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MLOps, or machine learning operations, has become the new buzzword in the industry, is giving rise to new job roles, and businesses are deriving insane results after implementing it.

Currently, we are in a position where every other company is trying to incorporate AI and ML technologies into their products or services. The increased use of disruptive technologies in businesses has led to new developments that ensure better results. One such innovation is this engineering discipline called MLOps.

MLOps is the discipline of AI model delivery. It includes all the capabilities that data science, product teams, and IT operations have to deploy to secure machine learning and other probabilistic models in production. Machine learning operations combine the practice of using AI/ML with the principles of DevOps to represent an ML life cycle that exists alongside the software development life cycle for more efficient workflow and accurate results.

Benefits of using MLOps in business operations

• Ensures a secure business: MLOps can maintain the security of the enterprise through role-based access controls across different platforms for users, data, models, and resources to ensure efficient delivery of results.

• Enhance the productivity of the teams: MLOps integrates the business workflows and tooling systems to provide clear roles and reduce wasted time and hurdles between operations. It allows users to have constant access to monitor and report on current projects to make informed decisions beforehand.

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• Manage infrastructure: It systematically manages computation resources across different models to meet business goals and also ensures cost-effective operations. MLOps can be deployed on-premises, in the cloud, or in a hybrid environment.

• Risk assessment: Assessing the risks and the cost of failures is an important step to consider while doing business. This technology rightfully intercepts the financial damages done or might happen in the future to prevent further losses.

• Bridges communication gaps: A communication gap between the technical and the business teams are a common issue in several companies. The teams find it hard to come to terms with a common language to collaborate forces. MLOps bridges these gaps and ensures efficient communication for timely deliveries.

The best tools to use while deploying MLOps in businesses

• DVC: Data Control Vision, or DVC, is an open-source platform for machine learning projects. It is an experimentation tool that helps the users define their data pipeline, irrespective of the programming language they use. This platform can handle versioning and organizing extensive amounts of data and store them in a structured manner.

• Amazon Sagemaker: This tool enables developers and data scientists to easily build, train, and deploy machine learning models at any level. It is a cloud-based system that eliminates all barriers that slow down developers interested in machine learning practices.

• Pachyderm: It is a platform that combines data lineage with end-to-end data pipelines. It is available in three versions, sufficing the needs of individual users, the ones working in teams, and for large-scale organizational users.

• Polyaxon: It is a platform for producing and managing the entire life cycle of machine learning and deep learning projects. This tool can be deployed into any data center or a cloud provider, which is managed by Polyaxon. When it comes to the orchestration of projects, this tool provides the best services.

• Neptune: Neptune is a metadata store that is built for research and production teams that run ML experiments. Data versioning, experiment tracking, and registry allow this tool to act as a connector between different parts of the MLOps workflow.

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