August21: AI/ML Notes

How to to approach Machine Learning Problems.

1. Define the Problem
2. Prepare Data
3. Spot Check Algorithms
4. Improve Results
5. Present Results

Machine learning involvesCheck this article

1. data & data pipelines,
2. model training & tuning (i.e., experiments),
3. governance,
4. specialized tools for deployment,
5. monitoring and observability

Kickass ML/AI Tools

WEKA

• Start with Weka, the workbench for Machine Learning.
• Weka will let you apply a lot of different algorithms to your data without writing a single line of code.
• Even better: Weka will generate code for you!

HYDRA(hydra.cc)

• With Hydra, you can compose your configuration dynamically, enabling you to easily get the perfect configuration for each run.
• You can override everything from the command line, which makes experimentation fast, and removes the need to maintain multiple similar configuration files.