Tability is a cheatcode for goal-driven teams. Set perfect OKRs with AI, stay focused on the work that matters.
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.
The best tools for writing perfect Machine Learning Model OKRs
Here are 2 tools that can help you draft your OKRs in no time.
Tability AI: to generate OKRs based on a prompt
Tability AI allows you to describe your goals in a prompt, and generate a fully editable OKR template in seconds.
- 1. Create a Tability account
- 2. Click on the Generate goals using AI
- 3. Describe your goals in a prompt
- 4. Get your fully editable OKR template
- 5. Publish to start tracking progress and get automated OKR dashboards
Watch the video below to see it in action 👇
Tability Feedback: to improve existing OKRs
You can use Tability's AI feedback to improve your OKRs if you already have existing goals.
- 1. Create your Tability account
- 2. Add your existing OKRs (you can import them from a spreadsheet)
- 3. Click on Generate analysis
- 4. Review the suggestions and decide to accept or dismiss them
- 5. Publish to start tracking progress and get automated OKR dashboards
Tability will scan your OKRs and offer different suggestions to improve them. This can range from a small rewrite of a statement to make it clearer to a complete rewrite of the entire OKR.
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!
OKRs to enhance machine learning model performance
ObjectiveEnhance machine learning model performance
KRAchieve 90% precision and recall in classifying test data
Implement and train various classifiers on the dataset- Evaluate and iterate model's performance using precision-recall metrics
- Enhance the algorithm through machine learning tools and techniques
- KRReduce model's prediction errors by 10%
- Increase the versatility of training data
- Evaluate and fine-tune model’s hyperparameters
- Incorporate new relevant features into the model
- KRIncrease model's prediction accuracy by 15%
- Enhance data preprocessing and feature engineering methods
- Implement advanced model optimization strategies
- Validate model's performance using different datasets
OKRs to become an expert in large language models
- ObjectiveBecome an expert in large language models
- KRDemonstrate 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
- KRComplete online courses on large language models with a score of 90% or above
- KREngage 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
- KRPublish two blog posts sharing insights and lessons learned about large language models
OKRs to enhance global issue feedback classification accuracy and coverage
- ObjectiveEnhance global issue feedback classification accuracy and coverage
- KRReduce 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
- KRImprove 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
- KRExpand 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
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.
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.
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.
Save hours with automated OKR dashboards
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 Tability to save time with automated OKR dashboards, data connectors, and actionable insights.
How to get Tability dashboards:
- 1. Create a Tability account
- 2. Use the importers to add your OKRs (works with any spreadsheet or doc)
- 3. Publish your OKR plan
That's it! Tability will instantly get access to 10+ dashboards to monitor progress, visualise trends, and identify risks early.
More Machine Learning Model OKR templates
We have more templates to help you draft your team goals and OKRs.
- OKRs to enhance the efficiency and accuracy of our testing procedure
- OKRs to successfully implement and optimize Exabeam operations
- OKRs to enhance employee engagement and retention
- OKRs to enhance equipment productivity and operational time
- OKRs to achieve an 80+ score in work output
- OKRs to improve self-serve onboarding