Context: Machine Learning (ML) has become widely adopted as a component in many modern software applications. Due to the large volumes of data available, organizations want to increasingly leverage their data to extract meaningful insights and enhance business profitability. ML components enable predictive capabilities, anomaly detection, recommendation, accurate image and text processing, and informed decision-making. However, developing systems with ML components is not trivial; it requires time, effort, knowledge, and expertise in ML, data processing, and software engineering. There have been several studies on the use of model-driven engineering (MDE) techniques to address these challenges when developing traditional software and cyber-physical systems. Recently, there has been a growing interest in applying MDE for systems with ML components. Objective: The goal of this study is to further explore the promising intersection of MDE with ML (MDE4ML) through a systematic literature review (SLR). Through this SLR, we wanted to analyze existing studies, including their motivations, MDE solutions, evaluation techniques, key benefits and limitations. Results: We analyzed selected studies with respect to several areas of interest and identified the following: 1) the key motivations behind using MDE4ML; 2) a variety of MDE solutions applied, such as modeling languages, model transformations, tool support, targeted ML aspects, contributions and more; 3) the evaluation techniques and metrics used; and 4) the limitations and directions for future work. We also discuss the gaps in existing literature and provide recommendations for future research. Conclusion: This SLR highlights current trends, gaps and future research directions in the field of MDE4ML, benefiting both researchers and practitioners

How does a machine learning project work? What are the different building blocks that go into making a machine learning or artificial intelligence (AI) system? This is a topic I personally struggled with during my initial days in the field. I knew how to make machine learning models but I had no clue how a real-world machine learning project actually worked. It was quite a revelation when I went through the process!

We are studying the emerging discipline of Machine Learning Engineering by investigating best practices for developing software systems that include ML components. In this article, we share the research motivation and approach, some initial results, and an invitation to help us by taking our 7-minute online survey on ML Engineering best practices. Artificial Intelligence (AI) is undeniably experiencing a new wave of attention, energy, and sky-high expectations. This wave is driven by the abundance of data that is generated in our connected, digital society, and by the low-barrier availability of enormous computational resources. Among various AI-techniques, Machine Learning (ML) in particular has come to play a key role.

Talend provides a number of Machine Learning components that can be used for a variety of purposes. I have previously described some of these various components, some in more detail than others, as well as outlining what they can do. However, one question remains, what use cases can be solved by using these Machine Learning components? Talend provides a set of'out of the box' components for various ML techniques. All of the above leverage Apache Spark for scale and performance, they enable a faster time to insight and value, they focus on business outcomes - not development and they present with a lower skills barrier to use.