MLOps Innovator Series: Bridging the Gap to Observability
In the machine learning space, teams recognize the importance of ensuring their models actually perform. By taking a glimpse into the data pipeline, they can uncover problems that need addressing, and streamline the process itself. AI observability and ML monitoring makes this endeavor possible for machine learning engineers and data scientists.
This webinar features Danny Leybzon, MLOps Architect of WhyLabs, and gives an overview of AI observability, its importance, and how it can transform the field of machine learning.
For machine learning engineers, the hard work begins once their ML model is deployed to production. Creating feedback loops and consistently monitoring the model to ensure model performance doesn’t degrade over time is necessary. Through AI observability and ML monitoring, teams can make informed decisions and deliver actionable insight to stakeholders.
AI observability is the ability to understand models through historical data. It’s a proactive and comprehensive approach to gathering statistical, performance, and metric data to analyze ML models. ML Monitoring is a subset of AI observability that enables data engineers to measure key performance metrics and understand where issues arise.
When ML teams leverage an effective AI observability system, they can automate the insight extraction process and respond to common issues more quickly. These common ML issues include data drift, data quality changes, and data engineering issues. This proactive resolution improves the ML process and accelerates the development lifecycle.
Although many solutions in the DevOps field enable the same extent of AI observability in software, the data utilized in machine learning is unique, making it impossible for ML teams to utilize the same DevOps solutions for ML monitoring. As data is a cardinal element in the machine learning lifecycle, embracing a data-centric approach is crucial in MLOps.
There are preexisting tools that organizations can leverage to automatically retrain and redeploy their ML models based on key findings. However, in the next few years, the industry is expected to shift towards new ways of automating AI observability.
Leverage Pachyderm to Streamline Your ML Lifecycle
AI observability illustrates the need for ML teams to optimize the MLOps process. With Pachyderm, you can leverage end-to-end automation in your data-driven pipelines so your MLOps team can focus on developing models more efficiently and shortening the machine learning life cycle.
See Pachyderm in action by scheduling a demo today.
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