MLOps: The Key To Pushing AI Into The Mainstream

In this blog, we will aim at discussing the reasons that make MLOps an essential aspect of pushing AI mainstream. Besides, we will highlight the capabilities of MLOps as a catalyst for AI implementation.

MLOps: The Key To Pushing AI Into The Mainstream
Photo by Miguel Á. Padriñán


As per the reports, the market for MLOps solutions is expected to grow to $4 billion by 2025. Thanks to the ever-expanding need for AI business technologies that are constantly improving operations at external and internal level for diverse industry verticals.  

Nevertheless, the sudden expansion of AI solutions in business has had a very strong influence on ML (Machine Learning) & advanced practices like DevOps. Since AI deployment that does not generate value could turn out to be an extremely expensive experiment to make, MLOps has pushed the pace for innovation in order to enhance AI execution.  

In other words, integrating MLOps into the AI development process could help streamline everything from deployment to monitoring and production with added ROI. Therefore, integrating AI into business can happen to be a huge technical accomplishment. 

However, one of the most significant roadblocks that prevent business organizations from leveraging AI into operational action is driving success throughout the development process to training and production. More importantly, progressive implementation of AI involves achieving the necessary speed and scalability to the existing business environment which often becomes a tough task to achieve.  

The need for greater stability and success, therefore, demanded DevOps intervene in AI deployment. Besides, the more forward-thinking enterprises have started relying on Machine Learning in the conventional DevOps models making space for MLOps. From streamlining the AI implementation to automating the process of developing smart applications, MLOps has shown the potential to drive value from deployment on a continual basis.  

In this blog, we will aim at discussing the reasons that make MLOps an essential aspect of pushing AI mainstream. Besides, we will highlight the capabilities of MLOps as a catalyst for AI implementation. 


MLOps as a Catalyst to AI Implementation 


Since MLOps brings the best of Machine Learning and DevOps to AI implementation, there are so many ways  MLOps drives more value to AI initiatives of enterprises. Some of these could be listed as: 


Improved Deployment 


The primary advantage that MLOps brings to AI implementation is improved deployment. Be it accommodating multiple teams or languages used on the builds, it aids deployment reducing the backlog of models. Besides, MLOps helps in standardizing the models to complement everything from development to production, cutting any flaws and reducing time for troubleshooting. 

Machine learning and DevOps capabilities when combined allow reducing the complexity of AI production while fastening the essential updates on multiple systems.  


CI/CD Integration 


Another significant reason why MLOps is integral for AI implementation is the advantage it brings for CI/CD integration into the process. Every time you update your code or data, the process allows rerunning the machine learning pipeline. In other words, MLOPs for AI implementation allows early integration before a new model is released allowing validation of new data and code. 


Advanced Monitoring 


The next important factor to AI implementation that is complemented through MLOps is the monitoring aspect which is usually skipped during production. MLOps allows keeping consistent checks on different models deployed across organizations in a very consistent manner.  

More importantly, it allows refreshing all the models that have stayed long in production while the determination of the model performance is worked through ML technology itself keeping the stress off the shoulders of data scientists. In a nutshell, MLOps for monitoring offers a more centralized way to view model performance for AI implementation nurturing greater accountability.  


Lifecycle Management 


Another significant issue that AI systems in enterprises encounter is the inability to assess model decay. However, MLOps allows meeting the resource-intensive needs of the lifecycle management process allowing updating models within production and constantly checking on model decay after initial deployment.  

MLOps allows negating potential outages cutting the involvement of data scientists in production model updates. Moreover, MLOps even helps to meet the high maintenance needs of the existing and upcoming models of the AI system. 


Model Governance


Since enterprise involves costly audits to align with compliance requirements relating to deployment, and modeling language, the centralized process on MLOps makes things easy. From production access control to traceable model results, model audit trails, as well as model upgrade workflows. 


How MLOps Pushes AI Forward? 


