Free Machine Learning Workshop - Build Higher Quality Models with Higher Quality Data

Debugging machine learning data can be a grueling task, but with Galileo, you can debug your data 10x faster. Sign up for our workshop to how to debug machine learning data using Galileo:

Reserve Your Seat

In This Workshop

Introduction:

• Explanation of the common challenges in working with machine learning data & resolving data errors
• Overview of the workshop objective

Data Collection:

• Overview of the process of collecting data for machine learning
• Discussion of the types of data sources available for machine learning including

Data Preprocessing in Pre-training:

• Explanation of the importance of preprocessing data for machine learning
• Discussion of the challenges in preprocessing data and how to overcome them in Galileo

Data Debugging:

• Explanation of the common challenges in debugging machine learning models
• Hands-on demonstration of how to debug machine learning models using popular libraries in Galileo

Model Evaluation:

• Overview of the different evaluation metrics used in machine learning including DEP Score
• Hands-on demonstration of how to evaluate machine learning models using popular libraries

Conclusion:

• Q&A session with the participants.

By the end of this workshop, participants will have a comprehensive understanding of the process of debugging machine learning data and the tools and techniques they can use to tackle the common challenges they may encounter.

Data Modalities Featured

Natural Language Processing

Computer Vision (Images)

Structured Data

What is Galileo?

We are a Machine Learning Data Quality Intelligence Tool.

Using Galileo you can inspect and fix data quality errors in all stages of the ML process. Our team at Galileo had first-hand experience working on large-scale Machine learning projects at Uber Michaelangelo and Google Ai. It’s no wonder they turned their experience into an obsession to fix the data quality issues.

Where does Galileo fit in the ML Workflow?

• Pretraining- Find and fix data errors before training without a model.
• Training- Connect Galileo in your training model to resolve data errors.
• Production - monitor and resolve data drift errors.

At our core, we believe that together we can create a better, more productive, bias-free future for the world by focusing on high-quality data.

Use Galileo to save time and focus on far more enjoyable and challenging tasks- get started in our free community offering.

Join our slack to share your thoughts on this data quality hurdle.