AI for Everyone

Starting an AI projects

• Workflow of a project.
• Select AI project (Framework).
• Organizing data and teams for the project.

Workflow of a Machine Learning project

1. Collect data.
2. Train the model. (Iterate many times until achieve the result.)
3. Deploy model. (Get back maintain and update.)

Workflow of a Data Science project.

Unlike a machine learning project, the output of a data science project is often a set of actionable insights, a set of insights that may cause you to do things differently.

1. Collect Data.
2. Analyze Data. (Iterate many times to get good insights)
3. Suggest Hypotheses/actions (Deploy changes, re-analyze new data)

Build a AI company

Case Study: Smart speaker To get a better idea about what is need it to build a AI company it is important to get and idea of what is need it in an AI system.

Steps to process a command 1. Trigger word/wakeword detection ("Hello Device"). 2. Speech recognition. 3. Intent recognition. 4. Execute joke.

Case study: Self-driving car

the key steps: 1. Car detection. 2. Pedestrian detection. 3. Motion Planning.

Example roles of a AI team

This roles might have other titles but the task that they perform will be similar:

• Software Engineer: those who develop the business logic, like the joke execution or those to ensure self-driving reliability.
• Machine Learning Engineer: those that create the model, that take data A and produce result B.
• Machine Learning Researcher: extend state-of the art in ML.

Note: the last two roles can be, in some cases, just one and it is called "Applied ML Scientist"

• Data Scientist: Examine data and provide insights, make presentation to team and executives.
• Data Engineer: Organize data, make sure data is saved in a easily accessible, secure and cost effective way.
• AI Product Manager: Help decide what to build; what's feasible and valuable.

AI transformation Playbook

to create or move towards AI the company might need to restructure itself.

1. Execute pilot projects to gain momentum. * More important for the initial project to succeed rather than be the most valuable. * Show traction within 6-12 months. * Can be in-house or outsourced.

2. Build an in-house AI team

It is better have a dedicated unit to work in this projects that a Business unit aside.

3. Provide broad AI Training

Role	What They Should learn
Executives and senior business leaders	1. What AI can do for your enterprise. 2. AI strategy. 3. Resource allocation.
Leaders of devisions working on AI projects	1. Set project direction (technical and business diligence) 2. Resource allocation. 3.Monitor Progress
AI engineer trainees	1. Build and ship AI software. 2.Gather data. 3. Execute on specific AI projects

4 Develop an AI strategy

• Leverage AI to create an advantage specific to your industry sector.
• Design strategy aligned with the "Virtuous Cycle of AI".

• Consider Creating a data strategy ( strategic data acquisition, unified data warehouse).

5. Develop internal and external communications

• Investor relations.
• Government relations.
• Consumer/user education.
• Talent/recruitment.
• Internal Communications.

Some application of AI

watch the video here Video about AI application