How To Ensure Data Quality For Machine Learning And AI Projects?
A poor quality training data for your machine learning model is not good from any angle. And until you feed the right data your Artificial Intelligence (AI) or Machine Learning (ML) model will not give you the accurate results.
If you train the computer vision system with incomplete data sets it can give disastrous results in certain AI-enabled models like autonomous vehicle driving.
And there are different types of training and testing data for machine learning is used to train the computer vision supported algorithms to create such AI models.
And to generate the high-quality training data for machine learning or AI you need a highly skilled annotators to carefully label the information like text, images or videos as your algorithm compatibility to make the perception model successful.
And consistency in providing the high-quality image is more important, and only well-resourced organizations can provide such consistent data annotation service.
Actually, there are few quality control methods discussed below you can use to ensure the quality of data for your machine learning or AI project.
STANDARD QUALITY-ASSURANCE METHODS
Benchmarks or Gold Sets
This process helps to measure the accuracy by comparing the annotations to a “gold set” or vetted example. And it helps to measure how much a set of annotations from a group or individual meet the benchmark.
Overlap or Consensus Method
This process helps measure the consistency and agreement among the group. And it is done by dividing the sum of agreeing data annotations by the total number of annotations.
This is one of the most common method of quality control for AI or ML projects with relatively of annotations objective rating scales.
Auditing Method
The auditing method of checking the quality of training data measures the accuracy by having review the labels by experts either by checking on the spot or by reviewing all.
This method is crucial for projects where auditors review and retread the content until and unless it reaches the highest level of accuracy.
DETAILED FOR IN-DEPTH QUALITY ASSESSMENT
To monitor the quality of data annotations, these baseline quality measurements are one of the solid method.
But if AI projects are different from each other, then organizations need to establish quality assessments in customize way to a specific initiative.
And only highly-experienced leaders can organize the in-depth quality control analysis by considering the process discussed below.
Multi-layered Quality Evaluation Metrics
This method is multiple quality measurement metrics helps to leverage the methods of quality measurement which has been already discussed.
It can ensure to maintain the accuracy level at best while not delaying the project.
Weekly Data Deep Monitoring Process
Under this method a project management team is implemented to examine the data on weekly basis and also set the expanded productivity and quality score.
For an example, if you need 92% accurate data, you can set goal at 95% and try to ensure the annotation process exceed your goals.
Management Testing and Auditing
To build the quality-assurance skill set of your project manager you can ask them to carry out annotation work and quality audits to make them get first-hand experience of annotation process.
This method helps management team a 360-degree view of the projects and a full understanding of the entire annotation process.
Get High-Quality Training Data for Unbiased Decisions
The high-quality training data ensures more accurate algorithms, and it can also rationalize the potential bias in different types of AI projects.
Bias can distinct as uneven voice or facial recognition performance for different types of genders, speech pattern or ethnicities.
During the data annotation process, fighting bias is another way to launch your training data set with best level of quality.
Hence, to avoid biasness at the project level, organization need to actively build diversity into the data teams defining goals, metrics and roadmaps and algorithms used to develop such models.
As, hiring a data talent team is easier said than done but if the composition of your team is not representing the population your algorithm training will be affected.
Hence, the final product risk only working for, or engaging to, a subset of people or be biased against certain subsets of the population of a single class.
Yes, there is not doubt, unavailability of high-quality training data is one the prime reasons for AI & ML project failure.
However, there are numerous quality assurance process vital for the AI development. Hence, quality training data not only good for the algorithm training but also helps to make the model work in real world.
Companies like Anolytics are providing the high-quality training data services for computer vision to build a model through ML or AI.
It is offering the image annotation service to annotate the different types of images to supply as a training data for different sectors healthcare, retail, automotive, AI in agriculture and autonomous robotics machines to work with right performance.
This story was originally featured on www.anolytics.ai
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