Welcome back to “Building Real World AI applications” – our 4-part MLOPS (Building Successful AI Applications in Production) series.

In the previous part you learned about designing effective pipelines, namely:  data ingestion and versioning, validation and pre-processing, model training, analysis,deployment and monitoring.

In this part 6 MLOPs series, you will learn the art managing the art of continuous iteration effectively, you could also call it Effective End-to-End Testing, if you will.

The model training, tuning and debugging are the core of the machine learning pipeline. In this webinar we will explore the following:

1. Training strategies – You need to train a first model and benchmark it.
2. Evaluate: Then, analyze its performance in depth and identify how it could be improved.For instance with larger models, and especially with large training datasets, this becomes very quickly also impossible to manage. Hardware limitations do allow for infinite computations (unless you’re Google), the efficient distribution of the model training is crucial.
3. Debugging quick and fast – You don’t have all the time in the world to keep building these models.

All in all, you need a robust strategy for a seamless iteration process.

Thanks and see you soon,

Tarry (and deepkapha.ai’s MLOPs team)

In this 4-part mini-series, Tarry will guide you through designing, developing, testing and finally deploying AI applications. Along with the LiveAI team the best practices, tools and techniques are shared, to help you gain confidence in building and deploying AI applications in production.

It’s one thing to have a few AI Models running in production, but it requires a a mature organization to run thousands of such systems in production.

To learn more about core fundamentals of AI, join our popular course AAIE

[1] AI based Industry Vertical Hive Projects: https://liveai.eu/hive-projects/
[2] More insights: https://liveai.eu/insights/
[3] Our instructors: https://liveai.eu/teachers/