Effective Data Collection Techniques for AI/ML Model Development

Gathering data from a variety of sources, both online and offline, and loading it into a single location is known as data collection. The most challenging element of a machine learning project can be high data creation or collecting volumes, especially when done at scale.

Machine learning models require enormous volumes of structured training data to be digested to create intelligent applications that can comprehend. To solve any AI-based machine learning problem, a substantial amount of training data must be gathered.

In This blog, we will learn about all the techniques required for effective data collection for AI/ML model development. Data collection is the most important part of running an AI/ML model as it gives you an idea of the past event and helps in the analysis, we will go through every step of the collection of the data, processing it, and then utilizing it according to the requirements. Let’s get started.

Why is it important to collect data?

Data collecting makes it possible to keep track of previous occurrences so that we can utilize data analysis to look for recurrent patterns. Utilizing machine learning algorithms to identify trends and forecast future changes, predictive models are created from these patterns.

High-performing predictive models must be developed, and since the data on which they are based must be of high quality, effective data-gathering practices are essential. The information in the data must be accurate and pertinent to the work at hand. The size of the tiger population, for instance, would not be beneficial to a debt default model, but petrol prices over time may.

Data Collection methods possessed by GTS

At GTS our team is fully dedicated to the collection process of the data in various forms, which helps AI/ML models to get the required data for the working models and helps in analysis, the data we collect manually are of text, image, video, and speech. These datasets help in solving the problem faced by companies in AI or ML.

Text Data collection

Text data gathering helps conversational interfaces be trained in a variety of languages and situations. However, the improvement of OCR Data collection for AI/ML Models is made possible by the collection of handwritten text data. Documents, receipts, handwritten notes, and other materials can all be used for text data collection for AI/Ml models.

For all types of ML/AI models, GTS offers a wide range of text data collection options. To make any computer vision project a tremendous success, GTS works hard to offer the best text collection services. Regardless of your AI model for further advancement, data-collection services are focused on generating the greatest database.

Audio data collection 

To enable robots to understand the intentions and nuance of human speech, automatic speech data collection for AI/Ml model recognition technologies must be taught using multilingual audio data of diverse types and connected with varied contexts. Conversational AI systems, such as personal assistants and chatbots, need a lot of high-quality data to train their models, including data on a variety of languages, dialects, demographics, speaker features, dialogue styles, surroundings, and scenarios.

GTS gives you access to every audio file you might ever need, in any quantity, to power your technology in any desired speech, language, or voice function. We have the resources and know-how to complete any project involving natural language corpus construction, truth data gathering, semantic analysis, and transcription.

Collection of image and video data

AI systems that analyze visual content, such as computer vision systems, must take a range of circumstances into account. The training data required for the computer to recognize images with the same degree of precision as a person is provided by vast quantities of high-resolution photographs and videos that are carefully annotated. For computer vision and image analysis services, algorithms must be trained with meticulously gathered and segmented data to assure objective findings.

For all kinds of applications involving machine learning and artificial intelligence, GTS offers a comprehensive range of image datasets for machine learning collecting and image data annotation services. When it comes to video datasets and video data collection for AI/ML models, GTS has the experience, knowledge, tools, and capacity to provide you with whatever you require.

The Approach in Data Collection Techniques for AI/ML

Inadequate data preparation and collection during AI/ML training and deployment might result in poor model performance and project failure overall. To assist you in enhancing your techniques in data collection for your AI/ML models, We will offer a roadmap.

Recognizing the need

One of the most important phases of the entire data collection process is the need identification.  Because there are many different dataset types, knowing the project’s scope will help you eliminate possibilities and select the one that best fits your needs.

Identification of needs aids in choosing the right data type and data collection technique. The crowdsourcing approach may be more effective for such a dataset because gathering it internally can be costly and time-consuming.

Deciding on the approach

You may now choose the manner through which the data will be acquired after determining the type of data that will be used. For your AI/ML projects, there are 4 main ways to gather training data:

1. Customized crowdsourcing-  Data is gathered from the crowd via microtasks in a custom crowdsourcing scenario. While done internally, this can be expensive and time-consuming; nevertheless, third-party data collection/harvesting service providers can effectively deliver it.
2. Private collection-  This approach is suitable for small datasets used in private or sensitive tasks. The GTS data collection team focuses on more private collection for accuracy and does it manually for security and effectiveness.
3. Pre-cleaned and pre-packaged data-  If the project doesn’t call for a fully customized dataset, widely accessible datasets may be the best option.
4. Web crawling and web scraping-  Web scraping is the process of using bots to collect data from websites belonging to a certain domain.

Quality assurance

The quality control phase of data collecting is the third. An AI/ML project’s performance and outcome are determined by data quality assurance.

Making sure the data is of a high standard allows for

1. AI bias reduction
2. Smooth training process Less likelihood of the model being over- or underfit
3. superior performance and accuracy
4. lower levels of false positives and incorrect outcomes

The four criteria listed below can guarantee that the data being collected is of a high enough standard:

• Relevance: Data should be pertinent to the project’s objectives. So, it is important to clear up any useless data.
• Completeness: All model criteria should be covered by the data. The model could be biased or produce incorrect findings if it has any flaws.
• Modern: The content shouldn’t include old, low-quality photographs or other out-of-date information.
• Validity: The information must be accurate and should not have been altered digitally or in any other way.

Data Storage 

No matter if you choose to collect data internally or through outsourcing or crowdsourcing, you will need a storage strategy to keep the information obtained.

The following factors should be taken into account when saving the data:

1. Analyze your storage requirements. If your data is private, for example, you may require private servers with excellent security. Additionally, scalable storage may be necessary if the dataset size varies.
2. Evaluate the storage provider: If you depend on outside storage companies, confirm that they have security protocols in place. They ought to satisfy the security and scalability requirements of your project.
3. Create backups in multiple formats: Another crucial component of guaranteeing data security and protection is having several backups. External hard drives, off-site backups, local server backups, etc. are all options.

A data annotation

An important step in getting data ready for training is data annotation for AI/ML models. Making the data machine-readable, entails labeling or tagging the information. For a facial recognition system, for instance, tags will be created on various aspects of the face recognition data collection in the image to annotate it.

The acquired data won’t be understandable or useful to the model without high-quality annotation. The types of data annotation described above apply to the above-mentioned data-collecting techniques.

Final thoughts

The foundation for success in the field of AI/ML model building is the use of efficient data collection methods. We use clever ways to handle the complexities of various data sources to compile a rich shade of data. with the seamless blending of short and long words, demonstrating the varied nature of data collection.

We break the complexity of the data landscape using a variety of text samples and manual data production. Our models surpass constraints by carefully annotating and comprehending the context, embracing the complexity of real-world circumstances. The entire potential of AI/ML models is unlocked through efficient data collection methods, enabling us to make ground-breaking discoveries and revolutionary advancements.