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Building ML Pipelines: Automating Parameter Search 🔍


Avinash Gopal
Metabob, CTO

Avi has an illustrious background in Aerospace Engineering, with expertise in every breadth of development, specifically versed within AI and machine learning. He’s worked on multiple Aerodynamics Research Projects, specializing in novel airfoil design and autonomously controlled crafts for personal transport and area mapping. He also served as the CTO of Clyste.


Workshop Overview:

The workshop will begin by discussing Model Tuning - a process by which developers fine tune their search with overlifting the variance. Model Tuning acts as the “dials” and “knobs” of the Machine Learning process, and is the critical first step to achieve proper Automated Training of a given machine. From here, the workshop will show attendees how to then take their newly tuned and ready to function automated training system, and implement it into the proper ML Pipeline. There are various pipelines given the needs of one’s given task, and we the workshop will go about explaining what types are preferred for certain situations over others.

Intended Audience**:

  • New and experienced data scientists and engineers.
  • Interested in learning how to implement automated training and ML pipelines
  • Note** Workshop is intended for individuals with an intermediate level proficiency with ML

Topics Covered:

  • Model Tuning / Hyperparamter Optimization 
  • Automated Training
  • Building ML pipelines

Workshop Takeaways:

  • Determining when to start model optimization
  • Which search methodology is effective for your domain
  • Configuring stop conditions and validation checks
  • Automating parameter searching in a scalable way

To-Do Before Workshop / How to Prepare:

No advanced preparation necessary