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Differences Between MLOps, ModelOps, AIOps, DataOps
In the IT industry, each of these operational domains is cross-functional and provides a distinct value. And each of the Ops domains stems from a common broad mechanism of agile concepts, which were originally initially developed for the guidance of software developers for their development, but now have spread its wings to other domains of related technologies that are data-driven applications, AI, and ML. In this post, you will come across the popular terms in the world of Artificial Intelligence that have emerged to great extent.
What is MLOps?
MLOps is a collaboration and communication platform for data scientists and operations experts to manage the production ML lifecycle. It is a machine learning engineering culture and practice that aims to unite the creation and operation of machine learning systems (Ops).
MLOps was born out of the need for machine learning engineers to manage their processes in production rather than relying on data or infrastructure teams. Model training and re-training, model deployment and integration with data pipelines integrating ML models into production workflows and systems, automating ML model lifecycle management, monitoring model performance in production, updating models as needed to reflect new information, and integrating ML results into business strategic processes are all steps in MlOps.
Here is the list of some popular tools and platforms for MLOps −
Domino Data Lab
Cloudera Data Platform
What is ModelOps?
The methods and processes involved in designing, managing, deploying, and integrating Machine Learning models, as well as monitoring them, are referred to as ModelOps. It considers the construction and operationalization of various forms of models, including Machine Learning models and others like rule-based models and Knowledge Graphs.
ModelOps necessitates the same abilities as MLOps, plus a few more in IT operations, risk management, governance, and a few other areas. As the initial step in the ModelOps process, businesses must monitor the operation of the ModelOps software. During development, validation, and deployment, you must keep track of each model's progress, set accuracy targets for models (or model classes), and monitor them for dimensions like drift and deterioration. Continuous Integration and Delivery (CI/CD), development environments, testing, model versioning, and model store are all examples of ModelOps.
Here is the list of some popular tools and platforms for ModelOps −
What is AIOps?
Artificial Intelligence for IT Operations is abbreviated as AIOps. It's a paradigm change that allows machines to handle IT problems without requiring human intervention. AIOps examine huge data gathered through various methods using machine learning and analytics, allowing issues to be detected automatically and dealt with in real-time. One of the main differentiators for AIOps platforms is their capacity to collect all kinds of data in varied velocity and volume, which simplifies data analysis. The platform then uses automated analysis of the data to predict and avoid future problems, as well as to pinpoint the source of current problems. AIOps not only streamlines IT processes but also anticipates unforeseen challenges, allowing for proactive administration of digital services. IT Ops teams are experimenting with the notion, and the AIOps industry is still developing.
Here is the list of some popular tools and platforms for AIOps −
What is DataOps?
DataOps is the use of agile development principles to swiftly and cost-effectively produce, distribute, and optimize data products. Modern data teams, which include data engineers, architects, analysts, scientists, and operations, practice DataOps.
It is a data management technique that stresses collaboration between data engineers, data scientists, and other data professionals through communication, collaboration, integration, automation, and monitoring.
It all began with the emergence of big data. Any organization that you can think of deals with vast amounts of data that comes from a variety of sources and is stored in a variety of forms. The data must be in a format that you can comprehend and analyze to answer your business queries. As a result, all of the data you collect is subjected to a series of adjustments (i.e. data and analytics pipelines). DataOps provides data consumers, internal and external stakeholders, and customers with the information they require at the time they require it. In the data economy, this gives businesses various competitive advantages.
Here is the list of some popular tools and platforms for DataOps −
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