BigML’s education initiatives are an integral part of what makes the platform useful and popular as they continually generate a new class of autonomous power users of Machine Learning that can creatively explore predictive problems to serve their customers.
For almost two years, we’ve been offering our BigML Certified Engineer program, which has produced an impressive 23 waves of graduates to date. At this point, the interest from the existing pool of Certified Engineers has made us decide to launch our brand new BigML Architect Certification program that requires the successful completion of the Certified Engineer program. Effective immediately, you can sign up for the first wave that starts on the week of October 15, 2019!
This new Architect course is aimed at advanced BigML users and BigML Certified Engineers who want to learn how to design, architect, and implement end-to-end Machine Learning applications. The students will learn how to make the best decisions for their smart application depending on the volume of data, the Machine Learning tasks to be automated, and the specific requirements of the problem being solved.
The Architect certification process consists of 8 online classes of 1.5 hours each. The evaluation will be based on solving a set of theoretical questions and exercises presented during the course. The sessions detailed below will be delivered in pairs as online classes.
- Machine Learning Engineering
- Real-world Machine Learning
- Building end-to-end Machine Learning applications
- How to size and address your project
- BigML Predictions
- How to generate thousands of predictions per second
- How to store predictions for further analyses
- How to implement robust predictions.
- Model Risk Management
- Local models vs. remote models
- How to use and operate models
- How to monitor your models
- Machine Learning Models: How to Automatically Create Models
- Automated model and parameter selection
- When good is “good enough”
- What your actual test set tells you about your model
- Model Retraining: When and How to Retrain Models
- Tracking models over time. You can learn from everything.
- Automating covariate shift detection
- Active Learning
- Building Datasets for Machine Learning
- Diversity vs. volume
- Detecting biases
- Detecting blind spots
- Automatically Preparing Your Data for Machine Learning
- Choice of data engineering tools
- Automating feature selection
- Automating feature generation
- Putting It All Together
- Anatomy of a robust Machine Learning application
- Lessons learned and best practices
- Design patterns: beyond lessons learned and best practices
This program is also a great opportunity for BigML delivery partners to demonstrate their mastery of the rapidly growing BigML Machine Learning-as-a-Service platform while further differentiating themselves from competitive analytical services organizations.
Not yet a BigML partner? Well, you can change that by contacting us today to find out more on how the new wave MLaaS platforms can help you deliver actionable insights and real-world smart applications to your clients within weeks.