Bring data science models into production with our Machine Learning Operations framework

So you have a data warehouse and developed models proven to generate valuable inferences, but the business impact is not there yet? In this webinar we will show how the right framework can activate these models within minutes to continuously deliver up-to-date predictions. This is the central objective of MLOps.

After we built a data warehouse for Meerlanden, their data scientist began working with the data. We proposed setting up a Machine Learning Operations (MLOps) framework together, allowing them to integrate their models directly into the existing environment. This enabled them to make predictions that improved the efficiency of Meerlanden’s services.

In the world of machine learning, working with large datasets and complex models can quickly become time-consuming and resource-intensive. To speed up this process, parallelisation becomes crucial. This technique involves breaking down tasks into smaller subtasks that can be processed simultaneously across multiple CPU cores or distributed machines within a cluster. By spreading out the workload, you can achieve faster and more efficient data processing on a large scale.

A global asset manager, specialising in Quant and Sustainable Investing, offers a range of investment strategies, including equities and bonds. To strengthen their competitive position and proactively respond to changing client needs and market developments, the sales and marketing department aimed to adopt a more data-driven approach.

In the world of data and technology, Data Engineers and Machine Learning Engineers are crucial players. Both roles are essential for designing, building, and maintaining modern data infrastructures and advanced machine learning (ML) applications. In this blog, we focus specifically on the roles and responsibilities of a Data Engineer and Machine Learning Engineer.

With the increasing availability of cloud services that enable companies to leverage Large Language Models, it becomes relatively easy to setup your own GPT-model. However, one important question needs to be answered before you start building: what are the benefits for my organisation?

Financial institutions need to process large amounts of documentation. For this particular institution, an internal team facilitates this by, for example, creating summaries using text analysis and natural language processing (NLP). They make these available to the various business units. To conduct audits more efficiently, they wanted to develop a question-and-answer model to get the right information to them faster. When ChatGPT was launched, they asked us to create a proof of concept.

Bringing machine learning models to production has proven to be a complex task in practice. MLOps assists organisations that want to develop and maintain models themselves in ensuring the quality and continuity. Read this article and get answers to the most frequently asked questions on this topic.

An essential part of Otis's business operations is the maintenance of their elevators. To time this effectively and proactively inform customers about the status of their elevator, Otis wanted to implement continuous monitoring. They saw great potential in predictive maintenance and remote maintenance.