Top 5 skills you need to get your Machine Learning POC into production

Why is ML so hard?

  1. Knowing how to frame an ML problem with the available data
  2. Analysing the data to make sure it’s prepared and ready for ML
  3. Investigating whether to train a custom model or use an off-the-shelf ML service
  4. Training the ML model effectively
  5. Evaluating the model and eliminating bias

1. Knowing how to frame an ML problem with the available data

  • the overall business problem you’re trying to solve,
  • what data you have available or need to help train the model to solve all or part of the problem, and
  • whether ML is a suitable solution to help solve the problem.

Here’s a client example:

2. Analysing the data to make sure it’s prepared and ready for ML

  • Accessibility: Does it exist? How do I access it? Can I ethically use it?
  • Fidelity: How was it collected? Was it aggregated? How accurate is it?
  • Context: Is it suitable for my purpose? For example, consumer behaviour data collected during pre-pandemic times might reflect a particular set of priorities which may make them totally unsuited for training a model to make predictions in the current situation.

Here’s a client example:

3. Investigating whether to train a custom model or to use an off-the-shelf ML service

  • AWS: Textract, Rekognition, Forecast, Lex, Polly, Transcribe and Translate
  • Google: Text to speech, speech to text, DocumentAI, video/image intelligence AI, vision OCR, natural language, and recommendation AI
  • Microsoft Azure: Knowledge mining, conversational AI, document process automation, translation and speech transcription.

Here’s a client example:

4. Training the ML model effectively

Here’s a client example:

5. Evaluating the model and eliminating bias

Here’s a client example:

Moving beyond the POC

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