Machine Learning Models

Machine learning models must be operationalized differently than they were created. You must evaluate five key areas to be successful in this change. Most machine learning teams work done in a laboratory setting when they first start. This implies that they follow the process manual but scientific way. They repeatedly build valuable machine learning models by generating a hypothesis, testing the model to confirm the theory, and changing the model to improve its behavior. It’s crucial to take these projects out of testing mode and operationalize them as they mature and evolve.

For individuals doing the work, operationalizing machine learning teams necessitates a shift in mentality and a distinct set of abilities. The curious state of what-ifs and trial and error gives way to predictable and steady habits. The goal is to replicate the same valuable benefits obtained throughout the creative process but more hands-off and long-term. The team’s goals shift from experimentation to experience management due to this.

Consider these five essential aspects when operationalizing your machine learning model: data collection, error management, consumption, security, and model maintenance.

Data Collection

Much of the data collection and purification is done manually during the experimental phase. A training and testing data set is culled by hand from a source, a data lake, a data warehouse, or an operational system. Merging, matching, deduplication, and overall data wrangling are typically done in stages. This is primarily due to the data scientists’ uncertainty about what will survive in the data collection. This data management process can include work in computer languages like Python and R and a spreadsheet or text editor.

The uncertainty of what data is valuable is removed with an operational model, and any data wrangling done during the development phase must now be automated, and production sized. This means that the development scripts must be standardized into something that can be utilized in a production setting. This could entail rewriting hands in a supported language, using scripting or an ETL tool to automate tasks performed in a spreadsheet, or ensuring that all data sources utilized are updated regularly and accessible as part of the data collecting process.

Error Handling

The most effective way to deal with these unexpected obstacles is to deal with them one at a time when they come. Data scientists manage the errors that develop as they progress through the process one step at a time. If data scientists encounter a problem, they communicate with the people and systems who can help them address it, whether dirty data or data access concerns.

Once the models have been promoted to a production environment, this is no longer the case. As these models get more integrated into a more extensive data pipeline, downstream processes become more reliant on their output, and errors become more likely to cause business disruption. During the pre-operation design and development stage, as many of these potential faults as feasible must be foreseen, and automated processes must be designed and developed to solve them.

Automation must be used to identify potentially disruptive problems and self-healing efforts. Furthermore, it is necessary to establish records and alerts connected with automated remedial measures to uncover patterns that suggest more pervasive and concerning underlying issues.


The purpose of the first step is to provide a single answer or prove a concept. The results of machine learning models are frequently vetted, collated, and presented — either manually creating charts and graphs or summarising the results into a slide presentation, which is then wrapped in data storytelling to transmit the most important findings. Because these findings are new and original, presenting them is one of the most crucial elements in the business’s decision-making process. This might be a stage-gate approval to move on to operationalize the models if there is enough value given.

The consumption model evolves when these models progress from the information distribution stage to the operational phase. This refers to both who consumes the information and how it is consumed.

The output of machine learning models is frequently embedded into broader processes that eventually produce business results once they are functional. The works of the first phase are often digested by key decision-makers, who either use the research to make a business decision directly or as a gatekeeper choosing if the model can go to the operational level. The same decision-makers who were consumers of the initial-stage output are now more concerned with the outcomes that result from having the data integrated into the company’s processes. During the operations phase, the process owners become new customers, collaborating with the IT team to automate the integration of these models with business processes.

Consumption in an operations model shifts from boardroom presentations to direct integrations with a data source that has been pre-populated from the model or via an API. This consuming model necessitates a different set of outputs than the discovery model.


When data scientists work on a model, they are usually among a small group who see and interact with the data. At the same time, they have a limited audience and data insights that are still being generated, tested, and validated. Companies may usually rely on solid desktop management procedures as their primary defense against security breaches.

The requirement for good corporate data management controls develops as more persons and teams become involved, and the company’s reliance on data as part of its business operations grows. This necessitates more comprehensive data ecosystem security protocols, such as enterprise data access restrictions and master data management. At this point, robust desktop security procedures are still necessary, but they are simply one part of a larger defense-in-depth strategy.

Raw data becomes a much more valuable asset when enriched and processed to derive insights. This knowledge could be considered a trade secret in some situations. Its confidentiality, integrity, and accessibility level should be increased to its organizational significance.

Management Of Models

Although the model generation process is very scientific and controlled, testing necessitates the data science team’s autonomy to alter and improve models incrementally to optimize the results. Data inputs, machine learning techniques, and model hyperparameters can all be affected.

Version control is used to prevent unintended modifications from disrupting operational processing. This also enables rollbacks to prior versions if something goes wrong.

The assumptions included in the model can become erroneous or start to wander away from the initial specifications as human behavior and the business environment evolve. Once a model is in production, it’s critical to keep an eye on it to maintain the same degree of accuracy and precision as it did in the lab. If it deviates from predetermined limits, it must return to the experimentation phase to determine what changes must be done to bring it back into compliance.


As a data and analytics leader, your ability to consider and account for these essential parts of the operationalization process will be critical. They will enable you to serve as a link between the creative experimentation phase of machine learning model building and the routine and consistent operational phase. You’ll be able to optimize two seemingly opposing processes and deliver business benefits by striking this equilibrium. This incremental optimization and improvement must be monitored considerably more attentively in a state of operations.

Do you want to know more about machine learning? To learncontact the ONPASSIVE team.