Machine Learning Operations Overview

MLOps comes by amalgamating machine learning and operations. It is a set of processes, tools, operational strategies, and best practices. It concentrates on creating a framework for highly scalable and consistent ML model development lifecycles.

MLOps is considered to be a collaborative function. Hence, many professionals are involved in the process, including

• Data scientists
• DevOps engineers
• Machine learning engineers
• Leaders across different vertical
• IT team members

You May Also Read - MLOps Tutorial- A Complete Guide For Beginners

What is the Use of ML Ops?

Businesses consider MLOps to be an exceptionally useful approach, especially for creating and maintaining quality ML and AI solutions.

With this approach, ML engineers and data scientists are able to collaborate, increasing the pace of developing and producing models. This happens as they are able to collectively implement CI/CD practices with proper governance, validation, and monitoring of ML models.

What are the Benefits of ML Ops for Businesses?

Some of the biggest benefits of MLOps for businesses include:

1. Automation & Repeatability

Every step mentioned in the ML data catalog can be automated. Processes like training, deploying, evaluating, and versioning of models are repeatable too. Hence, with MLOps, maximum efficiency is created. Consequently, data scientists are left with more time to focus on researching, innovating, and finding insights.

2. Productivity & Innovation

Rapid innovation is certainly a key benefit as this practice brings together different data processing teams, IT engineers, and analysis professionals. Everyone gets access to curated data sets due to the self-service environments it offers. Hence, increasing the speed of development and deployment.

3. Rapid Validation Process

With MLOps, engineers get a demonstration on how to build and where to deploy the models. As a result of automatic reporting, algorithms become transparent. Hence, the validation process becomes faster.

You can enroll in our latest MLOps Course Online program.

How Does ML Ops Work?

For an ML model to become production-ready, it must undergo certain stages. Here are four of those stages.

1. Data Cycle: Data cycle involves collecting and preparing data for training. After raw data is extracted from the right sources, many techniques are applied to it.
2. Model Cycle: Here, the model is trained with the data prepared above. Tools such as MLflow are used to simplify this cycle.
3. Development Cycle: The model is then developed, tested, and validated further to be deployed in the production environment. This can be automated with CI/CD pipeline. 
4. Operations Cycle: This is the monitoring process to ensure continuous working of the production model. It improves the performance over a period of time.

Wrap-Up

Machine Learning Operations or ML Operations has become a huge field with a wide scope of growth. Becoming an MLOps professional will benefit your career in the long haul.

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