3 reasons why you need to outsource data labeling.
1) Teams want to invest time in ML models, not in data-centric operations
2) You care about the amount and quality of labeled data
3) The entire data annotation process involves a lot of steps
⬇️
1) Teams want to invest time in ML models, not in data-centric operations
2) You care about the amount and quality of labeled data
3) The entire data annotation process involves a lot of steps
⬇️
1) The majority of the time invested in an AI project is allotted to data-centric operations
Data labeling methods keep being increasingly important to the success of ML solutions.
The process can be overwhelming. Especially for startups and small companies.
Data labeling methods keep being increasingly important to the success of ML solutions.
The process can be overwhelming. Especially for startups and small companies.
2) You care about the amount and quality of labeled data
The success of supervised learning depends extensively on these parameters.
Labels guide the ML model in the right direction such that it can classify unseen samples accurately.
The success of supervised learning depends extensively on these parameters.
Labels guide the ML model in the right direction such that it can classify unseen samples accurately.
3) The entire data annotation process involves a lot of steps
A general workflow:
1. Decomposition
2. Instruction
3. Interfaces
4. Quality control
5. Pricing
6. Results
7. Project Maintenance
A general workflow:
1. Decomposition
2. Instruction
3. Interfaces
4. Quality control
5. Pricing
6. Results
7. Project Maintenance
In our new Edge, we overview @TolokaAI's new service tailored for startups and teams in the early stage of their AI production.
We highly recommend you to read our review of Toloka App Services. It covers many important aspects.
thesequence.substack.com/p/edge138
We highly recommend you to read our review of Toloka App Services. It covers many important aspects.
thesequence.substack.com/p/edge138
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