What are Deployment Team metrics? Crafting the perfect Deployment Team metrics can feel overwhelming, particularly when you're juggling daily responsibilities. That's why we've put together a collection of examples to spark your inspiration.
Copy these examples into your preferred app, or you can also use Tability to keep yourself accountable.
Find Deployment Team 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 Deployment Team 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
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1. Code Quality Measures the frequency and severity of bugs detected in the codebase.
What good looks like for this metric: Less than 10 bugs per 1000 lines of code
Ideas to improve this metric Implement regular code reviews Use static code analysis tools Provide training on best coding practices Encourage test-driven development Adopt a peer programming strategy 2. Deployment Frequency Tracks how often code changes are successfully deployed to production.
What good looks like for this metric: Deploy at least once a day
Ideas to improve this metric Automate the deployment pipeline Reduce bottlenecks in the process Regularly publish small, manageable changes Incentivise swift yet comprehensive testing Improve team communication and collaboration 3. Mean Time to Recovery (MTTR) Measures the average time taken to recover from a service failure.
What good looks like for this metric: Less than 1 hour
Ideas to improve this metric Develop a robust incident response plan Streamline rollback and recovery processes Use monitoring tools to detect issues early Conduct post-mortems and learn from failures Enhance system redundancy and fault tolerance 4. Test Coverage Represents the percentage of code which is tested by automated tests.
What good looks like for this metric: 70% to 90%
Ideas to improve this metric Implement continuous integration with testing Educate developers on writing effective tests Regularly update and refactor out-of-date tests Encourage a culture of writing tests Utilise behaviour-driven development techniques 5. API Response Time Measures the time taken for an API to respond to a request.
What good looks like for this metric: Less than 200ms
Ideas to improve this metric Optimize database queries Utilise caching effectively Reduce payload size Use load balancing techniques Profile and identify performance bottlenecks
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Tracking your Deployment Team metrics Having a plan is one thing, sticking to it is another.
Don't fall into the set-and-forget trap. It is important to adopt a weekly check-in process to keep your strategy agile – otherwise this is nothing more than a reporting exercise.
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
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