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Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Machine Learning Rapid Prototyping with IBM Watson Studio
This IBM Web-Based Training (WBT) is Self-Paced and includes: - Instructional content available online for duration of course - Visuals without hands-on lab exercises An emerging trend in AI is the...
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$249 USD
GSA
$191.74
Course Code
W7072G-WBT
Duration
1.125 days
Available Formats
Self Paced
This IBM Web-Based Training (WBT) is Self-Paced and includes:
- Instructional content available online for duration of course
- Visuals without hands-on lab exercises
An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio- s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research. The focus will be on working with an auto-generated Python notebook. Learners will be provided with test data sets for two use cases.
Purchase this course as part of a subscription
Skills Gained
- Building a rapid prototype of Watson Studio AI
- Automated Data Preparation and Model Selection
- Automated Feature Engineering and Hyperparameter Optimization
- Evaluation and Deployment of AutoAI-generated Solutions
Who Can Benefit
This course is intended for practicing Data Scientists. While it showcases the automated AI capabilities of IBM Watson Studio with AutoAI, the course does not explain Machine Learning or Data Science concepts.
Prerequisites
In order to be successful, you should have knowledge of:
- Data Science workflow, Data Preprocessing, Feature Engineering, Machine Learning Algorithms, Hyperparameter Optimization, Evaluation measures for models, Python and scikit-learn library (including Pipeline class)
Course Details
Course Outline
Building a rapid prototype of Watson Studio AI - Describe the benefits of AutoAI for rapid prototyping - Identify implementations of AutoAI - Become familiar with the Watson Studio platform - Build rapid prototypes using Watson Studio AutoAI - Generate a Python notebook of the prototype with one click Automated Data Preparation and Model Selection - Evaluate the data preprocessing steps for the use cases - Refine data preprocessing using the AutoAI-generated Python notebook - Examine the model selection outcome for use cases - Refine the Python notebook to make changes to the selected model Automated Feature Engineering and Hyperparameter Optimization - Explain how the Cognito algorithm can save time by automating feature engineering - Evaluate the automated feature engineering performance for the use cases - Describe several strategies for HPO in order of increasing sophistication - Observe how changes to the model hyperparameters in the Python notebook affect the prototype's performance Evaluation and Deployment of AutoAI-generated Solutions - Evaluate the prototype for further development or deployment based on calculated performance metrics - Deploy the prototype using Watson Machine Learning
