In one of our articles, we have seen how MLOps is a set of practices to bridge data science, machine learning, data analytics, engineering, and development. This bridging feature of MLOps made it a highly emerging option to adopt between many organisations. Nowadays, we can see this as a helper to organisations, professionals, and advanced systems to continuously and consistently deploy data models. This technology combines some operating technology components, people and a set of practices. In simpler terms, MlOps leverages data, technology and people systems to empower production-level machine learning.
While working on catering MLOps to many clients, we have observed that growing companies are using MLOPs to meet the evolving challenges in scaling machine learning. In this blog, we will look at some top MLOps trends and our predictions for 2023 in the field that is certain to become a leading aspect in the field.
DataOps with MLOps
This has been seen from the start that machine learning models are more reliable when the data we use to train them is appropriate. The term DataOps has been in use since around 2013 and has gained widespread recognition and adoption in recent years as organisations have become more data-driven and have faced increasing challenges in managing large amounts of data. This set of practices helps streamline the machine learning pipelines by fixing the quality of data coming to train and run machine learning models. Its impact has also been seen in advancing the growth of machine learning deployment.
DSW’s solution platform UnifyAI is designed in such a way that it can give you growth in AI and ML development, including both MLOps and DataOps practices. To learn more about DataOps, you can refer to this article.
Value Enhancement of AI/ML Solutions
The main reason behind the adoption of AI and ML solutions is that they can increasingly make an impact on global industries, and those who are applying them in their functions can quantify and track the value. The greater the visibility of the impact, the higher the enhancement of the organisation’s health, security, and reputation. To improve the visibility of the impact of AI and ML, we can track the quality of the model and data and the reusability of the data. After that, quantification of the impact becomes easier.
Here UnifyAI comes with its unique feature(observability), using which anyone can track the health of the applied model and data. Its advanced features increase observability and provide better monitoring. Enabled feature store gives you high data reusability using which you can go for CE/CD(continuous experiment and continuous deployment).
Technical Component Management in MLOps Deployment
MLOps is one of the trending topics in the field of data science and artificial intelligence that expands beyond the process of transforming AI and ML development. Participation of technology components to successfully implement MLOps is required, but managing those technologies better is more important. Only A good architecture can give the development of AI speed, accuracy, and scalability. It is crucial as well as tough to form a group of technology components from various sources and integrate together to follow best practices.
UnifyAI encompasses all the critical technology components, and its development follows a cutting-edge architecture, allowing organizations to concentrate solely on AI and ML development without worrying about architectural design.
MLOps will Enhance the Reliability of AI and ML
There is no doubt in saying MLOps and machine learning are advancing the industry, and it has been mentioned in many reports that many sectors of industries will continue to adopt AI. As MLOps is not only changing the way of AI development but also ensuring that models that are working in production are more accurate and robust. This way, organisations are more focused on adopting the SOTA way of implementing MLOps. We can say that companies adopting MLOps will trigger increasing investment in machine learning.
UnifyAI is developed to leverage MLOps and bring AI applications from the experimental phase to large-scale production with increased efficiency, making organizations more competitive in the industry.
Integration of MLOps will Remain Challenging.
Building AI and ML models are challenging, but streamlining and taking them into production is more challenging. Onboarding these models requires orchestrating workloads of technology components, balancing servers and giving them scalability in production. When we say making an organisation AI-enabled, is not just mean applying one or two AI applications in their processes, but it takes a load of AI models where some of the AI models are required to be trained and stored in repositories for further usage either in case of failure or in case of more accurate results and some of them to make it to the production with required scalability and robustness.
UnifyAI facilitates smooth experimentation and deployment of AI and ML models for organizations. It features accessible interfaces for the rapid creation and storage of new models, as well as effective management of stored or running models. An orchestrator, acting as the central component of AI systems, provides a seamless experience for distributing models, data, and workloads throughout the processes.
More Libraries and Packages for MLOps Tasks
Since MLOps enhances the capability and adaptability of machine learning models regardless of cloud providers or technical stacks, getting a one-stop solution will remain challenging. The reason being the number of libraries and packages is increasing rapidly and making it difficult to choose and become dependent on one. Being adaptable all time is a difficult process and causes a decrease in the speed of development.
In the development of UnifyAI, we have ensured that it can be easily integrated with new technologies, enabling users to adapt to changes and facilitating the acceleration of advancements.
The usage of Feature Stores will Increase
However, the technology is newer than the others, but it has become a mainstream component of MLOps. As it increases the reusability of the data features, enhances data collaboration between various teams, and allows faster experiments, it makes the MLOps more efficient.
UnifyAI incorporates a feature store to offer the advantages of using a feature store in MLOps. The orchestrator within UnifyAI obtains data from the feature store and passes it to the models in production, streamlining the deployment process and reducing the likelihood of errors.
Final words
In this blog post, we’ve discussed our predictions for MLOps in 2023 and its growing importance across various industries. We have found through working with organisations in different sectors that the proper approach to AI development is crucial for delivering more significant impact and value. Without scalability, robustness, adaptability, and sustainability in AI development, organisations fail to bring AI into production. Our aim through these predictions is to make AI accessible to all and guide them in the right direction using UnifyAI.
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.