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Solving the Data Engineering Bottleneck in Production ML
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David Wang
VP of Marketing
Tecton
If your ML projects are moving slowly, it's likely because the complexity of data engineering is standing in the way. Many ML teams are bogged down by managing the data pipelines needed to develop and deploy new features, which are essential to model performance.
In this 20-minute talk, David Wang, VP of Marketing at Tecton presents the most significant challenge in production machine learning: the data engineering bottleneck. Discover the importance of a platform for developing, managing, and serving features in production ML environments.
David delves into real-world scenarios, such as building a real-time recommender system, the hurdles teams face with data scattered across various platforms, and the complexities of orchestrating numerous processing systems.
What you’ll learn:
Watch the video on-demand now!
In this 20-minute talk, David Wang, VP of Marketing at Tecton presents the most significant challenge in production machine learning: the data engineering bottleneck. Discover the importance of a platform for developing, managing, and serving features in production ML environments.
David delves into real-world scenarios, such as building a real-time recommender system, the hurdles teams face with data scattered across various platforms, and the complexities of orchestrating numerous processing systems.
What you’ll learn:
- The intricacies of the data engineering bottleneck in ML and why it matters.
- Strategies ML teams use to tackle this challenge.
- An introduction to Tecton and how it facilitates a faster and easier development workflow.
Watch the video on-demand now!