What Does Data-Centric AI Mean for On-the-Ground MLOps?
The focus on models has driven innovative and stable machine learning tools for research and enterprise. The rise of data-centric AI shifts the focus from models and code to the quality and context of your datasets.
In this ebook, you will find:
- Computing approaches for improving datasets
- Management and communication strategies for cultivating higher-quality data
- Why machine learning operations can no longer afford to ignore data versioning and lineage
Get the eBook
Are You Doing Data Science or Model Science?
As enterprise applications of machine learning have matured, so have the needs of the scientists and engineers building these tools: the ingestion, transformation and interpretation of data has evolved to suit specific audiences, data structures, and use cases.
Selecting a model is a solved problem for many scenarios. However, things are not so simple on the data side. Some data problems still need to be fixed: for example, science and analyst teams can’t access the data they need for an application, pipeline tools can’t handle streaming data, and datasets can be either over-or under-conforming.
What is Data-Centric AI in Practice?
Andrew Ng coined the phrase ‘Data-Centric AI’ to describe a future for MLOps where these issues don’t slow down machine learning projects and bloat engineering budgets. It sounds inspiring, but what does data-centric AI mean when a data engineer sits down at their desk?
Improve the performance of your Machine Learning with Superior Data Quality
In Practical Data-Centric AI for the Real World, Pachyderm’s Dan Jeffries presents common project delivery challenges when building machine learning applications for internal use cases like quality control and production line monitoring.
This ebook covers cases where a model’s output is insufficient for its purpose and helps you reorient your solutions to be data-centric instead of seeking model-based solutions.
- How to troubleshoot and remedy data labeling concerns
- Managing the storage of synthetic and augmented data
- Practical use cases for augmenting datasets
What does a data-centric approach look like?
Synthetic and Augmented Data in AI Systems
Data doesn’t appear out of thin air, especially usable data for enterprise machine learning. However, when working with rich media like image detection and natural language processing, generating more data may not be feasible from an operational or budgetary perspective. So what’s a data engineer to do?
This ebook describes methods to enhance, augment, or increase the amount of high-quality data to feed your model. It also covers best practices for managing complex data labeling and describes how Pachyderm’s data versioning and lineage ensure you don’t lose a high-performing dataset to an accidental overwrite.
Versioning and Lineage for ML Engineering
Understanding what your data has produced and how it got there makes your machine learning operations more efficient. In addition, it builds deeper trust across your organization in machine learning capabilities for your use cases.
Why focus on versioning and lineage for data and models? Because versioning models is only half of the equation. What dataset was used, when, and why is critical to understand where things have broken down, allowing you to test the same dataset on different versions. This report from Winder AI is an excellent overview of the importance of provenance and lineage in machine learning.
Don’t miss out:
Download Practical Data-Centric AI in the Real World
Optimal tooling for getting data-centric models in production
Managing data quality for collaborative AI & ML projects can be almost impossible with the wrong tools, starting with storage.
“Most companies have several competing systems that range from proof-of-concept to production infrastructure. Of course, flowing between them and through them is the data itself.
The data is taken for granted. It’s seen as moving from one tool to the next instead of a part of the whole stack, and that’s the core of the issue. Everything in ML is downstream of data.
It’s super common to find multiple copies of the same dataset from different points in time strewn around a company’s infrastructure, the same dataset fragmented into many smaller subsets. I’ve even talked to data teams who refer to themselves as “locksmiths” because they need to bypass their company’s internal security to get usable data.”
Joe Doliner, The Fragmentation of Machine Learning
Next, you need to build data-centric infrastructure: Protecting your time, workload, and data with automated versioning from point of ingestion, through transformation and processing, and finally with your experiment output.
For every step of the machine learning lifecycle, Pachyderm acts as an automated data versioning layer, making it simple to retrieve the best-producing datasets once you’ve run a series of training tests.
Take a Look at Pachyderm
With user-friendly integrations like Snowflake, JupyterLabs mount extension, Label Studio, and more, Pachyderm fully integrates with your MLOps stack to help teams build for reproducibility. The AI Infrastructure Alliance was founded to bring together data-focused product teams and encourage flexible, powerful integrations to build a robust machine learning technology stack.
See Pachyderm’s Data-Driven Pipelines In Action
Book a custom demo of Pachyderm’s versioning, lineage and data-centric pipeline management for enterprise machine learning teams.