# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 2 Background and Related Work
### Gaps in the Literature on Predictive Modeling for Performance Optimization in Apache Spark MLlib
#### 2.5.1 Current Gaps  
One of the biggest drawbacks of the existing work in the field is the use of black box techniques for hyperparameter tuning. Such approaches become inefficient because they often do not consider the mechanism by which data are computed within Spark MLlib, thus obtaining flawed information. For this purpose, there is a strong requirement to find out techniques other than model-free reinforcement learning or adaptive optimization strategies that can handle the reformation in Distributed Systems regarding hyperparameter tuning [19].  
Understanding Computational Resource Utilization  
While it is understood that CPU-bound issues and memory limitations influence Spark MLlib models' performance, existing studies do not offer a detailed analysis of how different computational resources--CPU, memory, and I/O--impact the overall model performance. High efforts are still necessary in studying the allocation of resources in distributed systems to make the best of the available resources for use in Spark applications to achieve the best performance [44].  
Integration of External Factors  
Most of the prior work is restricted to discussing the impact of external factors including institutional pressures, organizational culture, and structural constraints regarding the use of predictive modeling in big data environments. Integrating such externality into prediction environments may offer a needed and more realistic view of these externalities and help in enhancing the application of the models in various settings [45].  
Several research studies in this field are still limited to theoretical frameworks or pilot studies. This gives rise to a research question on the real-world scalability of a predictive model and especially on how well these techniques work in real-world big data scenarios. This is due, first, to the fact that the performance of models on the given data as a function of data size, type of computing environment, and application loads of actual large-scale real-world applications have yet to be investigated [46; 47].  
Thus, it is important to fill these gaps in the literature for the contribution to predictive model development and improvement of the performance of the Apache Spark MLlib. As such, it is recommended that ongoing and future research seeks to engage in more scalable and comprehensive assessments, consider fresh means of optimization in resource usage [48], and factor in more external variables that will make these models more applicable in the real world of Big Data Analytics.  
Future and ongoing research should focus on developing user-friendly tools, for example, graphical interfaces for Spark MLlib, so it is easier to apply machine learning algorithms. It could encourage wider take up among real users, who are more varied in their levels of experience. SparkReact is a tool for fabricating web applications on React on top of Apache Spark [49]. It combines Spark's real-time data processing abilities with its impactful interactive user interfaces. This integration facilitates application development for big datasets with an easy-to-use interface.