## 4 Machine Learning (ML) Algorithms
### Progress of Machine Learning in Pumps Application  
As science and technology advance, artificial intelligence technology is increasingly being applied to mechanical manufacturing and automation. Artificial intelligence technology creates a production model through a computer simulation system and conducts extensive data analysis to make the relevant precautionary measures in the event of an emergency, which ensures an orderly production system, reduces the potential capital loss of manufacturing enterprises, and greatly improves manufacturing efficiency and accuracy (110). Industrial pumps are crucial components of all industries and require adequate maintenance, often known as condition monitoring. Additionally, the oil and gas industry views oil field digitization as a brand-new possibility for greater production efficiency. The crucial issue is how to put these tools into practice in such a way that all known risks are managed, value is truly delivered, the actual outcomes actually affect the operation's profitability, and, of course, they are applicable to specific, set production optimization targets. \begin{table}
\begin{tabular}{c c} \hline \hline
**Tools** & **Annotations** \\ \hline TensorFlow IBM & Collection of open-source software for performing computational calculations. \\ \hline IBM Watson Studio RapidMiner & ML framework developed specifically for an AI-driven business. \\ \hline RapidMiner & Embraces the entirety of the data science lifecycle, including but not limited to data planning, machine learning, and the deployment of prediction algorithms. \\ \hline Google Cloud AI Platform & Machine learning can learn from any data, in any amount. \\ \hline Box skills & Organize your data, then, at scale, draw insights from it. \\ \hline Google Cloud AutoML & Develop superior models that are tailored to their company’s demands. \\ \hline SAS Enterprise Miner & Simplifies the data mining procedure so that models may be quickly created and the most important patterns for processes can be found. \\ \hline MATLAB & A software package made by MathWorks that can be used for programming, modeling, and simulation. \\ \hline IBM Watson Machine Learning & Use preexisting data for model development, testing, and production use in machine learning and deep learning. \\ \hline Anaconda Enterprise & Leverage ML/AI/data science techniques. \\ \hline Amazon SageMaker & Models may be constructed, trained, and deployed rapidly, regardless of scale. \\ \hline IBM Decision Optimization & Uses both mathematical and AI methods to improve planning and scheduling decisions. \\ \hline IBM Cloud Pak for Data & Transforms its data gathering and data analysis practices to integrate AI throughout its organization. \\ \hline BigML & Learning through programmable machines. \\ \hline H\({}_{2}\)O & Early disease identification, medication discovery, and individualized medical care. \\ \hline Oracle Data Science Cloud Service & Model training, deployment, and management in the Oracle Cloud. \\ \hline Domino & Accelerate the process of creating and deploying predictive models. \\ \hline Deep Cognition & Build ML models without code. \\ \hline KNIME Analytics & A platform for data analytics, reporting, and integration \\ \hline Qubole & A platform for ML, streaming, and ad hoc analytics using data lakes. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Common ML tools. Aspects of oil production applications, such as reservoir management, production optimization, artificial lift, flow assurance, and predictive maintenance were among the important areas where prior research in this field had promised to further modernize technology. This primarily is focused on the application of machine learning to comprehend equipment status, in order to assist in predictive maintenance and save operational downtime. Electrical submersible pumps are one of the most extensively utilized artificial lift methods (ESP). Machine learning (ML) techniques have gained popularity in numerous academic domains, including CBM, in recent years [33].