(ML) projects are developed and deployed to cure industrial business problems. They always try to create efficient projects with minimum time. It requires automating the ML pipeline, which is also one of the major challenges for them. To battle this issue, MLOps or machine learning operations came into existence.
This blog taps into machine learning operations (MLOps) overview, definition and architecture. It will help beginners get a better idea about this technology and how it's useful for organizations.
Machine learning aids companies in analyzing data and deriving valuable insights to push better decision-making. In spite of being a highly useful technology, it does not come without its own unique set of challenges and issues. Right control and guidance are needed to ensure that data science teams get their desired result.
To tackle these challenges, MLOps came into existence. This practice is a guiding map to steer ML projects towards success, irrespective of the constraints. There are many benefits of implementing MLOps including-
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The term comes by amalgamating the terms machine learning (ML) and operations (Ops). This set of management practices have become vital to ensure proper deep learning (DL) or production ML lifecycles. Just like DevOps, it is also neither a technology nor a tool.
It's a practice that entails the optimum utilization of various such aids available in the market. The goal is to reliably and efficiently deploy ML models in production as well as maintain them. Various DevOps, data engineering and ML processes are employed to make it happen. Uninterrupted collaboration and communication is promoted between data scientists and operations professionals.
This approach shares quite a lot of similarities with DevOps, DataOps and ModelOps. Its focus is to enhance the quality of production ML and expand automation to maintain attention on business and regulatory requirements. In recent years, it has experienced dramatic changes and it's still evolving.
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MLOps architecture refers to a comprehensive framework comprising different processes and components of the ML pipeline.
MLOps ensures that machine learning models work reliably, scale well and stay updated over time.
DevOps focuses on software delivery, while MLOps focuses on managing the full lifecycle of machine learning models.
Popular MLOps tools include MLflow, Kubeflow, TensorFlow Extended (TFX), Docker, Kubernetes and cloud platforms.
| Course Name | Batch Type | Details |
| MLOps Training | Every Weekday | View Details |
| MLOps Training | Every Weekend | View Details |
