You've encountered unexpected algorithm deviations. How do you navigate recalibration effectively?

Few important aspect to be considered: 1. Before recalibration, do we have correct test dataset for benchmarking and training. 2. If so, then look at any orthogonal algorithms check their performance. 3. Check for variables selected for building the model.

Here's how you can identify causes for the deviation in your algorithm before recalibrating, 1. Check for any recent changes or updates in the data sources used by the algorithm. 2. Use statistical techniques to detect shifts or anomalies in data patterns that might impact algorithm behavior. 3. Perform a thorough investigation to pinpoint the specific source of the deviation.

To navigate recalibration effectively after encountering unexpected algorithm deviations, first identify and isolate the specific components causing the deviations. Analyze recent changes and compare results against expected outcomes to pinpoint discrepancies. Use diagnostic tools and logging to gather detailed data for troubleshooting. Adjust parameters incrementally, test thoroughly, and document each change to track progress and ensure a systematic approach.

To navigate unexpected algorithm deviations effectively: Stay Calm: Maintain focus and composure. Identify Symptoms: Document deviations and error messages. Reproduce Issues: Ensure consistent reproduction of the problem. Analyze Changes: Review recent modifications and related code. Debug Methodically: Use breakpoints, step through code, and check variable states. Validate Assumptions: Use assertions to check assumptions. Isolate Problem Areas: Test smaller parts independently. Consult Resources: Check documentation and seek expert help if needed. Apply Fixes: Address root causes, not just symptoms. Test Thoroughly: Validate with diverse inputs and edge cases.

Personally I like to follow this approach. - Identify the specific behaviour that deviates from your expectations. - Trace the execution flow backward from the point where the issue occurs. - Add logging statements to your code. Log relevant information (e.g., variable values, function calls) during execution. I find this a good way to establish root causes.

There is a concept in ML model development called Data Distribution Shift. In simple words this means that the data your algorithm/ model was previously trained on before deployment is not representative of the data that your system is encountering during production. There are several ways to tackle this issue, the major one being ‘continual learning’ where you update the model continuously based on new data distributions from time to time.

Where is the data coming from? Is it coming from a simple CSV or JSON? A database such as MySQL, MongoDB, Postgres or an API? Writing test functions is a sound way to review your data. If applicable - Check the data schema are defined as expected. Check the documentation (if available) Try to leverage tools to build a good picture of the data you are working with. For example you can export Entity Relationship Diagrams (ERD) from software such as DataGrip and PgAdmin. Refer to the source code itself. Look at the entity/model files associated with the data if there is any. Check if there is any data transformation executed on the data between the data-source and your algorithm.

Data should be reviewed in any platforms, software thus we have to check up data use and handles' usability and quality for the first attempt to take on any platforms use.

I personally approach writing my algorithms with the Test-driven development(TDD) software development practice. Couple with software development principle e.g SOLID, KISS and DRY to name a few.

In a modern distributed execution environment monitoring the source and sink of the algorithm for any recent changes in execution time/schedule, configuration changes or software/library packages updates is also important. Following are few reasons why algorithm may start failing suddenly: 1. Source system is updated and now sending data/input in a little different format. 2. Source system is updated and now sending data with different bounds/scenarios that previously not thought while designing algorithm. 3. Algorithm is triggered when source is not yet ready to send data/input. 4. Algorithm is depend on some additional resources e.g. access to cloud storage etc. but configuration like secrets, firewall configuration is changed recently.

1. Diagnose and Identify Root Causes: - Conduct a thorough analysis to identify the factors contributing to the deviations, such as data drift, model overfitting, or changes in input data patterns. 2. Update and Validate the Model: - Adjust the algorithm or retrain it with updated data. Perform cross-validation and testing to ensure the recalibrated model meets the desired performance standards. 3. Communicate and Document Changes: - Transparently communicate the recalibration process and results to stakeholders. Document all changes and rationales to maintain clarity and provide a reference for future adjustments.

Look for patterns in the deviation. Is there any systematic deviation? Quite possibly the pattern itself will reveal the solution