OptML Solutions

Harnessing the Power of Machine Learning as a Service


Machine learning enables companies to reimagine end-to-end business processes with digital intelligence. Organizations planning for the future will seek to incorporate it into daily decision-making and longer-term planning processes.

OptML offers engineering, data science and subject matter expertise, coupled with a proprietary library of models, to drive results. OptML’s machine learning as a service (MLaaS) environment negates the need for up-front investments in costly hardware or staffing previously required to launch these enablement solutions.


OptML enables rapid, cost-effective solution deployment for any organization by consistently following these five practice steps:

Step 1 – Determining objectives, metrics, and constraints

The first and most important stage is determining the business objective, available data resources, and the constraints to be used in the modeling process. OptML’s team of technology veterans, seasoned engineers and industry thought leaders will work closely with solution stakeholders to outline this process.


Step 2 – Assessing data

The next step is the assessment of data sources, estimation of available data volume, structure, organization and composition of the data. A significant difference between machine learning technologies, from the more conventional ways of data analysis, is that the most valuable data is either raw data, without aggregation and pre-processing or augmented data that includes data overlays from external resources.


Step 3 – Model training

Training the model, in contrast to conventional software development, does not require pre-development of rules and algorithms. The OptML data scientists determine the range of factors that may affect the process being modelled – this is often an extensive process. The training process is iterative with 1 to 2 months spent on designing the model, where within the level of accuracy of this model constantly increases.


Step 4 – Integration and testing

After model training, the machine learning is integrated into the client’s management systems. This integration is simplified by the fact that the model completes an exclusive function – it predicts or recommends. In this regard, the interface is very simple and the integration is reduced to data transfer and displaying those recommendations.


Step 5 – Model monitoring

The last stage is the production use of the OptML service(s) that successfully pass testing. The production use requires constant monitoring of the model quality and its regular additional training on newly collected data. This is easily maintained by OptML’s team of data scientists in the machine learning as a service environment.

The newly deployed OptML solution will become increasingly intuitive, allowing for streams of real-time data to reveal problems before they happen, optimize customer solutions, and reduce the need for human interventions on a minute-by-minute basis.