How to leverage machine learning outcomes
After you have identified and tuned a feasible framework of your problem it is time to leverage that model. You might be required to revisit your why and tell yourself what form you require a solution for the issue that you are identifying a solution to your problem.
The problem is not tackled till you do something with the outcomes. In this blog post by AICorespot, you will learn about tactics for putting forth your outcomes in response to a question and considerations when converting your prototype model into a production system.
Dependent on the variant of issue you are attempting to solve, the presentation of the outcomes will be very different. There are two primary facets to leveraging the outcomes of your machine learning endeavour.
- Report the results
- Operationalize the system
After you have found out an adequate model and an adequate outcome (or not, as the scenario might be), you will wish to summarize what was learned and put it forth to the stakeholders. This might be yourself, a customer, or the business with which you work.
Leverage a powerpoint template and tackle the sections detailed below. You may like to whip up a one-pager and leverage part section as a section header. Attempt to adhere to this procedure even on small experimental projects you do for yourself like tutorials and competitions. It is simple to spend an inordinate number of time on a particular project and you ought to ensure to capture all the great things you learn along the way.
Below are the sections you can finish when reporting the outcomes for a project.
- Context (Why): Give definition to the environment in which the problem exists and establish the motivation for the research question.
- Problem (Question): Concisely detail the problem as a question that you went out and answered.
- Solution (Answer): Concisely detail the solution as an answer to the query you posed in the prior section. Ensure to be specific.
- Findings: Bulleted lists of discoveries you found out along the way that are of interest to the audience. They might be discoveries in the information, strategies that did or did not function or the model performance advantages you accomplished along your journey.
- Limitations: Take up where the model does not function or queries that the model does not furnish solutions to. Do not avoid these queries, defining where the model exceeds expectations is more trusted and if you can give definitions to the areas where it does not excel.
- Conclusions (Why+Question+Answer): Revisit the why, research queries and the solution you found out in a tight little package that is simple to remember and repeat both for yourself and others.
The kind of audience you are putting forth your findings to will give definition to the amount of detail you go into. Possessing the discipline to finish your projects with a report on outcomes, even on small side projects will accelerate your learning. On these small side projects, it is very recommended sharing outcomes of side projects on blogs or with communities and obtain feedback that you can gather and take with you into the beginning of your subsequent project.
You have discovered a model that is adequate enough at tackling the issue you encounter that you would like to enter it into production. This might be an operational installation on your workstation in the scenario of a fun side project, all the way up to integration of the model into a current enterprise application. The scope is enormous. In this portion of the blog, you will learn three critical facets to operationalizing a model that you could consider meticulously prior to putting a system into production.
Three areas that you ought to ponder carefully about are the algorithm implementation, the automated evaluation of your framework and the tracking of the models performance through the passage of time. These three problems will probably impact the variant of model you select.
It is probable that you were leveraging a research library to discover the strategy featuring the best performance. The algorithm implementations in research libraries can be excellent, however, they can additionally be authored for the general scenario of the issue instead of the particular scenario with which you are working.
Think very well about the dependencies and technical debt you might be developing by putting such an implementation straight into production. Take up locating a production-level library that assists the strategy you desire to leverage. You might have to repeat the procedure of algorithm tuning if you switch to a production level library at this juncture.
You might also take up implementation of the algorithm proper. This option might put forth risks dependent on the intricacy of the algorithm you have selected and the implementation tricks it leverages. Even with open source code, there might be a number of complicated operations that might be very tough to internalize and reproduce confidently.
Author automated tests that verify that the model can be developed and accomplish a minimum level of performance repeatedly. Also, author tests any data preparation steps. You might wish to control the arbitrariness leveraged by the algorithm (arbitrary number seeds) for every unit test run so that tests are 100% reproducible.
Include infrastructure to survey the performance of the model over the passage of time and raise alarms if precision dips below a minimum level. Tracking might happen in real-time or with samples of live data on a re-created model in a separate environment. A raised alarm might be an indication that the structure learned by the framework in the data have altered (concept drift) and that the model might require to be updated or tuned.
There are a few model variants that can carry out online learning and update themselves. Think meticulously in enabling models to update themselves in a production setting. In a few scenarios, it can be a better idea to manage the model update process and swap out models (their internal configurations) as they are verified to be more performant.
In this blog post by AICoreSpot, you learned that projects are not viewed as finished till you provide the outcomes. Outcomes might be put forth to yourself or to your clients and there is a minimum structure to adhere to when putting forth results.
You also got to know about three concerns when leveraging a model in a production environment, particularly the nature of the algorithm implementation, model tests and on going tracking.