Description

Objective

– Get ready to design and build robust Machine Learning-based applications that operate in real-world environments.

Pre-requisites

– You need to pass the BigML Certified Engineer course in order to enroll in the BigML Certified Architect course.

1. Machine Learning Engineering

a). Real-world Machine Learning

b). Building end-to-end Machine Learning applications

c).How to size and address your project

2. Machine Learning Workflows as Predictive Models

a). Models complexity

b). Memory requirements

c). Predictions time lapse

d). Combined models as a new model

3. Integrating ML in the Data Pipeline I

a). Immediately actionable models: The Dashboard

b). Local or remote

c). Model export and packaging: Up or Down

d). Models called from third part applications: Zapier

e). Models embedded in third part applications

4. Integrating ML in the Data Pipeline II

a). Single model, time-sensitive predictions

b). Local memory management

c). Predict Server

5. Integrating ML in the Data Pipeline III

a). Multiple models, batch predictions

b). Retraining and monitoring workflows

c). Client versus server complexity

6. Models in IoT

a). BigML Node-RED

7. Machine Learning End-to-End Applications I

a). Tailored ML apps

b). Acquiring and defining the data entities

c). Storing modeling workflows

8. Machine Learning End-to-End Applications II

a). Data shift monitoring

b). Retraining and monitoring workflows

For more inputs on BigML Certified Architect you can connect here.
Contact the L&D Specialist at Locus IT.

Locus Academy has more than a decade experience in delivering the training/staffing on BigML Certified Architect for corporates across the globe. The participants for the training/staffing on BigML Certified Architect are extremely satisfied and are able to implement the learnings in their on going projects.