Build one or Buy one? What to do while selecting a Data Annotation tool?

Now that you have decided to implement a machine learning model in your business after carefully analyzing the problem and gathering enough data for training your model, you are likely thinking to segregate the raw data, cleaning it, and annotating it to feed into the machine.

You come to three solutions: Setting up an in-house team, Crowdsourcing, or Outsourcing a Vendor for your annotation work. Which is better and why, what are the pros and cons, do we buy one or we build or set up one? Following are the comparisons on various criteria on what fits and when to apply it.

• Reliability and Continuity - Outsourcing or buying one will give you continuous access to the dedicated teams while building one will make you dependent on internal resources to run a solution thus adding extra costs, though reliable
• Integration and usability - building one will consume time and effort for deploying and integrating with the current systems while buying one has the expertise and tools which are user friendly and can accommodate multiple formats.
• Scalability - With no hassle on whom to hire and having worked on multiple projects with the right expertise outsourcing frees you from the troubles and with increased accuracy and Quantity, a business can scale and deploy its model quickly and obtain results in a stable manner.

• Costs involved and security - Buying one lets you start immediately without any loss of time and money which is involved in setting up an in-house team, similarly lets you leverage security protocols which are obtained easily with third parties than creating one back at the office.

Crowdsourcing is cheaper means to do your annotation work, it's helpful for very small projects which last for a week or so but there is no security, extreme quality checks are needed and no reliability on when the data will be annotated.

Visit us at annoberry.com for services and more if you are willing to buy one rather than build one.