always a welcome sign. This dedicated team shall be treated as the core team with the option to add resources on a need basis. The standby resources can also be evaluated by your internal team and the offshore team should be always ready to replace a resource that doesn’t fit into the profile and team.
While outsourcing your data labeling, selecting a partner/offshore team that is versatile and has expertise in using multiple tools. A team that works efficiently with data labeling tools will be the one to provide better results with lesser time and not compromising on the quality. If you can find a team/partner that can work on their indigenously developed tool rather than relying on 3rd part tools is an added advantage. At OptiSol, we use our own model/tool “Saivi” that covers end-to-end data-related services and our expert consultants can help all the way from sourcing to visualization!
Secure and Safe
For any application, transactions, and exchange of information – making it safe and secure will be the primary focus. When it comes to data and data-related work applications, it is in the best interest to have it more secured and safer.
Finding a partner/offshore team that can comply with regulatory requirements, based on the level of security your data requires is as good as developing your data project. Documentation of data security approach for their workforce, technology, network, and workspaces is mandated.
IP and NDA are to be executed well before any piece of data are to be shared with the partner/offshore team. This will make sure that your labeled data will not be shared or used for other projects. Gather minute information and details on the ways the partner/offshore team can secure your data and the ways and measure they have in place to secure their work premises.