The Role of a Machine Learning Pipeline in the ML Lifecycle

The machine learning (ML) pipeline is an integrated, end-to-end workflow for developing machine learning models. Because ML is integral to many modern applications, organizations must have a reliable, cost-effective process for feeding operational data into machine learning models. In this article, we explore the role of MLOps in the creation of machine learning pipelines and highlight three common barriers organizations face when using their data in machine learning applications. We also look at how 

What Is a Machine Learning Pipeline? 

The machine learning pipeline is used in machine learning operations (MLOps) to deploy and maintain machine learning models. MLOps organizes the machine learning process into an efficient, multi-step workflow, orchestrating the movement of ML data into models via data pipelines and the resulting model outputs. This includes raw data, features, outputs, the machine learning model and parameters as well as the prediction outputs. 

Creating, deploying, and maintaining machine learning models is a complex process and involves multiple specialties, including IT, DevOps engineers, and data scientists. The machine learning pipeline divides the entire process into its component parts, enabling each piece to be developed, optimized, configured, and automated separately, before being brought together during the final stages of production. 

How MLOps Streamlines the Development and Deployment of Machine Learning Pipelines

MLOps synchronizes the development of ML models, making it possible to extract actionable insights from ML data more quickly and efficiently. Here’s how. 

ML lifecycle speed

The machine learning pipeline plays an important role in accelerating the development and deployment of ML models through its use of automation. For example, pipelines can be used to optimize compute resources by running intensive data processing tasks on high-memory CPU machines while reserving the computation-heavy training for the more expensive GPU machines. In addition, machine learning pipelines help data scientists improve the efficiency of model training, automatically detecting which steps from prior training can be reused for the current one.

Cross-team collaboration

MLOps provides the development framework that facilitates collaboration across teams. Without it, work tends to happen in silos, creating unnecessary delays and costly rework when tasks aren’t synchronized. By breaking down the entire process into its component parts, teams can build repeatable workflows that help them identify key dependencies with other specialties, highlighting the need for collaboration at key points in the development process. 

Seamless scalability 

With its focus on repeatability, MLOps enables organizations to develop and maintain additional machine learning pipelines without adding more members to their data teams. For organizations already using a cloud data platform, scaling data storage and compute resources on-demand facilitates the growth of ML initiatives to meet current business needs. 

Regulatory compliance

As government and industry data regulations become more stringent, specific legislation such as the European Union’s General Data Protection Regulation (GDPR) has ramifications for the use of machine learning technologies. The MLOps process includes built-in safeguards for ensuring compliance with relevant standards for how data is used and stored.

Preventing model bias

Machine learning algorithms are only as representative as the data they contain, and they are subject to bias. The MLOps process contains built-in safeguards that prevent certain factors within a data report from outweighing others. 

New business applications 

Machine learning processes such as features and models can be retooled and deployed in new business use cases. Creating additional machine learning pipelines from repurposed components helps organizations achieve key business objectives more quickly than building them from scratch.

Barriers to Building Efficient Machine Learning Pipelines

The MLOps development framework requires modern, cloud-native technologies to operate effectively. Legacy infrastructure hampers the deployment of machine learning technologies, placing its many benefits out of reach. Removing these three common roadblocks paves the way for improved ML model training efficiency and cost reduction.

Insufficient compute power

Machine learning algorithms consume a massive amount of compute resources. For businesses still using on-premise systems, ML initiatives can interfere with the computing needs of other business areas, creating disruptive and costly resource bottlenecks. A cloud data platform eliminates resource contention, providing dedicated, rapidly scalable compute power for machine learning processes. 

Fragmented data storage

Siloed data prevents well-designed models from providing quality outputs. Without a cloud data platform, bringing together data stored across multiple systems together in one place is all but impossible. The cloud data platform serves as a single source of truth, uniting structured and unstructured data to provide a centralized repository for storing all of your data. 

Inability to adjust to changing requirements

Legacy, on-premise infrastructure is inflexible, especially when it comes to supporting the fast-evolving requirements of machine learning. In contrast, a cloud data platform designed to support data science workflows such as MLOps provides the flexibility to adjust as needed to meet future business objectives. 

Optimize Your ML Pipelines with Snowflake

Snowflake helps businesses maximize the value of their ML data, making it easy to construct and operate powerful machine learning pipelines. The Snowflake Data Cloud provides the fully elastic compute and storage resources required to meet changing data requirements in real time. Snowflake works with leading data science and ML/AI partners to deliver faster performance and accelerate the pace of innovation. Connect your ML tool of choice to Snowflake data, with native connectors and robust integrations from a broad ecosystem of partner tools. Effortlessly make model results available in Snowflake for teams and applications to easily consume and act on ML-driven insights.