What are Datasets metrics? Identifying the optimal Datasets metrics can be challenging, especially when everyday tasks consume your time. To help you, we've assembled a list of examples to ignite your creativity.
Copy these examples into your preferred app, or you can also use Tability to keep yourself accountable.
Find Datasets metrics with AI While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI metrics generator below to generate your own strategies.
Examples of Datasets metrics and KPIs 1. Number of Parameters Differentiates model size options such as 1 billion (B), 3B, 7B, 14B parameters
What good looks like for this metric: 3B parameters is standard
Ideas to improve this metric Evaluate the scalability and resource constraints of the model Optimise parameter tuning Conduct comparative analysis for various model sizes Assess trade-offs between size and performance Leverage model size for specific tasks 2. Dataset Composition Percentage representation of data sources: web data, books, code, dialogue corpora, Indian regional languages, and multilingual content
What good looks like for this metric: Typical dataset: 60% web data, 15% books, 5% code, 10% dialogue, 5% Indian languages, 5% multilingual
Ideas to improve this metric Increase regional and language-specific content Ensure balanced dataset for diverse evaluation Perform periodic updates to dataset Utilise high-quality, curated sources Diversify datasets with varying domains 3. Perplexity on Validation Datasets Measures the predictability of the model on validation datasets
What good looks like for this metric: Perplexity range: 10-20
Ideas to improve this metric Enhance tokenization methods Refine sequence-to-sequence layers Adopt better pre-training techniques Implement data augmentation Leverage transfer learning from similar tasks 4. Inference Speed Tokens processed per second on CPU, GPU, and mobile devices
What good looks like for this metric: GPU: 10k tokens/sec, CPU: 1k tokens/sec, Mobile: 500 tokens/sec
Ideas to improve this metric Optimise algorithm efficiency Reduce model complexity Implement hardware-specific enhancements Utilise parallel processing Explore alternative deployment strategies 5. Edge-device Compatibility Evaluates the model's ability to function on edge devices with latency and response quality
What good looks like for this metric: Latency: <200 ms for response generation
Ideas to improve this metric Optimise for low-resource environments Develop compact model architectures Incorporate adaptive and scalable quality features Implement quantisation and compression techniques Perform real-world deployment tests
← →
1. Percentage of Basic Data Quality Checks Implemented Measures the proportion of datasets with basic data quality checks applied
What good looks like for this metric: 80% or higher
Ideas to improve this metric Prioritise the implementation of basic checks on all datasets Provide training for team members on basics of data quality Allocate resources for implementing basic checks Automate basic data quality checks to ensure consistency Regularly review and update checklists for basic checks 2. Percentage of Advanced Data Quality Checks Implemented Measures the proportion of datasets with advanced data quality checks applied
What good looks like for this metric: 60% or higher
Ideas to improve this metric Identify datasets requiring advanced checks Develop a strategic plan for advanced data quality implementations Seek external expertise for complex checks Increase budget for advanced data quality tools Regularly review advanced check requirements 3. Month-Over-Month Improvement in Data Quality Maturity Tracks the percentage change or improvement in the implementation of data quality checks month-over-month
What good looks like for this metric: 5% increase
Ideas to improve this metric Set monthly targets to improve data quality metrics Analyse bottlenecks from previous months and address them Ensure consistent reporting and monitoring of progress Incorporate regular feedback loops from data teams Recognise and reward teams exceeding targets 4. Data Quality Issue Resolution Time Measures the average time taken to resolve data quality issues
What good looks like for this metric: Less than 48 hours
Ideas to improve this metric Streamline issue reporting processes Establish clear guidelines for issue prioritisation Provide tools and training for faster issue resolution Monitor and analyse common issue types Implement a rapid response team for data quality issues 5. User Feedback on Data Quality Collects user feedback regarding the perceived quality and reliability of data
What good looks like for this metric: 80% user satisfaction
Ideas to improve this metric Conduct regular surveys to gather user feedback Engage with users for detailed feedback sessions Communicate improvements to users regularly Set up feedback loop in data systems Address user concerns and demonstrate improvements
← →
Tracking your Datasets metrics Having a plan is one thing, sticking to it is another.
Setting good strategies is only the first challenge. The hard part is to avoid distractions and make sure that you commit to the plan. A simple weekly ritual will greatly increase the chances of success.
A tool like Tability can also help you by combining AI and goal-setting to keep you on track.
More metrics recently published We have more examples to help you below.
Planning resources OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:
Tability's check-ins will save you hours and increase transparency
The best metrics for AI Model Performance Evaluation