Expertise
One of the primary factors to consider is the expertise of the vendor or team you intend to hire for your machine learning project. The team you choose should have the most hands-on exposure to data annotation tools, techniques, domain knowledge, and experience working across multiple industries.
Besides technicalities, they should also implement workflow optimization methods to ensure smooth collaboration and consistent communication. For more understanding, ask them on the following aspects:
- The previous projects they have worked on that are similar to yours
- The years of experience they have
- The arsenal of tools and resources they deploy for annotation
- Their ways to ensure consistent data annotation and on-time delivery
- How comfortable or prepared they are in terms of project scalability and more
Data Quality
Data quality directly influences project output. All your years of toiling, networking, and investing come down to how your module performs before launching. So, ensure the vendors you intend to work with deliver the highest quality datasets for your project. To help you get a better idea, here’s a quick cheat sheet you should look into:
- How does your vendor measure data quality? What are the standard metrics?
- Details on their quality assurance protocols and grievance redressing processes
- How do they ensure the transfer of knowledge from one team member to another?
- Can they maintain data quality if volumes are subsequently increased?
Communication And Collaboration
Delivery of high-quality output does not always translate to smooth collaboration. It involves seamless communication and excellent maintenance of rapport as well. You cannot work with a team that does not give you any update during the entire course of the collaboration or keeps you out of the loop and suddenly delivers a project at the time of the deadline.
That’s why a balance becomes essential and you should pay close attention to their modus operandi and general attitude towards collaboration. So, ask questions on their communication methods, adaptability to guidelines and requirement changes, scaling down of project requirements, and more to ensure a smooth journey for both the parties involved.
Agreement Terms And Conditions
Apart from these aspects, there are some angles and factors that are inevitable in terms of legalities and regulations. This involves pricing terms, duration of collaboration, association terms, and conditions, assignment and specification of job roles, clearly defined boundaries, and more.
Get them sorted before you sign a contract. To give you a better idea, here’s a list of factors:
- Ask about their payment terms and pricing model – whether the pricing is for the work done per hour or per annotation
- Is the payout monthly, weekly, or fortnightly?
- The influence of pricing models when there is a change in project guidelines or scope of work
Scalability
Your business is going to grow in the future and your project’s scope is going to expand exponentially. In such cases, you should be confident that your vendor can deliver the volumes of labeled images your business demands at scale.
Do they have enough talent in-house? Are they exhausting all their data sources? Can they customize your data based on unique needs and use cases? Aspects like these will ensure the vendor can transition when higher volumes of data are necessary.