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Overview on mlops

Author: Ankita Garg
by Ankita Garg
Posted: Jul 02, 2022

MLOps (a compound of Machine Learning and "information technology OPerationS") is a new discipline/focus/practice for data scientists and information technology (IT) professionals to collaborate and communicate while automating and productizing machine learning algorithms. MLOps aims to establish a culture and environment in which ML technologies can generate business benefits by rapidly, frequently, and reliably building, testing, and releasing ML technology into production through practice and tools. mlops training

MLOps captures and expands on prior operational practices while also extending them to address the unique challenges of Machine Learning.

MLOps (machine learning operations) is the process of streamlining and standardising the ML development and deployment lifecycle. A typical ML lifecycle is divided into four stages:

  • preparation of data (managing data sources, performing exploratory analysis, data cleaning, feature engineering)
  • model creation (identifying and choosing algorithms, running experiments, customising and optimising algorithms)
  • model development and testing (tracking)
  • model manufacturing and deployment (monitoring, performing explainability checks)
  • MLOps combines these stages into a process that promotes reproducibility, governance, and collaboration. Establishing and managing an effective MLOps practice within an organization is frequently a difficult task.

    Machine Learning is a collection of computer algorithms that can learn from examples and improve themselves without being explicitly programmed by a programmer. Machine learning is an artificial intelligence component that combines data with statistical tools to predict an output that can be used to make actionable insights.

    The breakthrough is the idea that a machine can learn from data (for example) to produce accurate results. Data mining and Bayesian predictive modeling are closely related to machine learning. The machine receives data as input and formulates answers using an algorithm.

    A common machine learning task is to make a recommendation. For those who have a Netflix account, all movie or series recommendations are based on the user's historical data. Unsupervised learning is being used by tech companies to improve the user experience by personalizing recommendations.

    Machine learning differs significantly from traditional programming. A programmer in traditional programming codes all the rules in consultation with an expert in the industry for which software is being developed. Each rule is built on a logical foundation, and the machine will perform the output that follows the logical statement. More rules must be written as the system becomes more complex. Maintaining it can quickly become unsustainable.

    About the Author

    I am an IT engineer with 5 years of experience. I have completed multiple trainings and certifications.

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    Author: Ankita Garg

    Ankita Garg

    Member since: Sep 21, 2021
    Published articles: 13

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