Operationalizing machine learning to drive business value is hard. With Machine Learning (ML) & Artificial Intelligence (AI), while it has become easy to build models, translating them into reliable and scalable software to bring business value is hard.
Companies are getting more and more interested in applying DevOps principles for Machine Learning (ML) based software. DevOps is bringing together people, processes, and tools to produce continuous values at high velocity by connecting Development and Operations in a continuous pipeline. Over 50% of enterprise technology decision-makers are already implementing or are in the process of implementing ML and AI, as per data released by Forrester.
That is where MLOps comes into the picture. A convergence of Data Engineering, DevOps, and Machine Learning, the functionality of MLOps is like DevOps, but the execution is different.
Machine Learning
The process of learning guided by data to achieve valuable insights from existing information is called Machine Learning. Advanced machine learning algorithms are comprised of many technologies, including deep learning, neural networks, and natural language processing. It refers to the tools and techniques used to create Artificial Intelligence algorithms.
With digital transformation, data is enabling professionals across all sectors to dig insightful learning from the sea of data to predict or prevent repeating mistakes. While developers used to write algorithms using a programming language before that enabled them to define the tasks to be performed, with machine learning, the scene is changing. With this changing paradigm, developers can design an algorithm framework that enables the machine to learn from some input data.
From managing datasets to monitoring models and building processes that are shareable throughout an organization, there is a lot to keep track of in an organization. With Machine Learning Operations (MLOps), many of these best practices can be codified into one place.
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What is MLOps?
MLOps is a set of procedures for collaboration and communication between data scientists and operations professionals. By applying the MLOps practices, developers can increase the quality, simplify their process, and automate the deployment of ML and Deep Learning models in different environments, making it easier to align models with business and regulatory requirements.
MLOps is evolving as an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle, ranging from data gathering and model creation - software development lifecycle and continuous integration/continuous delivery - to orchestration, deployment, diagnostics, governance, and business metrics.
As per Gartner's report, MLOps is considered a subset of ModelOps. While MLOps is focused on the operationalization of ML models, ModelOps encircles the operationalization of all types of AI models.
The key stages of MLOps include:
- Data gathering
- Data analysis
- Data transformation
- Model training & development
- Model validation
- Model serving
- Model monitoring
- Model re-training
MLOps comprises of detection of model drift that can trigger the process of automated training, evaluation, and deployment. A model drift is a widening gap between the predictions and expected results. MLOps adds continuous training to the workflow that is triggered by the continuous evaluation process, employed to continually compare the accuracy of model predictions.
Importance of MLOps Integration
MLOps integration is fundamental. Machine learning helps in deploying solutions to unlock previously untapped sources of revenue, saves time, and reduces costs by providing more efficient workflows, leveraging data analytics for insightful decision-making, and improving customer experience.
For businesses, these goals are hard to accomplish without a solid framework. By automating model development and deployment with MLOps, businesses and individuals can experience faster go-to-market times and lower operational costs. It also enables managers and developers to be more agile and strategic in their decision-making.
MLOps serves as a framework to guide individuals, team members, and businesses to accomplish their goals, irrespective of the constraints, be it sensitive data, shortage of resources, or small budgets.
Understanding Computer Vision
Computer vision models are becoming insanely sophisticated as their wide variety of use cases help to enhance business effectiveness, automate critical decision systems, and so on. While these models can seem costly, developing and deploying computer vision models that function effectively matters a lot!
Machine learning engineers are embracing DevOps practices in their model deployment systems. But it is essential to consider several aspects like code versioning, deployment environment, continuous training, production model monitoring, data drift & quality, and model features & hyperparameters.
For tasks that involve recognizing or analyzing visual data, Computer Vision can help in building more powerful and automated solutions. Computer vision tasks involve feeding the software a large amount of training data along with the expected output and allowing them to construct and refine a model based on the specific use case. While the training process is time-consuming and expensive, the resulting system is likely to yield significant gains in speed and convenience with accuracy and reliability.
Computer vision is only a component of intelligent automation. Implementing computer vision requires business and interpersonal talent. To help the project succeed, it is important to understand the repetitive parts as well as the more fulfilling and creative parts.
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Employing MLOps for Computer Vision
A field of Artificial Intelligence, Computer Vision involves applying learning algorithms to image-like input types, including videos, pictures, and hyperspectral images. It differs from other types of data like tabular data or text due to the size of the files. Computer Vision enables machines to perceive, observe, and make sense of the images presented, just like humans.
Computer Vision models assist software in understanding visual cues and making sense of them. When data is fed into them, the algorithms train themselves to differentiate the images from one another and detect and segment objects.
It can be inferred that computer vision involves:
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Data management
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Creation of algorithmic models
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Deployment
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Error Analysis
Hence, it is evident that MLOps is critical to gaining effective outputs from computer vision algorithms.
While prototyping Computer Vision models is simple, building an integrated ML system that works efficiently is extremely difficult.
The natural progression is to develop a fusion of Computer Vision and MLOps to build CVOps. This helps in creating a set of steps and processes dedicated exclusively to Computer Vision projects.
CVOps: An Amalgamation of CV and MLOps
CVOps involves employing steps and processes of MLOps exclusive to computer vision to accomplish the development and deployment of computer vision projects.
Stages of CVOps:
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Data and feature management, which incorporates data collection, creation, management, verification, and processing.
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Model development; involves training the ML models, managing metadata, along with hyperparameter tuning and model registry maintenance.
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Operationalization, which includes-
- Deployment of the ML model to a right server
- Execution of CI/CD/CT in the ML pipeline
- Monitoring of CV model in production
ML platforms are used to achieve distinguishable steps and to automate the pipeline. By employing MLOps, computer vision projects can be pushed to the deployment stage by following a CI/CD/CT pipeline.
Read more: Bias in Artificial Intelligence: Is Diversity the Key to the Future Of AI?
In Conclusion
Today, software built for businesses is envisioned to be scalable, reliable, and efficient. With the ongoing digital transformation, in order to reap the benefits of AI, businesses need to understand and integrate the models that are increasingly driving business decisions.
For years, corporations have optimized the way they build, run, and maintain software through DevOps, and now is the time to employ the same for machine learning. Uncharted territory for years, businesses are now opening up to the idea of employing AI to scale through years of trial and error. With MLOps, they are ensuring that their business derives the most value from machine learning.
MLOps provides the best iterative development implicated in training machine learning models and scalable model deployment. A missing piece of the enterprise AI strategy puzzle; it is time for enterprises to understand the significance of MLOps and adopt it as an integrated framework to implement machine learning projects.
Employing MLOps for Computer Vision helps create an automated ML pipeline to make model improvements quicker and more reliable. As a result, CVOps aids organizations in bringing reliable Computer Vision systems into production.
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