Most people are familiar with the term ‘Artificial Intelligence,’ an umbrella nomenclature for innovative tech that enables more effortless living for humans. This is achieved by generating data capable of producing machines that perform human tasks accurately.
A sub-field of AI in recent tech is Machine Learning (ML), which encompasses identifying, studying patterns, and leverage them. Machine Learning Operations is requisite for Machine Learning to be possible; it is the lubricant factor in the Machine Learning process.
As explained by AI writer Stephen Watts, MLOps is a practice for collaboration and communication between data scientists and operations professionals to help manage production Machine Learning (or deep learning) lifecycle. Complementary, according to AI Multiple’s Cem Dilmegani in a recent blog post, MLOps practices help us standardize and streamline the construction and deployment of machine learning systems, covering the entire lifecycle of a machine learning application from data collection model management.
MLOps serve to enlighten and modify ML production while recording business profits and upholding binding regulatory policies. MLOps encompasses all engineering activities that aid the smooth running and maintenance of AI models.
Be that as it may, data scientists and programmers have recently lamented some of the bottlenecks in Machine Learning Operations (MLOps) infamous for delaying production processes. Top contenders for the most cumbersome MLOps include – but may not be limited to – Model Monitoring and Model Maintenance and retraining.
Quoting the Full Stack Deep Learning alumni survey infographic, the Machine Learning lifecycle stages that proved most difficult for programmers and data scientists were those above, with the former recording 60.0% of the population study asserting to its difficulty and 61.1% admitting that the latter was their most challenging stage of the Machine Learning process to get past.
Model retraining is the ML operation that sees the correction of varying data by retraining the model to be more suitable for the program. Even after the production process is seemingly completed, some errors or inaccuracies may surface, necessitating a do-over involving a new set of data for better results.
The challenging factor in the Model retraining stage is probably derived from model deployment complications that entail a seemingly endless retraining process. This is owed to the fact that when it has to do with models, degradation is inevitable; therefore, the retraining process is almost always necessary, at least for satisfactory results.
On the other hand, Model Monitoring is the stage of Machine Learning that closely observes the overall performance of Machine Learning models for the AI team to recognize flaws and prevent these flaws from sabotaging business opportunities. In simple terms, model monitoring is the stage in the ML lifecycle where the model is monitored for potential errors and eliminating (or adding) causal (or relevant) data, ensuring that the model is updated and improved to suit different scenarios and tackle them self-sufficiently.
Model Monitoring practices are essential for preparing against failure of the algorithm and generally serve as a sort of insurance for the machine learning model against obsolescence and potential loss.
Both Model Monitoring and Model retraining have been identified as bottleneck processes in Machine Learning, regardless of their collective value. For Model Monitoring, the most probable reasons for its popular interpretation as cumbersome would be its time-consuming nature and general lack of knowledge on adequately executing the monitoring process.
Most AI teams in model production processes are eager to release their models into the trade market, and the monitoring practices involved in ML are infamous for stalling launch programs. Tech companies are driven by two fundamental factors; the desire to meet human needs through revolutionary technology and make money. Model Monitoring significantly slows down the process and is not exactly the most anticipated stage of ML model deployment.
The other possible reason for the general dislike of this stage is the unfortunate lack of tech know-how on the appropriate or applicable ML model monitoring practice to be implemented.
Monitoring ML systems certainly require modalities different from the popular monitoring processes relevant to regular software. For whatever reason, many data scientists and programmers are yet to grasp the knowledge needed for proper Model Monitoring fully. Therefore, this makes the process unappealing to most professionals.
It may be likened to going out to do your chores and getting to the point where you have to do your most hated task. You wish you do not have to – you seek shortcuts to avoid doing it, but you know you HAVE to; it is inevitable. Whether appealing or not, Model Monitoring is necessary to enable early detection of possible flaws and provide a fix to them. It is all for the greater good.
In the case of Model maintenance and retraining, its’ financial strain on AI companies and some of its obsolete, stressful retraining tasks and practices represent the primary bottleneck.
Constantly ‘updating’ ML models can be costly for tech companies who unavoidably have to spend a lot of funds on human resources, computational costs, and actual implementation.
Companies do not particularly relish the thought of constantly investing finances into maintaining skilled human resources whose job it is to retain and retrain ML models. Running costs on computation is also a cause for worry for most AI companies involved in ML model deployment.
It also does not help that some of the Model retraining practices are painfully cumbersome and difficult to execute. Reasons aside, model maintenance is utterly relevant for a balanced ML model production as these practices ensure that the models are in sync with what is obtainable in our ever-changing world.