What are the career prospects of a data annotator? What are the compensation, trends, and opportunities? Find out in this post.

In our earlier post, we explored what the data annotation job was about. Now, we explore what the career is about:

Here's a quick overview:

• Career classification and job requirements
• Compensation, trends, and opportunities
• Advice on getting started

Data annotation can be a good additional source of income or your full-time job, depending on you. Learn more. 

How to scale the data annotation process in AI/ML projects? We discuss 5 best practices.

The game is vastly different at scale. Scaling data annotation brings its own challenges. How to scale at all?

Today we cover:

• Data labeling challenges at scale
• 5 best practices for scaling data operations
• Key takeaways

Scaling is a beast that you need to tame iteratively. Follow the guidelines. Learn more. 

Why are there so many data annotation platforms? How did they rise in the last 10 years?

There are many data annotation platforms available, both open-source and proprietary. How is so?

In this post:

• Need for processed data to train AI
• Difficulty of annotating data manually
• Review of top data annotation platforms

Data platforms have risen due to the rise of AI and need for processed data.

Learn what the Segment Anything Model is and its use cases with a hands-on labeling tutorial.

Segment Anything Model? Magic Touch? Segmentation? We cover all in our latest guide, with a hands-on tutorial:

In this post:

• What is SAM?
• Why SAM?
• Demo project with SAM

SAM is a game-changer for image labeling because of its flexibility, accuracy, and coverage. Learn more. 

How computer vision can be, and is being, used in the insurance industry.

Insurance is a trillion-dollar industry that still relies on manual procedures and paperwork. Why and how to use CV to improve the workflows?

In this post:

• What is insurance?
• 7 use cases of CV in insurance?

New technologies, AI, ML, and CV, hold the key to improving the workflows by enabling humans to operate at their prime.

Learn essential terms of computer vision, Part 2.

We shall continue with our post and define the next 25 essential terms related to computer vision. We now focus more on details of computer vision: data types, models, and datasets. Learn more. 

Learn essential terms of computer vision, Part 1.

The first step towards understanding a field is to be familiar with its core terms. Computer vision is no exception.

Here's a quick overview:

• Most common terms
• Glossary and examples

Familiarize yourself with these terms so that you can have an easier time understanding more complex terms. Learn more. 

What is bias in AI/ML models? How do they happen and affect the performance and accuracy?

There is bias in your data that affects your AI/ML model performance. But how and why?

In this post:

• What is bias in AI/ML models, essentially?
• What are types of bias in your data and annotation?
• What to do?

Bias is an systematic, invisible assumption or error that hinders your AI/ML model. Learn more. 

How data annotation works at scale? How is it different?

Everything changes at scale. It takes a giant leap from an ordinary activity to a great scale. So how does data annotation work then?

In this post:

• What scaling brings?
• Scaling in AI
• Leveraging auto-annotation

Scaling is a very big, multi-faceted challenge. But, using auto-annotation tools with human oversight is quite beneficial. Learn more. 

Data is the new oil - what exactly does this mean?

The phrase, "Data is the new oil" has become almost a buzzword. But it really is?

In this post, we explore:

• Why processed data is the new oil
• How labeled datasets power AI
• Why processing data is difficult

Data powers the data economy and AI. However, raw data is not useful in and of itself. Find out more.