From Data to Deployment: AI and ML Pipelines

Developing high-performing, secure artificial intelligence (AI) applications is a complex, multistage process. The AI pipeline provides the workflow structure that development and operations teams need to work in a systematic, controlled and repeatable manner. From data preprocessing to model evaluation, pipelines make it possible to move models from prototypes to production systems with quality and efficiency. In this article, we’ll explain the role of machine learning operations (MLOps) in automating development and deployment, and explore the primary AI pipeline stages. We’ll also discuss the benefits of using an AI pipeline for orchestrating your ML workflows.

MLOps in AI pipelines 

MLOps brings DevOps principles to the machine learning project life cycle. MLOps is a set of standardized practices that facilitates collaboration and communication between data scientists, DevOps engineers and IT.  It is essential to building efficient, scalable and secure AI pipelines. MLOps practices focus on the specific steps involved in developing and deploying models, and include operational and management processes, tools used for version control, automated testing, CI/CD, training and monitoring activities.

AI and ML pipeline stages

Developing production-ready AI and ML systems involves a structured workflow that governs how models are developed, deployed, monitored and maintained. Pipelines provide this structure, offering a repeatable, scalable development process comprising a series of interconnected stages.

Data collection

Gathering the raw data required to train the model’s algorithms is the first stage in the pipelines. During this stage, data is sourced from numerous sources, including relational and NoSQL databases, data warehouses, APIs, file systems, hybrid cloud systems and third-party providers. The project’s business use case drives the sources and types of data collected.

Data cleaning and preprocessing

Before raw data is suitable for model training, it must first be cleaned and processed. This stage of the ML pipeline involves analyzing, filtering, transforming and encoding data so it can be readily understood by the algorithm. 

Model training

During model training, data scientists select the model and parameters best suited for their use case, fitting the right weights and biases to the algorithm they’ve chosen. The primary goal of model training is to minimize the loss function, which accounts for the difference between the model’s predicted outputs and the actual target values. This performance measure guides the team as they work to optimize and refine their model. The end result of model training is a working model ready to be tested and deployed into production.

Testing and deployment

Before a model can be made available for end users, it must be tested to ensure its predictions are accurate. To accomplish this, the model is provided with a test set, a separate subset of the data that was withheld during the training phase. The test set mirrors the kinds of real-world data the model is likely to encounter once it has been released into production. By evaluating the model's performance on these previously unseen examples, data scientists can measure how well the model is able to generalize and make accurate predictions on new data. These results are used to identify and correct potential issues with design, model selection and programming. After needed adjustments are made, it is deployed for use. 

Model monitoring and updating

After deployment, models must be continuously monitored and maintained. Issues such as model degradation, data drift and concept drift may require periodic intervention to maintain an acceptable level of performance.

Key Capabilities of an AI pipeline

Data-driven organizations are rapidly scaling their use of artificial intelligence, applying it to a growing number of business use cases. As adoption accelerates, the capabilities of the pipeline become crucial. 

Efficiency and productivity

Artificial intelligence pipelines provide an organized approach for model development, orchestrating the activities of various teams into a single, streamlined workflow. Pipelines emphasize automation, removing the need to manually complete tasks such as data preprocessing, feature engineering and more. By design, machine learning pipelines are compartmentalized, broken down into small, individual components. This architecture encourages experimentation, allowing teams to quickly test and improve the pipeline design. 

Reproducibility

With their highly automated, standardized processes, AI pipelines ensure consistency across projects. Many of the pipeline components are reusable and can be easily repurposed for use in future projects.

Scalability and enhanced performance

Artificial intelligence pipelines can scale rapidly to accommodate large datasets. Using distributive processing, pipelines automatically spread the computing workload across the number of machines required for model training and inference. Pipelines incorporate a number of features that improve performance and efficiency, such as executing multiple pipeline stages in tandem and automatically optimizing the allocation of compute and storage resources. 

Model deployment, monitoring and maintenance 

Pipelines play an important role in integrating production-ready models into the APIs, apps or web services they’ll be a part of. With support for model versioning, developers can update or roll back models as needed.  ML pipelines include defined processes for monitoring post-production performance, allowing developers to proactively detect permanence issues that will require retraining or updates to correct. 

Iterative development and model evaluation

AI and ML pipelines foster a culture of iterative development, allowing teams to experiment with multiple models and parameters as they search for the model or algorithm best suited to the problem requirements and characteristics of the data. Pipelines also include methods for evaluating model performance, helping teams evaluate the efficacy of their models during both the initial stages of development and after deployment.

Model governance and security

AI governance practices ensure that models are developed and deployed in an ethical, transparent manner that aligns with data security best practices, organizational policies and relevant regulations. AI pipelines contain built-in checkpoints and validations that ensure models meet these standards.

Engineer high-performing AI and ML pipelines with Snowflake

Snowflake's architecture supports scalable pipelines, enables data preparation for ML model building, and integrates with various development interfaces, enabling you to build from familiar tools while efficiently and securely processing the data in Snowflake. Performance speed is critical for supporting machine learning models. Snowflake can scale up or down, and can bear ML data-preparation responsibilities, reducing data-related burdens from machine learning tools.

By offering a single consistent repository for data, Snowflake removes the need to retool the underlying data every time you switch tools, languages or libraries. Also, these activities' output is easily fed into Snowflake and made accessible by non-technical users to generate business value. 

With Snowpark ML, you can quickly build features, train models and deploy them into production—all using familiar Python syntax and without having to move or copy data outside its governance boundary. Snowpark Model Registry (in private preview) makes it easy to manage and govern your models at scale.