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Undeniably, data labelling is one of the main cores of artificial intelligence to teach techniques in building robust training model for the machine to interact and respond to every action that is being given Now, let us know what comes after getting labeled data.

 

Must haves in storing labeled datasets

1.       Identifying what to solve

Having a good perspective of your machine learning goals is the first step in taking on a storage solution for labeled data sets. An established picture of your desired outcome will be an equipment to identify the best storage solution that would fit your needs.

Storage capacity is one part of the equation. Considering a more integrated storage solution which includes not only a manageable amount of storage but also the necessary processing power for handling your training requirements.

One must consider the logistics of your data use, when the labeling of data is done, the data pipeline should still be considered present and flowing, as a result, a storage solution with high bandwidth scale-ability is preferred.

 

2.       Saving raw copies of unlabeled data

Keeping the raw copies of unlabeled data is more practical, just in case you decided to re-label them. If your models come up empty and identify labeling method at one point in the future, this situation is necessary.

Regardless if it is for redundancy or retraining, keeping the raw copies of unlabeled data is always a good idea for backups.

 

3.       Separating algorithms and data storage

Moving your algorithms before moving your data is a must practice for large datasets, wherein moving your data between servers can consume a lot of your time and resources. Considering that algorithms are much leaner than data.

 

4.       Using compatible formats when storing labeled data

Working across multiple formats can be tedious and time-consuming especially if it is compressed in a different type or exclusive to a certain platform. Maintaining compatibility and consistency and standard formats when storing labeled data is essential

Every project will dictate which format will be used – from csv, xlxs, plain text, and/or jpeg – but trying to maintain one is highly recommended.

 

TIP: Make sure your format is compatible across platforms to ensure future migration or scaling.

 

5.       Backup!

Data is the most valuable material in this case, that is why data managers are avoiding data loss, since it engraves a real threat that can quickly kill your machine learning efforts.

 

6.       Establishing minimum data requirement

High-accurate model could only be achieved when establishing minimum data requirement. This is also a great way to establish a general understanding of your storage requirements. Hence, knowing the minimum of what you need should enables you to predict and allocate more realistic costs and resource consumption.

 

7.       Storing variables that provide context

Computers struggle with classifying contexts of an image which results to continuously teach the nature of a particular context.

It is crucial to add variables and labels which provides the correct context. This practice is one of the best ways to ensure seamless and accurate classification of your data in the future. Easy classification could help you save on processing power and avoidance on the possibility of having to retrain your model.

 

8.         Sentence-Level Classification

It is typically a good starting point for assigning the right content to your data under text classification. This is usually done in a complex sentence which could be interpreted in different ways. Sentence-level classification is an ideal medium in providing speed and accuracy, especially for larger datasets.

 

9.       Scale Up!

As you grow and identify new solutions for your model, storage needs is expected to change. It is necessary for machine learning models to grow and adapt over time as more data comes through.

Upon reaching the limits of an existing storage solution, you` will be torn between expanding, scaling back or starting from scratch depending on the results of the previous training data. These practices are being employed in storing labeled data, hence, adapting will be seamless.