MLOps as a digital practice helps business entities to overcome several issues related to the building, deployment, and management of smart applications. Especially, when there are large data sets for training that are constantly expanding in the case of AI enterprise implementation, MLOps jumps in complementing monitoring, adjustment, and retraining of AI models. ML thus makes the process much more affordable in terms of resource consumption on production AI models. Here’s how MLOps therefore push forwards AI modeling in enterprise: 


Improving the Environment 


One of the most important reasons that makes MLOPs a necessity for AI implementation in business is improving the environment. From data sanity check routines to DevOps processes that respond to the change in model code.  

Besides, MLOps implementation for AI deployment allows saving time by creating a window for reusable assets. Be it the setup of dynamically scalable infrastructure or automating of services like creation of wrapper REST API, Data Drift analysis, or Security through Multi Factor Authentication.  


Meeting the MLOps Requirements 


Effective implementation of MLOps allows access to various capabilities such as full lifecycle tracking, metadata optimizing, hyperparameter logging, and most importantly AI infrastructure that contains the best of networking, storage, and servers.  

Besides, the process even requires software technologies that can help with the rapid iteration of ML models. Also, the process needs to be shaped around two forms of MLOps which include predictive and prescriptive. The former works at past data to chart future outcomes while the latter aims at making recommendations for decision making.  

The process is an extremely important aspect of trickling AI to every organization, irrespective of its size. Most of the time, it is the failure of ML projects under traditional deployment practices that hampers the development of AI systems.  

And if you are able to align well with MLOps requirements, it contains the potential to turn all your failures into success. Above all, the process can help lift all barriers to the AI implementation complementing enterprises on mainstream data operations.  


The Much Predictable Success


Though the concept of ML is very young and so is the term MLOps, in implementation it has proven to be much more than just a buzzword. Thus, designing and targeting MLOps in the proper manner requires effective coordination between various components of the MLOps environment.  

It includes CI/CD pipeline, data monitoring, model serving, and version control while keeping a check on security and governance mechanisms. Such an approach could not only minimize the risks surrounding ML activities but even diminishes the chances of any compromises.  

At times, it may appear like the element of autonomy could take over humans, there are reports that have justified how AI cannot be scaled without teams who can implement and command the system.  

In a nutshell, MLOps is an extremely important component of tech diversification. From development to deployment and progressive management of AI projects, working on MLOps and aligning with AI systems requires a broad human intensive skill set that can help create the enterprise business models of the future.  


The Crux


Even the most advanced digital initiatives could yield no or little value if the effort made on the transition is compromised at any stage from lab to real-time implementation. And since Artificial Intelligence (AI) is one of the most dynamic tech innovations that are likely to change the future of humans, it is necessary that every innovation surrounding AI must be made to yield practical value.  

Considering the extensive myths and complexities surrounding AI and ML technology, betting completely on MLOps may involve unexpected outcomes. However, a single move of success or any action plan that can foster the best of AI through ML advantage could transform the whole enterprise world.  

More importantly, the efforts made in the right direction could turn out to be of great value in cutting the cost of experimentation and overcoming the chances of failure. Besides, putting the technology in the hands of more people who could figure out effective implementation of the AI and ML could help create a more productive digital world.  

To conclude, MLOps allows businesses to realize the total benefits of AI initiatives with every model deployment made. Besides, harnessing Machine Learning and DevOps could help enterprises to progress ahead of time on their AI goals bypassing any distractions encountered during model development.  Right from delivering the speed to the deployment goals to aligning with the scalability requirements, MLOps works as a catalyst to the development of AI-based business solutions. All in all, it can actually be the next big move to managing and governing AI.  

Kanika Vatsyayan is vice-president, delivery, and operations at BugRaptors, a certified software testing, and Quality Assurance company. She is a QA professional with a grip on several leadership positions such as test program planning, innovation, and process transformations. From quality control to test leadership, test practices, and assurance strategies, Vatsyayan is a seasoned expert with influential tech skills. Besides this, she has a knack for writing and therefore has published countless articles and blogs educating audiences across the software testing industry.