According to the state of AI in 2020 research, 50% of respondents claim that their organisations have implemented AI in at least one business function. According to the report, only 17% of all respondents had a clearly defined AI vision and strategy, excluding “high performing AI enterprises,” according to the report. How organisations build their AI pipelines today will significantly impact how they maintain AI in the future. According to Gartner, 70% of organisations will employ cloud and cloud-based AI infrastructure to operationalise AI by 2024, significantly relieving concerns about integration and upscaling.
A high failure rate has been seen in projects involving analytics (including data science and machine learning). Various factors contribute to the failure of projects to achieve their business goals. To create the best AI pipeline for a company, one must first determine the risk mix of tools that will be used to address the various parts of the pipeline. Today, knowing the differences between Model Operations (ModelOps) and Machine Learning Operations (MLOps) has become important. The main distinction between ModelOps and MLOps is that the MLOps technologies focus largely on machine learning models, whereas ModelOps tools allow you to operationalize all AI models.
Evolution
MLOps is a field of study that allows data scientists and IT experts to work together and communicate while automating machine learning algorithms. It builds on DevOps ideas to facilitate the automated development and deployment of machine learning models and applications. MLOps is a practice that uses machine learning (ML) models on a regular basis. However, decision optimisation models, optimisation models, and transformational models have been added to applications, expanding the range and usefulness of models.
ModelOps is a progression of MLOps that includes not only the routine deployment of machine learning models but also continuous retraining, automatic updating, and synchronised development and deployment of more complicated machine learning models. ModelOps refers to the operationalisation of all AI models, including the MLOps-related machine learning models.
Activity
MLOps technologies facilitate collaboration across many teams and stakeholders involved in the development of AI-enabled applications (data science teams, machine learning engineers, software developers). Business leaders can use ModelOps technologies to get dashboards, reporting, and insights.
Operations
ModelOps is a company’s approach to managing the life cycle and governance of artificial intelligence and machine learning/decision models. It is essentially a centralised management system for all artificial intelligence initiatives, proofs of concept, and pilots. MLOps is the process of putting machine learning models into action, which is an important part used to standardise machine learning model deployment and management. It encompasses the people, processes, and technology required to achieve success.
Objective
Evaluation and development of machine learning models are carried out in a closed-loop experimental environment. Data Scientists, DevOps, and machine learning engineers utilise MLOps to deploy algorithms to production systems. Similar to DevOps and DataOps approaches, MLOps aims to increase automation and improve the quality of production models. Based on the DevOps technique used by the application development industry, ModelOps is a new way for application operations management. ModelOps is more concerned with delivering models from the lab through validations, testing and deployment phases as quickly and efficiently while maintaining quality results. Constantly checking and re-training the models is also a priority.
Conclusion
ModelOps is a holistic strategy to move models through the analytics life cycle quickly and iteratively so they may be deployed faster and generate desired business value, whereas, MLOps is a set of approaches for delivering and maintaining machine learning models in production in a consistent and timely manner. ModelOps is essentially a superset of MLOps with enterprise features. Data science teams benefit from MLOps technologies, but there’s still a gap between the teams designing and using AI and IT executives responsible for overseeing it. So, ModelOps comes into play, justifying its potential to be so game-changing.
