ML/AI and Data analytics are part of the day-to-day today. And many have taken this seriously and invested in Machine Learning Operations (MLOps). Mostly by introducing practices and tools/software (e.g. MLFlow, Kubeflow, AWS Sagemaker etc) that aim to operationalize the development, deployment, and management of machine learning models.
Data is one of the cornerstones of the successful port merger integration activity, and for many, it is ELT (Extract Transform Load) that executes on it. While ETL and MLOps share some similarities, such as data processing and transformation, they serve different purposes. ETL is focused on data integration and management, while MLOps is typically focused on the operationalization of machine learning models.
And here is the point. Data today (besides traditional systems e.g. ERP, Core etc.) is not relational anymore. Decisions are insight-driven and require all types of data to be analyzed and taken holistically. And if you want to maintain a moment, and retain a competitive position, while undergoing post-merger integration it is better to take data seriously. MLOps in this scenario can help automate the process of integrating and deploying machine learning models, as well as monitor their performance and maintain their accuracy over time. And even better one start adopting MLOps approaches and thinking, tools when thinking data layer. Here are some ways MLOps can help:
- Streamline data integration: MLOps can help streamline the integration of data from different sources by automating the data integration process. This can reduce the time and effort required for data integration and ensure that the data is accurate and consistent across different systems/sources.
- Automate processes: MLOps can help automate tech processes such as data cleaning, data preprocessing, and data transformation. This can reduce the time and effort required for these processes and ensure that they are carried out consistently and accurately.
- Improve data analysis: MLOps can help improve data analysis by automating the process of training machine learning models and deploying them in production. This can help companies extract insights from large volumes of data more quickly and accurately.
- Enhance collaboration: MLOps can enhance collaboration between IT and business teams by providing a common platform for data analysis and model deployment as you integrate organizations. This can improve communication and collaboration between teams, ensuring that everyone is working towards the same goals.
- Ensure data security: MLOps can help ensure data security by implementing robust security protocols for data storage, data processing, and data transfer. This can reduce the risk of data breaches and ensure that sensitive data is protected.
Hope the above summarized the argument well and illustrated a point. Happy to discuss it in any case…
