September 30, 2024 • 6 minute read •

5 Best Practices AI Engineers Should Learn From Data Engineering

AI engineering is data engineering. Here are 5 best practices the former should adopt from the latter to succeed.

Name: TéJaun RiChard
Handle: @tejaun

With how big artificial intelligence (AI) has become and how far it has taken off, it’s understandable that AI engineering can feel like a separate discipline from data engineering.

However, at its heart, AI is built on the same fundamental principles that have held true for data engineering for years. AI systems, especially machine learning models, are only as good as the data they consume and the infrastructure supporting that data.

To be scalable, reliable, and performant, AI engineers must adopt data engineering best practices.

In this post, I’ll cover five lessons that data engineers have learned the hard way and go over why they apply to AI engineering too.

Understanding AI Engineering and Data Engineering

Before getting into the best practices, it’s important to know what AI and data engineering are and just what they aim to achieve.

What is AI Engineering?

AI engineering is the development of AI tools and systems so that they can be used and implemented in the real world. Some of these real world implementations look like:

developing algorithms for various applications
developing natural language processing models for chatbots, language translation, and text analysis
predictive maintenance
building data preprocessing techniques
building recommendation systems for personalized content or product suggestions
product optimization

The AI engineers who take on this challenge do so by pulling data from data sources. Some use machine learning models and others use pre-trained models or rule-based systems – the approach depends on the particular app and its requirements. From there, they use API calls or embedded code to build and implement AI applications.

To do their job, an AI engineer usually employs a combination of skills from a variety of disciplines, including: data engineering, software development, data science, and machine-learning.

What is Data Engineering?

Data engineering is designing and building software for collecting, storing, and managing data. This includes things like developing data pipelines, optimizing data storage and collection, and ensuring data integrity and availability.

A data engineer takes data from different sources, transforming it into usable formats, and then storing it somewhere (normally databases or data lakes). The stored datasets are referred to as data assets and are made accessible by data engineers for analysis, reporting, or further processing by other teams.

AI Engineering is Built on Data Engineering

Despite the differences between the two fields, AI is all about data. Data engineers focus on the architecture and infrastructure needed to collect, store, transform, and process data, while AI engineers need and depend on that data to build intelligent, high-quality, and reliable models.

Data quality plays a huge role in how effective models are. Machine learning models are essentially data assets that live on high-quality, clean, and well-structured data to make predictions, insights, and decisions. Without proper data pipelines—things that data engineers have historically handled—even the most advanced AI models would be useless.

Luckily, AI engineers can learn from the trials and tribulations of data engineers so that this doesn’t happen.

AI and Data Engineering: Best Practices

Here are five lessons from data engineering that AI engineering must apply to be successful and scalable:

Ensure That Pipelines Are Idempotent and Repeatable

In data engineering, making pipelines idempotent – meaning that they produce the same result every time the same input is used – is a fundamental practice. This applies equally to AI engineering regardless of whether you’re building models or working with ones that already exist.

AI engineers often need to process large datasets, create embeddings, store them in vector databases, or run evaluations. Idempotency in these pipelines prevents the inconsistencies and errors that make their systems unreliable.

Some steps to achieve this include:

Assigning unique identifiers to each data point for consistent processing
Checkpointing, or saving the state of a pipeline at different stages so you can pick up from the last successful point if things go wrong
Using deterministic functions to make sure your processing functions always produce the same output for the same input
Keeping track of different versions of your datasets and models for consistency’s sake

Keeping idempotency at the forefront of pipeline planning helps AI engineers ensure that their pipelines are repeatable and their systems stay stable as they scale.

Use Scheduling to Automate Pipeline Runs

Data engineering also uses scheduled pipelines to ensure consistent and timely processing.

A lot of AI engineers often invoke pipelines haphazardly or manually, relying on single notebooks run on individual computers – an error-prone approach that doesn’t scale. They should instead build automated data pipelines that handle retries, failures, and partial executions.

Having effective scheduling in their pipelines would make them more consistent and improve the timeliness of their data processing. It also simplifies ongoing maintenance, which makes model training and data processing more reliable overall because there are less chances of human error. The lack of a need for as much human intervention also frees up valuable engineer time, letting them focus on something else like improving their model as opposed to manually managing pipelines.

