With ML & AI models delivering novel, media-friendly and headline grabbing results like DALL-E 2, It’s not surprising that the machine learning market is full of model building, versioning, and observability tools claiming various capabilities for turning raw data into interesting, business-relevant outcomes.
However, in recent years, the focus has increasingly moved to data, especially how to manage it. Interest in versioning and data lineage in machine learning has remained a late-stage concern for ML teams, due to perception it might slow down MLOps while increasing storage expenses and technical debt. The truth is, this is one of many data-centric roadblocks to building faster, more efficient ML teams.
1. Uncertain and Unstructured Data
It is difficult to sift through large volumes of data to find critical information for strategic decisions. Furthermore, obtaining and using data containing mistakes and abnormalities can have costly consequences, especially when high stakes are high.
Take the case of Epona Science, a team trying to create the fastest thoroughbred breeding lines with data. They have copious amounts of collected data, such as race performances, genetic history, medical profiles, weather data, and images, but what should they look at exactly to make the best decision? Since the return on their multi-million dollar investments largely depends on an animal’s performance on the track, they must know which horses are worth buying.
Noisy and unstructured data like Epona’s is notoriously difficult to label and process—a major hurdle for your ML teams. Make your data pipelines efficient with the right tools for cleaning, filtering, and labeling data. Data lineage in machine learning is crucial in generating a solid dataset for accurate outcomes and sound decisions.
2. Data Control and Privacy
As applications become more data-driven, governments and consumer groups have raised valid data control and privacy concerns. For instance, when is it appropriate to use personal data for training algorithms and producing predictive outcomes? Or, how do organizations safeguard sensitive data and prevent abuse of easily available critical information?
With these policy factors in play, data lineage in machine learning becomes essential for legal and regulatory compliance. With the proper tools in their ML stack, teams can quickly identify data origin and provenance—how it changed, where it moved, etc. Audit, even at the most granular level, won’t be a headache and hindrance to your workflows.
3. Limited Datasets Driving Poorly-Founded Conclusions
It’s a cardinal rule in modeling to work with as much data as possible when training algorithms. However, ML/AI teams don’t always have the resources to take heed. And without enough relevant data, outcomes won’t be as accurate as expected.
Data scientists have developed ways around this problem by generating synthetic data to augment the shortfall. And with data lineage in machine learning, your team can quickly identify where the data came from and whether it is a trustworthy source.
Other data-related challenges include noisy data, datasets with missing values, and biased datasets. To manage and overcome the limitations of dataset issues like these, you need an ingestion tool that can handle complex blending and transformation of structured and unstructured data. This allows your teams to have good quality datasets, be flexible, and create ML models with better predictions.
4. Pipelines That See Data, Not the Datum
Most pipeline tools in the machine learning space work as a sequence of jobs on the data. And when the required data changes, it’s harder to run workloads at scale. These tools don’t consider the data at its granular level.
When pipeline and versioning tools that break down your jobs to the datum level, distributed processing and scale can be much more of a challenge. Think of datums as a tool for splitting input data and distributing processing workloads. It simplifies your ML workflows.
A tool like Pachyderm considers ingested data as a series of datums or small blocks of files. Instead of the usual data-centric MLOps, such a datum-centric approach streamlines and speeds up dataset processing, especially with lineage and versioning already included in pipeline management.
5. Watching Cloud Compute Costs Rise
Incremental changes in your dataset typically lead to your team running the entire MLOps stack system to generate newer, more accurate forecasts.
When working with live and continuously updated datasets, teams frequently re-run full datasets through their model when new updates arrive. This works for smaller datasets, typically structured data. However, repeating the process for rich, unstructured and other large-scale datasets across numerous experiments will quickly push up cloud computing costs.
Unpredictable cost increases can limit your team’s experiment velocity and data processing capacity, especially when there’s an enterprise-wide mandate to reduce expenses. As a result, it diminishes the value of your machine learning program.
But with Pachyderm’s datum-centric pipelines, you can process new data incrementally and in parallel. Eliminate unnecessary costs, extra time, and additional effort.
Don’t Let Data Challenges Slow Down Your MLOps
As data moves to the forefront of MLOps, many hurdles to managing it might hurt your organization’s machine learning initiatives, particularly those still in the pipeline. However, if you have the necessary tools in your technology stack, you’ll be well on your way to success.
See how you can streamline your ML lifecycle with a flexible, fully-integrated MLOps stack.