1. Introduction to ML Foundation.

2. AI and ML.

3. ML Pipeline.

4. ML Tools and Services.

5. Next Steps.

Data Scientist:

• Analyze and interpret complex digital data.

• Responsibility:

  1. Collect data by analyzing business results or by setting up new studies.

  2. Transfer data into a new format.

  3. Build tools to automate data collection.

  4. Search through large datasets for usable information.

  5. Create reports and presentations for business uses.

  6. Correlate similar data to find actionable results.

• Focusing:

  1. Analytical, statistical and programming skills.

ML Engineer

• Research, design and build ML models.

• Responsibility:

  1. Design ML systems.

  2. Research and Implement ML algorithms and tools.

  3. Develop ML apps according to requirements.

  4. Select appropriate datasets and data representation methods.

  5. Run ML tests and experiments.

  6. Perform statistical analysis and fine-tune by using test results.

  7. Train and Retrain systems when necessary.

• Focusing:

  1. Programming skill and software architecture.

Applied Science Researcher

• Do scientific research with a focus on applying the result of their studies to solving real-world problems.

• Responsibility:

  1. Use the scientific method to develop research questions.

  2. Conduct studies that lead to practical solutions.

  3. Apply ML skills to develop functional applications.

  4. Use ML to improve business outcomes.

• Focusing:

  1. The type of science that you decided to concentrate on.

ML Developer

• Research, design, and build machine learning models.

• Responsibility:

  1. Implemt ML systems.

  2. Research and implement appropriate ML algorithms and tools.

  3. Develop ML applications.

  4. Select appropriate datasets and data representation methods.

  5. Run ML tests and experiments.

  6. Perform statistical analysis and fine-tune by using test results.

  7. Train and retraining systems.

• Focusing:

  1. Software development skills.

  2. Data scientist.