Make Pipelines Observable

Observability and data visibility are fundamental in data engineering, letting teams monitor pipeline performance and data quality. It ensures that models are producing accurate and reliable results by detecting data drift or performance degradation. With the proper monitoring tools and observability platforms in place, engineers can also fix issues quickly and keep data and models intact.

AI engineers can benefit from the same observability and data visibility when doing things like monitoring computational resources used by their models to manage costs, maintaining a detailed log of AI decision-making processes for compliance and ethical AI best practices, and aiming to reduce downtime and improve reliability in their systems.

If AI engineers want to be able to track model health, monitor outputs, and capture metrics over time, they need to have observable pipelines.

Use Flexible Tools and Languages for Data Ingestion and Processing

Data engineering requires flexibility in handling different data sources and formats, normally achieved by using tools and languages that can ingest and process data from multiple sources – structured databases and real-time streams. Being able to handle different datasets is key to scaling.

AI engineers need this same flexibility because it will give them versatile and future-proof pipelines. Having flexible data ingestion and processing tools means that AI engineers can handle different data sources, scale their systems, adapt to new technologies, integrate with existing infrastructure, and experiment safely and efficiently as they build more and more systems.

Ultimately, following this practice enables AI systems to play nice with other frameworks so they can adapt to changing business needs or data ecosystems.

Test Pipelines Across Environments Before Production

Successful data engineering teams follow the best software engineering delivery practices, and these principles should be applied to AI development too. One particular principle is testing pipelines across different environments, which data engineering teams do to catch environment-specific issues before they hit production.

Testing across environments in AI engineering would ensure that AI models are stable and reliable when finally deployed to production. This would also help engineers find and fix compatibility or performance issues early so deployments are smoother and models perform better in real-world scenarios.

If AI engineers want to ensure that their models work as expected in production environments, they must apply this same discipline in their testing.

How Dagster Enables AI and Data Engineering Best Practices

To follow data engineering best practices you’ll need the right tools.

Dagster is a modern orchestration platform that makes these best practices possible and easy to implement.

This is because Dagster:

Makes pipelines consistent and idempotent through its asset-based APIs that track the state and lineage of data assets to ensure idempotency.
Provides custom event-driven automation and flexible scheduling through Declarative Automation, letting teams automate system operation by setting conditions for when data assets are materialized.
Brings observability and monitoring to teams by design through its data catalog and Insights. The catalog captures output data asset metadata while Insights gives you visibility into operational data from Dagster and downstream systems.**
Plays nice with other environments through its numerous integrations and Dagster Pipes, which lets you integrate existing AI code and external execution environments into Dagster, for easy access to structured databases, real-time streams, and other data sources.
Ensures extensive testing across all environments through Asset Checks, which let you define, execute, and monitor data quality checks directly within your pipelines, support local development, staging, and CI environments, and structure your approach to error handling and version control for reliable and battle-tested pipelines.

With these features, Dagster helps AI teams follow data engineering best practices so they can build high quality, scalable, and reliable AI systems.

Wrapping Up

AI engineering is data engineering. To build AI systems that are reliable, scalable, and adaptable, AI engineers need to apply the lessons from data engineering. Following best practices like idempotent pipelines, observability and data visibility, flexible data processing, and rigorous testing, AI teams can ensure that their models succeed and last.

With Dagster, it’s easier and more effective to follow these best practices. By bridging AI and data engineering, Dagster gives AI engineers the platform to do this at scale confidently.

Ready to apply these data engineering best practices to your AI systems?

Explore how Dagster can help you improve reliability, streamline your pipelines, and scale your AI projects. Visit our platform page to learn more or contact us for a demo, where we’ll show you firsthand how you can build smarter, more efficient systems.

The future of AI is up for grabs, but one thing is certain: it will definitely rely on leveraging these shared foundations so that AI systems can become more efficient, capable, and powerful.

We're always happy to hear your feedback, so please reach out to us! If you have any questions, ask them in the Dagster community Slack (join here!) or start a Github discussion. If you run into any bugs, let us know with a Github issue. And if you're interested in working with us, check out our open roles!

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