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The data science and data analytics solutions helps to deep dive into the business processes and operations to analyze the critical heads-up situations in the industry to know the upcoming trends to quickly resolve the unpredictable, saving effort, time, and energy. The business data-driven approach helps understand the critical inputs to derive qualitative results. The current data science technology can integrate artificial intelligence, Machine learning abilities along with data analytics to focus on deviated inputs.

 Five best practices to follow
 1. Clear understanding on business requirements The clear set of requirements understanding on what has to be achieved and what process area needs to be evaluated, identified and corrected. Once the problem statement is formulated with stakeholders for specific process it becomes easier to generate the roadmap and assignment of KPI’s

 2. Goal and strategy Once the problem is identified the goal along with timelines are mapped and an equivalent strategy is imposed. The effective data acquisition and resources needs to be implemented Once the problem is identified the goal along with timelines are mapped and equivalent strategy is imposed. The effective data acquisition and resources needs to be implemented.

 3. Selection of appropriate tools The selection of appropriate tools are necessary to be decided in advance. The data scientists prefer to work in languages such as Python. The relevant infrastructure needs to be in place The tools like Business Intelligence tools, SQL Consoles, MATLAB, Python, R and RStudio, BigML, Jupyter, Apache Spark and much more could be used.

 4. Set of standard practices ( MLOps) The standard set of practices use to communicate between the data scientist, developers and other stakeholders follow through MLOps. The standard set of practices is also derived from DevOps intersecting with ML and Data engineering. Such practices helps to increase the quality deliverables and automate the deployment of ML and AI models. The MLOps serves as a guideline to accomplish the business goals with right KPI’s ticked in the delivery roadmap. The MLOps helps the data engineer to work in an organized manner reducing the rework and iterations and reducing waste to increase the automation power.

 5. System updates The developed system needs to be evaluated for updates in technology and also the business processes. For the model to be working as expected, you need to upgrade on the model based on the business requirements and customer expectations. As the business process and landscape may change, the ML model also needs to be corrected and updated to get the best results.