Tools and techniques for optimizing AI data pipelines

The importance of AI data pipelines

In simplest terms, an AI data pipeline is the complete process used to convert raw new data into a refined data set that's used with an ML model or a bigger AI system. Pipelines are vital for training, testing, evaluating and real-world deployment. A typical AI data pipeline involves five distinct steps:

AI data pipelines are typically highly automated and well orchestrated to enhance data preparation consistency and completeness. A mature AI data pipeline is a critical part of successful AI project development and system performance. The pipeline also supports scalability as data volumes change and facilitates ongoing AI adaptation and learning.

AI data pipeline tools

Manual processes and deliberate human intervention don't work well for enterprise data pipelines -- there's just too much data. AI systems raise the magnitude of demands on traditional data pipelines. Therefore, AI data pipelines need highly automated tools for ingestion, preparation, feature engineering, training and testing. They also require the deployment and monitoring of data intended for ML models and AI platforms.

Popular tools include Amazon SageMaker, Databricks and Google Cloud AutoML, though others are available depending on an organization's data complexity, needs, overall toolchain integration and budget. The following sections list in alphabetical order the tools available for each part of the AI data pipeline.

Ingestion tools

Ingestion tools in the AI data pipeline collect data and, when appropriate, centralize and organize it in a suitable storage environment for further processing. Ingestion tools are highly automated, can operate in real-time or batch mode, and support the collection of a variety of structured and unstructured data, including text, PDFs, images, video and audio.

Since data quality is a central concern for AI, data pipelines are increasingly incorporating anomaly detection and other data quality features into ingestion tools to ensure high-quality data from the outset. The following are some examples of ingestion tools for an AI data pipeline:

• Airbyte.
• Amazon Kinesis.
• Apache Kafka.
• Apache NiFi.
• AWS Glue.
• Databricks.
• Domo.
• Fivetran.
• Google Cloud Dataflow.
• Matillion.
• Microsoft Azure Data Factory.
• Qlik Talend Cloud.
• Stitch Data.

Preparation tools

AI data pipelines rely on preparation tools to clean, normalize and structure data into a usable, properly organized form. Preparation typically supports data quality, removing noise from the data set. It also identifies and responds to missing data, filling gaps and ensuring a uniform data format that facilitates further processing and use by ML models.

Preparation tools are highly visual, automated and scalable. They simplify complex human interactions, support massive data sets and increasingly rely on AI-based features to facilitate data engineering tasks. In addition, modern AI data preparation tools support data provenance and governance, ensuring a record of data manipulation for regulatory compliance needs.

Examples of data prep tools for an AI data pipeline include the following:

• Alteryx Designer Cloud.
• AWS Glue DataBrew.
• Databricks.
• Dataiku.
• Dbt Labs.
• Domo
• Informatica Intelligent Data Management Cloud.
• Microsoft Power Query (part of Excel and Power BI).
• Nimble.
• Qlik Talend Cloud.
• Salesforce Tableau Prep.

Feature engineering tools

Data prepared for an AI system can still be weak. The many individual data elements used as input data might fail to adequately let an AI system identify patterns, make classifications or form predictions. Feature engineering creates new data from the existing data to improve ML models performance on new or unseen data. In effect, feature engineering is a means of enriching data to improve ML and AI outcomes.

Feature engineering is complex and data-dependent, so some data sets might not need it, while others might require different amounts and types of features. For example, image data could benefit from enhancements to emphasize the presence of lines, shapes and edges.

Feature engineering is a powerful and vital step in an AI data pipeline, and feature engineering tools demand high levels of automation and scalability. Examples of these tools and libraries include the following:

• Alteryx Featuretools.
• Amazon SageMaker.
• Autofeat.
• Databricks.
• DataRobot.
• Feature-engine.
• Google Cloud Vertex AI.
• H2O.ai H2O Driverless AI.
• NumPy.
• Pandas.
• Scikit-learn.
• Tsfresh.

