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Using MLOps to Quickly Deploy and Sustain Machine Learning Pipelines

Machine learning (ML) technology now underpins customer applications and the operations in many industries. Machine learning powers route-planning in logistics, disease diagnosis and treatment in healthcare, optimization of supply chains in manufacturing, risk reduction in financial services, and customer-facing applications such as search and recommendation systems and fraud detection services.

Businesses from every industry leverage the benefits of feeding operational data into powerful machine learning models to boost efficiency and profitability. But building and maintaining machine learning pipelines can be time-consuming and costly. Machine learning operations (MLOps) aims to change that. The goal of MLOps is to standardize and streamline the machine learning process that spans data and feature engineering, model development, and model production. In doing so, they can achieve the scale, reproducibility, and governance needed to effectively productionize ML initiatives.

optimizing ml lifecycle and mlops white paper

What Is MLOps?

MLOps is an engineering process used to streamline and standardize the entire machine learning lifecycle. This includes standardizing the machine learning development, validation, and reporting processes so that machine learning models and their data pipelines can be rapidly brought into production where they are maintained and monitored.

Functions of MLOps

Data science teams depend on tight collaboration with data engineers, ML engineers, and DevOps engineers—and with the business owner. Ultimately, MLOps provides cross-disciplinary teams with a robust, easy-to-replicate framework for collaboratively developing machine learning workflows.

MLOps enables easily repeatable data exploration, real-time coworking capabilities for experiment tracking, feature engineering, and model management. This robust end-to-end process also includes controlled model transitioning, deployment, and monitoring, making it possible to automate the operational and synchronization components of the machine learning lifecycle.

MLOps vs. DevOps

MLOps is an offshoot of the DevOps process, which is focused on streamlining the development, testing, and operational tasks associated with software. Where DevOps aims to shorten the development cycle for producing and releasing a software product, MLOps aims to do the same for the machine learning lifecycle.

Benefits of MLOps

Developing and deploying machine learning pipelines can be complex and cumbersome. As a result, many machine learning products die in production before they’re ever released. MLOps accelerates the process of developing and releasing machine learning pipelines, enabling diverse teams to work in tandem. Let’s look more closely at six benefits of using an MLOps process.

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Lifecycle speed

MLOps is a standardized process for creating machine learning pipelines with easily repeatable workflows. A machine learning model can smoothly move from inception to deployment without the typical months-long process of haphazard collaboration between the different specialized teams involved in the project.

Increased collaboration

Developing machine learning products frequently involves teams with multiple specialties including IT, DevOps engineers, and data scientists. Without a development framework that actively encourages collaboration, teams tend to operate in silos, resulting in costly bottlenecks that waste valuable time and resources. The MLOps process requires that teams coordinate operations, orchestrating collaboration efforts that result in faster development times and a finished product tailored to meet the business objective.

Compliance with regulatory and data governance standards

As the field of machine learning grows, regulations and industry standards have tightened. For example, the European Union’s General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA) both have implications for machine learning applications. The MLOps process makes it possible to replicate machine learning models that comply with relevant governmental and industry standards.

Scalability

With its focus on standardization, the incorporation of MLOps processes enables organizations to rapidly grow the number of machine learning pipelines they develop, manage, and monitor without the need to dramatically expand their teams of data professionals. For this reason, MLOps makes ML initiatives highly scalable.

Reusable features and models

Machine learning features and models developed using MLOps processes can be easily repurposed to meet new business objectives. Reusing existing features and models further reduces the time to deployment, achieving valuable business outcomes faster.

Safeguard against bias

Underrepresenting certain demographic groups can result in creating machine learning algorithms that are inherently biased. The MLOps process helps to mitigate this risk by ensuring that certain factors within a data report do not overshadow others. This built-in safeguard helps to produce machine learning products that fairly represent all groups.

Challenges to Implementing MLOps Effectively

MLOps is not without its challenges. Here’s what to consider when developing and deploying your machine learning pipelines.

Computing power bottlenecks

One of the biggest barriers to implementing MLOps is a lack of computing power. Machine learning algorithms require an enormous amount of resources to run. On-premises systems often struggle to satisfy the intense demands of ML while continuing to adequately meet the computing needs of other business areas. In contrast, the compute power provided by a cloud data platform elastically scales, providing dedicated resources for executing any machine learning task, from data preparation to model training and model inference. Most MLOps initiatives will require using cloud-native solutions.

Lack of unified data store

Machine learning algorithms must be fed enormous amounts of data to deliver quality results. Many organizations struggle with siloed data stored in various locations in different formats. For this reason, you’ll need a cloud data platform that offers the ability to store all data, or any structure (that is, structured and unstructured) in a centralized repository with consistent data security and governance.

Difficulty in integrating workflows

Machine learning data pipelines must be flexible enough to handle evolving data requirements over time. Using a cloud data platform built for data science workflows makes it simple to construct machine learning pipelines that can be easily adjusted when needed to continue providing high-quality outputs.

MLOps and Snowflake

Snowflake’s Data Cloud platform provides full elasticity that allows MLOps and machine learning data pipelines to handle changing data requirements in real time. Snowflake works with leading data science and ML/AI partners to deliver faster performance, faster pace of innovation, ease of access to the most recent data, and zero duplication. Connect your ML tool of choice to Snowflake data, with native connectors and robust integrations from a broad ecosystem of partner tools. Run scalable and secure ML inference with models running inside Snowflake as UDFs or communicating with a secure model endpoint with external functions. Effortlessly make model results available in Snowflake for teams and applications to easily consume and act on ML-driven insights.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.