Intelligent AI and ML models are everywhere, be it
- Predictive healthcare models for proactive diagnosis
- Autonomous vehicles with lane-keeping, reverse parking, and other built-in traits
- Intelligent chatbots that are privy to content, context, and intent
But what makes these models accurate, highly automated, and insanely specific
Data, Data, and More Data.
For data to make sense to an AI model, you need to keep the following factors in mind:
- Massive raw data chunks are available
- Data blocks are multivariate and diverse
- Unlabelled data is like noise to intelligent machines
Solution: Data Annotation (Process of labeling data to create relevant and use-case-specific datasets)
Acquiring AI Training Data for ML Models
Credible AI Data collectors focus on multiple aspects before initiating data capturing and extraction across avenues. These include:
- Focusing on preparing multiple datasets
- Keeping the data collection and annotation budget under control
- Acquiring model relevant data
- Only working with credible dataset aggregators
- Identifying organization goals beforehand
- Working alongside suitable algorithms
- Supervised or Unsupervised learning
Top Options for Acquiring data that adheres to the mentioned aspects:
- Free Sources: Includes open forums like Quora and Reddit and open aggregators like Kaggle OpenML, Google Datasets, and more
- Internal Sources: Data extracted from CRM and ERP platforms
- Paid Sources: Includes external vendors and using data scraping tools
Point to Note: Perceive open datasets with a pinch of salt.
Budget Factors
Planning to budget our AI Data Collection initiative. Before you can, take the following aspects and questions into consideration:
- Nature of the product that needs to be developed
- Does the model support reinforcement learning?
- Is deep learning supported?
- Is it NLP, Computer Vision, or Both
- What are your platforms and resources for labeling the data?
Based on the analysis, here are the factors that can and should help you manage the pricing of the campaign:
- Data Volume: Dependencies: Size of the project, preferences towards training and testing data sets, the complexity of the system, type of AI technology it adheres to, and emphasis on feature extraction or lack thereof.
- Pricing Strategy: Dependencies: Competence of the service provider, quality of data, and complexity of the model in the picture
- Sourcing Methodologies: Dependencies: Complexity and size of the model, hired, contractual, or in-house workforce sourcing the data, and choice of source, with options being open, public, paid, and internal sources.
How to Measure Data Quality?
To ensure whether the data fed into the system is high quality or not, ensure that it adheres to the following parameters:
- Intended for specific use cases and algorithms
- Helps make the model more intelligent
- Speeds up decision making
- Represents a real-time construct
As per the mentioned aspects, here are the traits that you want your datasets to have:
- Uniformity: Even if data chunks are sourced from multiple avenues, they need to be uniformly vetted, depending on the model. For instance, a well-seasoned annotated video dataset wouldn’t be uniform if paired with audio datasets that are only meant for NLP models like chatbots and Voice Assistants.
- Consistency: Datasets should be consistent if they want to be termed as high quality. This means every unit of data must aim at making decision-making quicker for the model, as a complementary factor to any other unit.
- Comprehensiveness: Plan out every aspect and characteristic of the model and ensure that the sourced datasets cover all the bases. For instance, NLP-relevant data must adhere to the semantic, syntactic, and even contextual requirements.
- Relevance: If you have some outcomes in mind, ensure that the data is both uniform and relevant, allowing the AI algorithms to be able to process them with ease.
- Diversified: Sounds counterintuitive to the ‘Uniformity’ quotient? Not exactly as diversified datasets are important if you want to train the model holistically. While this might scale up the budget, the model becomes way more intelligent and perceptive.
Benefits of Onboarding end-to-end AI Training Data Service Provider
Before enlisting the benefits, here are the aspects that determine the overall data quality:
- Platform used
- People involved
- Process followed
And with an experienced end-to-end service provider in play, you get access to the best platform, most seasoned people, and tested processes that actually help you train the model to perfection.
For specifics, here are some of the more curated benefits that deserve an additional look:
- Relevance: End-to-End service providers are experienced enough to only provide model and algorithm-specific datasets. Plus, they also take care of the system complexity, demographics, and market segmentation into account.
- Diversity: Certain models require truckloads of relevant datasets to be able to make decisions accurately. For instance, self-driving cars. End-to-End, experienced service providers take the need for diversity into account by sourcing even vendor-centric datasets. Put plainly, everything that might make sense to the models and algorithms is made available.
- Curated Data: The best thing about experienced service providers is that they follow a step-pronged approach to dataset creation. They tag relevant chunks with attributes for the annotators to make sense of.
- High-end Annotation: Experienced service providers deploy relevant Subject Matter Experts to annotate massive chunks of data to perfection.
- De-Identification as Per Guidelines: Data security regulations can make or break your AI training campaign. End-to-End service providers, however, take care of every compliance issue, relevant to GDPR, HIPAA, and other authorities and let you focus completely on project development.
- Zero Bias: Unlike in-house data collectors, cleaners, and annotators, credible service providers emphasize eliminating AI bias from models to return more objective results and accurate inferences.
Choosing the right Data Collection Vendor
Every AI training campaign starts with Data Collection. Or, it can be said that your AI project is often as impactful as the quality of data that is brought to the table.
Therefore, it is advisable to onboard the right Data Collection vendor for the job, who adheres to the following guidelines:
- Novelty or Uniqueness
- Timely deliveries
- Accuracy
- Completeness
- Consistency
And here are the factors you need to check as an organization for zeroing in on the right choice:
- Ask for a sample dataset
- Cross-check the compliance-relevant queries
- Understand more about their data collection and sourcing processes
- Check their stance and approach towards eliminating bias
- Make sure that their workforce and platform-specific capabilities are scalable, in case you want to make progressive developments to the project, over time