Training and testing tools

Once properly prepared, data is typically segregated into training and testing sets. The training data set is fed into models so the AI system can effectively learn, enabling it to recognize new or unique situations. Testing is then needed to validate the trained models and ensure that the AI system is accurate, production-ready and meets security and compliance requirements.

The tools involved at this phase of the AI data pipeline are vital to automating and orchestrating this learning and evaluation process. Proper execution is closely monitored to ensure proper data flows and quality standards throughout the AI data pipeline. Testing tools also play a role in production, continuously validating the performance of models and AI systems to ensure reliable, accurate and fair outcomes.

Testing tools can also identify data quality problems, such as data drift and format inconsistencies, that affect AI outcomes. The following are some examples of popular training and testing tools used in an AI data pipeline:

• Amazon SageMaker.
• Apache Airflow.
• Applitools.
• Databricks.
• DataRobot.
• Dbt Labs.
• Google Cloud Vertex AI.
• H2O.ai H2O Driverless AI.
• Kubeflow.
• Mabl.
• Microsoft Azure Machine Learning.
• MLflow.
• Seldon.
• Tricentis Testim.

Deployment and monitoring tools

After an AI system is deployed to production, it can use real-world data to provide business value. Deployment tools consistently provision resources and connect services needed to host the AI system. Deployment can use high levels of automation and orchestration and apply versatile rule sets to scale resources as production demands change over time.

Deployed AI systems also require careful monitoring to ensure that outcomes meet the enterprise's availability, performance, accuracy and fairness requirements. Deviations such as performance degradation and output inaccuracies can trigger early warnings for remediation, such as retraining, which might also be automated. Recognized tools for deployment and monitoring in the AI data pipeline include the following:

• Amazon SageMaker.
• Apache Airflow.
• Arize AI.
• Cisco Splunk.
• CoreWeave Weights & Biases.
• Dagster.
• Databricks.
• Datadog.
• DataRobot.
• Google Cloud Vertex AI.
• Grafana Labs Grafana.
• Kubeflow.
• Microsoft Azure Machine Learning.
• Prefect.

Best practices for AI data pipelines

AI data pipelines are typically built around cloud-native or cloud-first architectures. This offers some advantages in scalable storage and cost-effective compute resources, but there are persistent challenges in data quality, scalability, observability and security. Best practices help to ensure data pipelines remain reliable and offer the high performance that AI development demands:

• Think of data as a product rather than a process. Data pipelines are about moving data, but it's the result of that movement that matters to the business. Instead of focusing on data movement, remember that the goal is to create useful and trustworthy AI data that delivers business value. Design an AI data pipeline that provides well-validated, high-quality, version-controlled data that's packaged and ready for use.
• Maintain data quality throughout the entire pipeline. Bad AI data leads to poor AI outcomes. Ensure data quality early in the pipeline through clear data provenance, extensive data observability, automated data validation, standardized data format enforcement and data anomaly detection. Validating data quality early and maintaining it throughout the pipeline ensures more reliable AI outcomes. Implement detailed logging, tracing and data quality tracking to quickly identify data issues and support compliance with AI explainability and transparency requirements.
• Pay close attention to data scalability. AI is about performance because users expect fast, accurate results, often in real time. Design an adaptable AI data pipeline that can support real-time data ingestion and transformation to accommodate changing data structures. Look for technologies and architectural approaches that deliver high-throughput, low-latency pipelines and scale as needed in response to changing data and user needs.
• Use automation and orchestration. Avoid manual processes in AI data pipelines. Once a sound workflow is designed and validated, use automation and orchestration to eliminate manual effort, synchronize processes, ensure consistency and drive pipeline efficiency. Data checks and test responses can use event triggers and hooks to detect and respond to errors in data quality and processing, reducing delays and enhancing data and pipeline reliability.
• Invest in data security and governance. Data often includes sensitive or personal information, and data pipelines must withstand increasing regulatory scrutiny. This demands careful attention to ensure data security and compliance with prevailing regulations. Common security tactics include end-to-end encryption and strong authentication. The importance of data governance also can't be overlooked. Data provenance and transparency are vital, as well as ensuring repeatable pipeline runs, also known as data idempotency.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.