Challenge Based Learning

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Our AI for Good Challenges allow you to build production ready state-of-the-art AI solutions for impact organizations that contribute to the SDG's, in 10 weeks. During the Challenge you will be guided and mentored by a team of AI experts to make sure you'll reach your learning goals and make a positive impact. The best part? By the end you're a certified AI for Good Engineer!

Engineer, mentored by experts.

Start your AI for Good Engineer journey and achieve your AI learning goal in 10 weeks by joining Challenges, follow bootcamps & masterclasses.

• 1-on-1 Mentor sessions
• Real-world project
• Personalized learning journey
• Positive impact
• Teamwork
• Authorized certificate
• Often eligible for self-development budget

The program

Onboarding (1 week)

• Onboarding on our platform & Slack, digital skills assessment, determining their current skill tree
• 1-on-1 onboarding with AI expert mentor to assess their skill level & determine learning goals, examples of tracks:
  • MLOps Engineer
  • Data Engineer
  • ML Engineer
  • Product Owner
• Set learning goals in the platform & determine the role & tech domain they will work on
• Develop their personal learning journey based on the delta between the current skills & skillset associated with the target role
• Get recommendations on courses to follow by AI expert, based on their unique learning needs

Participate in AI for Good challenge (10 weeks) - examples of challenges

• Kickoff with challenge owner - examples of challenge owners: Stanford professors, marine biologists, Greenpeace activists, government officials.
• AI & domain Masterclasses
• Join a subteam & assigning a role based on learning goals
• Weekly meetings with the challenge owner to discuss progress, ask questions & course correct
• Weekly subteam meetings to discuss blocks, strategize next steps & determine deliverables
• Asynchronous contact during working hours with expert AI mentor on Slack & email for questions & support
• Bi-weekly 1-on-1 Mentoring calls to discuss progress on learning goals, determine new learning resources, ask questions
• Midterm presentation to challenge owner
• Endterm presentation to challenge owner
• Writing of publication about the challenge & open-sourcing the code (some participants)

Assessment & Accreditation (1 week)

• Peer review: The engineers do a peer review where they rank the performance of each other on specific skills. Skills on which engineers score in the 80th percentile will be accredited with a ‘skill badge’ and added to their skill tree. These skills have been shown in their real-world performance in the challenge.
• Mentor assessment: The engineers are assessed on the skills in their learning goal by their expert AI mentor
• Certification: They receive a certificate representing all the skills accredited, the challenge they completed which is now part of their portfolio.