In the software development industry, DevOps(development operations) has shown a lot of potential and reliability for managing and scaling operations during any application development. When it comes to machine learning applications, the business must adopt the ML lifecycle as they follow in DevOps. Developing machine learning models in such a way not only helps in scaling ML applications but also lets us use the application of ML in real-time.
Developing ML applications in a DevOps manner is called MLOps, which is a new field, and we can also consider it as a blend of best practices we use in software development, data processing, machine learning and data science.
A prediction by Congnilytica says that the market for MLOps applications will increase by over US$4 billion. This data is simply presenting that the field of MLOps is in a surge of growth, and people in the field understand the importance and necessity of adopting MLOps for machine learning applications. MLOps is a set of practices that lets organizations optimize model development, deployment and monitoring. This article explores the dark area of machine learning development and discusses how MLOps helps brighten those dark areas. So let’s take start a discussion on how organizations face the problem with ML development and reasons why organizations need MLOps.
Main Reasons Why Organizations Need MLOps
Complex Deployment
When we look at the machine learning workflow of many organizations, they find deployment of machine learning models into their workflow is complex, or they don’t realize the benefits of applying AI and ML use cases. It has often been seen that the ML models are deployed but are slow performing, and the scope for scalability is low.
Deployment with MLOps
When applying MLOps with machine learning development, the following ease we can achieve:
- Multiple models, using multiple languages, and teams can be built.
- Gives ease of taking any model into production.
- Reduces the backlog of the stored models that are waiting for deployment.
- Allows data scientists to save time in troubleshooting models during deployment.
- Practices allow for taking models from experimentation to production seamlessly.
- Gives ease in updating many systems that come across the way of deploying models into production.
Lack of Model and Data Monitoring
Monitoring machine learning models and data flow lets organizations generate transparency between regular ML development. Organsation using the traditional system of ML development lack in monitoring data flow and model performance. MLOps makes this easy and gives the following noticeable changes in development lining.
Model And Data Monitoring with MLOps
- Gives access to monitor models in the production environment.
- Combines all the deployed models under one roof and provides a consistent way to monitor the model’s performance.
- Generates options for refreshing models that have been in production for a long time.
- Automates the traditional manual monitoring processes.
- Gives a transparent and insightful view of data flowing across multiple systems and models.
- Centralizes the monitoring system and allows one to get an overall view of multiple models and data pipelines.
Difficult Model Lifecycle Management
After monitoring the model performance and identifying the model and data decay, organizations need to provide updates to the models and data pipelines regularly. This process with traditional systems becomes brittle because manual coding is time taking and inaccurate way to update applications.
Lifecycle Management with MLOps
- MLOps makes it easy to update models in production.
- Less involvement of data scientists requires in production model updates.
- Building new projects becomes easy because of better management.
- Identifying model and data decay at early stages becomes easy.
Issues With Model Governance
Organizations spend huge amounts of time and money in auditing processes to ensure compliance with model governance. This makes the development, deployment and management of models slow. As well as traditional methods lack a centralized view of AI in production.
Model Governance with MLOps
- MLOps makes the process standardized, which makes compliance management easy to perform.
- Reduces the likelihood of errors by providing production access control, and this also lets the organization control model activity in case of fault performance.
- As it gives more flexibility in monitoring the models, tracing model results become more accessible.
- Monitoring also makes model audit trails easy.
Here in the above, we have discussed the problems organisations face during machine learning development and how MLOps help in making easy, reliable and scalable machine learning development procedures. DSW | Data Science Wizards has been offering to develop machine learning and AI use cases across the domain for a very long time. Because of our extensive experience, we know the importance of scalable and reliable AI in today’s scenario. We also know the important role of MLOPs in such a development procedure. So by considering the facts, we have developed our solution platform UnifyAI, which leads organisations to work around MLOps practice efficiently and ensure state-of-the-art results from their AI use cases. So let’s take a look at how UnifyAI can make MLOps adoption easier and more efficient.
MLOps via UnifyAI
As discussed in the above sections, we can say that there are various reasons which enhance the need for MLOps in industries for AI and ML development. Now that we all know MLOps makes AI development easy, we may also consider the fact that the adoption of MLOps is also a painful procedure. Recently a study performed by NewVantage Partners stated that only 15% of AI use cases had made it into widespread production performed by 70 leading enterprise companies. This tells the level of difficulties in adopting MLOps practice.
While MLOps provides a convenient and dependable approach to AI development for real-world situations, ensuring that the various components of MLOps are correctly aligned can be challenging. Furthermore, distributing workloads across these components can exacerbate the difficulty of adopting MLOps effectively.
UnifyAI has been engineered to facilitate the adoption of MLOps by streamlining all the essential components, such as feature store, model repository, model and data orchestrator, and monitoring system, to conform to MLOps best practices. The components that make up UnifyAI have demonstrated cutting-edge performance in development and are engineered to provide you with the flexibility to take AI use cases from experimentation to production. Not only does this platform enable you to deploy AI and ML use cases into production seamlessly, but it also ensures that your models adhere to the established governance standards in both development and production.
UnifyAI’s user-friendly features make it straightforward for organizations to deploy, monitor, and update their models in production. With UnifyAI, businesses can streamline their AI development process, which can significantly improve their return on investment (ROI) by accelerating the time to market and improving the efficiency of their AI initiatives.
Ultimately, UnifyAI empowers organizations to follow the best practices of MLOps to harness the full potential of AI while reducing the complexities and risks associated with end-to-end AI and ML development.
About DSW
DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.
Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.
Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.