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3 Data Orchestration Roadblocks that Impact ML Success

Ad-hoc machine learning projects can generate technical debt and bugs that plague your projects and waste production cycles for years. Over time, engineering teams learn to work around idiosyncrasies in data delivery and processing. While bugs are an unavoidable reality of complex engineering projects, a high-performing system requires well-planned architecture. The alternative will take you down a road of conflict, failure, and drift

Like any production-level system, the ability to efficiently diagnose an issue causing model outage or underperformance is essential for building internal trust and long-term value with colleagues relying on machine learning. 

Identifying the source of an issue isn’t easy since you have to go through the entire ML lifecycle to diagnose accurately – and cannot always happen quickly. 

The right approach to MLOps is the key: Effective data orchestration to cultivate high quality data, managing the risk of poor model fit, and automating robust systems to avoid disconnected pipelines reduces the risk of your team’s projects succumbing to these common pitfalls that unnecessarily bloat project timelines.

Here’s a closer look at three data-related problems that signal it might be time to level up your MLOps.  

1. Poor Data Hygiene

Data hygiene refers to the collective processes involved in ensuring data stays high-quality and useful in business intelligence, analysis and machine learning operations. Clean data is error-free, controlled for duplication with a ‘single source of truth’, and processes are in place to reduce the impact of incomplete, outdated, and incorrectly formatted data. 

No matter how much work you put into your models, training them with dirty data won’t lead to accurate outcomes. Expect off-the-mark predictions if your data is improperly labeled, full of unnecessary features, irrelevant, or inappropriate for the model. Proper code is a key element of the code-data loop

Controlling data quality requires internal data management policy and automated controls. Bringing version control to data science, along with human in the loop practices, is part of building compliant and reproducible outcomes. 

2. Poor Model Fit

The training data used for the machine learning model will significantly affect its outcomes’ accuracy. Over- and under-fitting the data is an issue developers face when available data is inappropriate for the model developed.  

Under-fitting happens when used datasets are too simple to identify the underlying or precise relationship between the variables. On the other hand, over-fitting occurs when massive amounts of data are used to train a model, resulting in poor performance.

There are a variety of ways your data and model can be a mismatch, like trying to fit a new use case to a model built for a different purpose. Other times, the dataset is poorly matched to its purpose, or the dataset is too specific. When data is too specific or too vague, you will produce skewed outcomes. Iteratively tweaking and modifying training data is a common way to improve the accuracy of your model and address over- and under-fitting issues.

There are ways to manage this within a Data-Centric AI approach. Synthetic and augmented data, feeding simulated data scenarios to your model to train for a known use case, when a dataset is limited. When relying on these techniques, the provenance of your data will be important to track, in order to improve or weed out issues with synthetic and scenario-focused datasets. 

3. Poor Deployment Visibility

Once the data has been tested and processed, your model is ready for production. Over time, however, it is natural that your team will make changes: live data ingestion, model adjustments, output classifications and more can affect the reliability of your model’s output over time. 

Machine learning data and model control and monitoring across an organization remains an ever-present challenge to balance security and usability. Each team will have its own policies and procedures for handling data, creating difficult conditions when data and features start to drift.

For enterprise machine learning operations, this means monitoring tools for machine learning data, models, and outputs. Tools like Pachyderm’s ML Tech Stack map ML technologies to the critical functions they provide to data engineering teams, allowing teams to ensure the right combination of technologies to monitor every aspect of MLOps.

Addressing Machine Learning Challenges

Bugs are inevitable at any stage of machine learning development, so it’s important to have the right tools to address them.  they’re more likely to crop up with self-built ETL infrastructure when retrieving and manipulating data. With the right tools, you can diagnose and solve issues faster and more efficiently.

Pachyderm reduces time to diagnosis and solution for machine learning programs. As a result, we minimize pager incidents and shorten downtimes for organizations relying on ML to transform their data. It’s all possible, thanks to:  

  • Flexible pipelines with data-driven automation
  • Rapid processing for experiments and testing
  • Difference-based data versioning

With Pachyderm’s best-in-class features, cross-functional teams can make strategic decisions and launch ML projects more quickly, creating more value in the long run.

Achieve Results Faster

Hygiene doesn’t have to be difficult if you have the right tooling. If you set yourself up for iteration (on your data, techniques, collaboration) and track things that are changing, you are going to achieve better results in the long run.

Reduce the time and achieve better results for your AI/ML projects by downloading our white paper, Three Core Principles to Accelerate Machine Learning Success.