2 customisable OKR examples for Machine Learning Model
What are Machine Learning Model OKRs?
The Objective and Key Results (OKR) framework is a simple goal-setting methodology that was introduced at Intel by Andy Grove in the 70s. It became popular after John Doerr introduced it to Google in the 90s, and it's now used by teams of all sizes to set and track ambitious goals at scale.
How you write your OKRs can make a huge difference on the impact that your team will have at the end of the quarter. But, it's not always easy to write a quarterly plan that focuses on outcomes instead of projects.
That's why we have created a list of OKRs examples for Machine Learning Model to help. You can use any of the templates below as a starting point to write your own goals.
If you want to learn more about the framework, you can read our OKR guide online.
Building your own Machine Learning Model OKRs 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 generator below or our more complete goal-setting system to generate your own OKRs.
Our customisable Machine Learning Model OKRs examples
You'll find below a list of Objectives and Key Results templates for Machine Learning Model. We also included strategic projects for each template to make it easier to understand the difference between key results and projects.
Hope you'll find this helpful!
1. OKRs to become an expert in large language models
Become an expert in large language models
Demonstrate proficiency in implementing and fine-tuning large language models through practical projects
Continuously update and optimize large language models based on feedback and results obtained
Complete practical projects that showcase your proficiency in working with large language models
Create a large language model implementation plan and execute it efficiently
Identify areas of improvement in large language models and implement necessary fine-tuning
Complete online courses on large language models with a score of 90% or above
Engage in weekly discussions or collaborations with experts in the field of large language models
Schedule a weekly video conference with language model experts
Document key insights and lessons learned from each discussion or collaboration
Share the findings and new knowledge with the team after each engagement
Prepare a list of discussion topics to cover during the collaborations
Publish two blog posts sharing insights and lessons learned about large language models
2. OKRs to enhance global issue feedback classification accuracy and coverage
Enhance global issue feedback classification accuracy and coverage
Reduce incorrect feedback classification cases by at least 25%
Train staff on best practices in feedback classification
Implement and continuously improve an automated classification system
Analyze and identify patterns in previous misclassifications
Improve machine learning model accuracy for feedback classification by 30%
Introduce a more complex, suitable algorithm or ensemble methods
Implement data augmentation to enhance the training dataset
Optimize hyperparameters using GridSearchCV or RandomizedSearchCV
Expand feedback coverage to include 20 new globally-relevant issues
Identify 20 globally-relevant issues requiring feedback
Develop a comprehensive feedback form for each issue
Roll out feedback tools across all platforms
Machine Learning Model OKR best practices to boost success
Generally speaking, your objectives should be ambitious yet achievable, and your key results should be measurable and time-bound (using the SMART framework can be helpful). It is also recommended to list strategic initiatives under your key results, as it'll help you avoid the common mistake of listing projects in your KRs.
Here are a couple of best practices extracted from our OKR implementation guide 👇
Tip #1: Limit the number of key results
Focus can only be achieve by limiting the number of competing priorities. It is crucial that you take the time to identify where you need to move the needle, and avoid adding business-as-usual activities to your OKRs.
We recommend having 3-4 objectives, and 3-4 key results per objective. A platform like Tability can run audits on your data to help you identify the plans that have too many goals.
![Tability Insights Dashboard](https://tability-templates-v2.vercel.app/_next/static/media/tability-insights-board.e70f9466.png)
Tip #2: Commit to weekly OKR check-ins
Having good goals is only half the effort. You'll get significant more value from your OKRs if you commit to a weekly check-in process.
Being able to see trends for your key results will also keep yourself honest.
![Tability Insights Dashboard](https://tability-templates-v2.vercel.app/_next/static/media/checkins-graph.b2aec458.png)
Tip #3: No more than 2 yellow statuses in a row
Yes, this is another tip for goal-tracking instead of goal-setting (but you'll get plenty of OKR examples above). But, once you have your goals defined, it will be your ability to keep the right sense of urgency that will make the difference.
As a rule of thumb, it's best to avoid having more than 2 yellow/at risk statuses in a row.
Make a call on the 3rd update. You should be either back on track, or off track. This sounds harsh but it's the best way to signal risks early enough to fix things.
How to turn your Machine Learning Model OKRs in a strategy map
OKRs without regular progress updates are just KPIs. You'll need to update progress on your OKRs every week to get the full benefits from the framework. Reviewing progress periodically has several advantages:
- It brings the goals back to the top of the mind
- It will highlight poorly set OKRs
- It will surface execution risks
- It improves transparency and accountability
Spreadsheets are enough to get started. Then, once you need to scale you can use a proper OKR platform to make things easier.
![A strategy map in Tability](https://tability-templates-v2.vercel.app/_next/static/media/tability_strategy_map.2ad25843.png)
If you're not yet set on a tool, you can check out the 5 best OKR tracking templates guide to find the best way to monitor progress during the quarter.
More Machine Learning Model OKR templates
We have more templates to help you draft your team goals and OKRs.
OKRs to consistently publish high-quality content aligned with audience needs
OKRs to increase company revenue and enhance the organizational environment
OKRs to enhance incident management and outage call bridge creation processes
OKRs to drive successful implementation of relevant alliance principles
OKRs to establish robust financial structure for sustainability and growth
OKRs to boost customer acquisition for consultancy business
OKRs resources
Here are a list of resources to help you adopt the Objectives and Key Results framework.
- To learn: What is the meaning of OKRs
- Blog posts: ODT Blog
- Success metrics: KPIs examples
What's next? Try Tability's goal-setting AI
You can create an iterate on your OKRs using Tability's unique goal-setting AI.
Watch the demo below, then hop on the platform for a free trial.