The Road to MLOps: Machine Learning & Implementation Approach

November 10, 2021

MLOps is the automated deployment and operationalization of Machine Learning (ML) pipelines and models. It forms a bridge between data scientists and the operations/production team. It’s deeply collaborative in nature, designed to eliminate waste, automate, and produce more consistent insights through machine learning.

Today, machine learning can be a game-changer for businesses. But without some form of systematization, most ML projects don’t get past the initial experimental or prototype phase. MLOps brings business interest back into the forefront of ML operations, where ML experts and data scientists work through the lens of an organizational interest with clear direction and measurable benchmarks.

MLOps does not aim to restrict the freedom of the data scientists but rather enables data scientists to focus on what they do best, such as developing models to find the best answers. It takes business decisions entirely off the plate, so data scientists can build and deploy models that create insights more efficiently.

MLOps removes the barriers and lets the analytics data team find answers directly and efficiently. It follows a similar pattern to DevOps and drives a seamless integration between the DevOps cycle and overall operational process. Just like DevOps shortens production lifecycles by creating better products with each iteration, MLOps drives insights we can trust and easily puts them into productionthe process.

The complete MLOps process includes three broad phases of “Designing the ML-powered application,” “ML Experimentation and Development,” and “ML Operations.” It is an iterative and incremental process with continuous deployment, versioning, testing, and monitoring. It is comparable to the best practices of DevOps, coupled with unique features of the ML lifecycle like model retraining.

As an initial step, the design phase is devoted to business understanding, data understanding, and designing the ML-powered model. This stage aims to identify potential solution features and design the ML components that will solve complex problems. In general, there are two categories of business problems – either increasing the productivity of an application and end-user or increasing the interactivity of an application. During this phase, work is focused on inspecting the available data necessary to train the model and specifying the functional and non-functional requirements of said model. Furthermore, these requirements are used to drive the application architecture design, establish the delivery strategy, and prepare analytical datasets.

The next phase is the experimentation and development phase, which is devoted to verifying the applicability of machine learning for the defined business problem. ML models will be validated by proof of concept and prototyping, all the while ensuring the stability of the model through data and ML engineering. Model requirements will be conducted by leveraging validated datasets.Model accuracy and performance metrics will be validated using unseen data to ensure the model is suitable for business use.

Finally, the operationalization phase will promote the validated model into production through established DevOps practices and automated control of testing, versioning, continuous delivery, monitoring, and model retraining.

These three phases are tightly interconnected and influence each other. For example, the design decisions made throughout the design phase will propagate into the experimentation phase and finally influence the deployment options during the final operations phase.

Why MLOps

ML pipelines and models always target a specific business problem, and operationalization of these models aims to accelerate the uncovering of insights and turning those insights into actionable business actions.

First, we have already established that the data science team excels at developing suitable models to extract insight.  Their lack of ability to operationalizeexperience in operationalizing these models within the organization’s environment can stop projects from attaining their goals.  Through MLOps, we can introduce the operations team to the project to ensure that all broadened team skills can be successfully delivered.

Second, through MLOps, the organization can ensure that any regulatory requirements (either business-specific, industry-specific or operations-specific) are brought forward and dealt with through the appropriate subject matter experts.  This frees up the data science team to focus on their modelscore model development without the added burden of needing to understand the regulatory requirements.

Third, in the case where ML is used to improve and optimize business applications.  In this scenario, a deep understanding of the business context is required.  Through MLOps and having business subject matter expertise at the table, the data science team can focus their efforts on developing complex algorithms that are directly tailored to solving business problems. The organization of tasks through MLOps can ensure that each member focuses on their expertise to accelerate the delivery of the project.

The Steps to Implement MLOps

MLOps follows a similar pattern to DevOps, aiming to drive the seamless integration between the development cycle and overall operations process. Just as DevOps shortens the the production lifecycles and creates better products with each iteration, MLOps drives insights using the same automation and iteration model. MLOps includes the process of selecting the best model for making business predictions and automatically deploying that model into production.

After defining a business use case and establishing success criteria, the process of delivering an ML model to production involves seven steps. These steps can be completed either manually or through automatic pipelines, as intended with MLOps.

The process starts with data extraction, where the data engineering team selects and integrates various data sources for the necessary ML tasks. Then, the analytics team performs exploratory data analysis to understand the quality and availability of the data for building the machine learning model. The next step is data preparation. The team conducts a series of data cleaning, transformation, and feature engineering tasks to prepare the data for model development, development and splitssplitting the data into training, testing, and validation sets. The data science team will experiment and prototype several ML models best suited for the particular business use case and evaluate them on the respective validation datasets. Model validation confirms that the predictive performance of the model on unseen data is sufficient for business use and can be deployed into a higher testing or production-grade environment to be used within business applications. Finally, the underlying data and the model’s predictive performance is monitored regularly to potentially involve new iterations of the process if/when performance degrades or data drifts.

Why Trust Zelusit

Zelusit Corporation transforms businesses into digital leaders. For the past 20 years, Zelusit has been helping global organizations accelerate innovation, improve operational excellence, and create unforgettable customer experiences, all with the power of their data. By providing cutting-edge Artificial Intelligence, Big Data, Cloud, Digital, and Governance services and solutions, Zelusit helps enterprises leverage data they can control and trust, connecting them to their customers – and their customers to the world.

With continuous advancements in Machine Learning, Zelusit invests in ongoing learning to stay abreast of recent developments, including certifications and research partnerships with academic institutions and government supercluster programs. Zelusit focuses on providing practical applications that will give your business a competitive edge. From simpler regression models leveraging structured data to more complex models leveraging various types of structured and unstructured data, our team of highly qualified data scientists can build models that fit your specific business needs and data sets. Let Zelusit help your company achieve data quality excellence.