The traditional machine learning approaches rely on using open-source tools for data analysis and prediction making. This approach does not work out well when the data is large. The RAM on the system gets damaged when large files like these are involved. We need to use an approach that not only helps us build the machine learning models successful but also ensures that the system is not burdened or damaged while an operation is being performed. Hence, we need to learn Distributed Computing in Machine Learning.
What is distributed computing?
An approach to improve the system performance, resolve scalability issues and increase the system efficiency by dividing the task being performed on a single machine to different systems is called distributed computing.
Distributed computing has many applications such as the world wide web, global financial systems, machine learning and much more. Here we concentrate basically on the concepts of Machine Learning Training with distributed computing.
Distributed computing trainingÂ
The main purpose of this training in machine learning is to help an individual master the skills in machine learning and resource allocation and management. Distributed computing came up as a technique to resolve the scalability associated with machine learning algorithms. It developed on a massive scale in recent years to provide large-scale operations such as big data analysis efficiently.
When we talk about distributed computing, there are two main approaches:
- Horizontal fragmentation- It uses an approach to store the selected portions of the available instance at different sites.
- Vertical fragmentation- Storing of the selected attributes of the subsets of the instances comprises of vertical fragmentation.
The data involved in machine learning is very massive if a real-time problem is involved. A situation might be encountered where the machine learning model needs to be trained again and again without disrupting the ongoing parallel task. In this situation, distributed computing serves as a boon by resolving the issues.
The training in distributed computing also highlights the importance of applying these techniques in fields such as medical computing where huge amounts of data are uploaded at every instance of the given time and need to be analyzed for relevant purposes.
Distributed machine learning platforms
Training in distributed computing for machine learning also provides information about the platforms that been developed to do so. Some of these platforms are listed below:
- H2O- Developed by H2O.ai, H2O is an open-source platform for distributed computing in machine learning with in-memory support. It also provides support for traditional machine learning algorithms and includes AutoML functionalities.
- TensorFlow- Distributed TensorFlow provides different servers each of which is considered to be a cluster and each process is made to run on an executive search engine.
- DMTK- It stands for distributed ML toolkit and is developed by Microsoft to provide highly efficient techniques for performing a machine learning task.
Apart from the frameworks mentioned above, there are other frameworks such as Apache Spark Mlib and Apache Mount that assists in the machine learning applications as well.
Conclusion
Most of the problems that we encounter today are voluminous and very hard to process for machine learning tasks. Distributed computing left its footprints in the field of machine learning by solving one of the major issues that are big data handling. It has gained a lot of popularity in recent years because of its high degree of scalability, efficiency, and performance. It has not only helped in performing large-scale computations but has also helped in the optimization of the operating systems. To be accurate, it has revolutionized the world of machine learning training and computations.
