Gen AI -Zero to Hero

Step-by-Step Plan to Learn Generative AI in GCP from Scratch to Professional Level.

Why do spend more money and time. Please follow these step by step plan to become master in Gen AI.

This plan is designed for individuals with no prior AI/ML experience who want to learn generative AI in Google Cloud Platform (GCP) and reach a professional level.

Phase 1: Foundational Knowledge (Estimated Time: 2–3 Months)

1. Introduction to AI and Machine Learning:

• Timeline: 1 month
• Resources:
• Google Cloud AI Platform Fundamentals Coursera specialization (Self-paced) https://www.coursera.org/learn/gcp-fundamentals

https://www.coursera.org/specializations/machine-learning-tensorflow-gcp

• “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig (Book) http://ai.berkeley.edu/course_schedule.html
• https://www.amazon.com/Artificial-Intelligence-A-Modern-Approach/dp/0134610997
• Machine Learning Crash Course by Google (YouTube series): https://developers.google.com/machine-learning/crash-course
• Courses: Take the following free courses on Coursera: “Machine Learning” by Andrew Ng, “Deep Learning Specialization” by deeplearning.ai, and “Natural Language Processing Specialization” by deeplearning.ai.
• Books: Read “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” by Aurélien Géron and “Deep Learning with Python” by François Chollet.
• Hands-on: Complete the TensorFlow tutorials and practice with Kaggle datasets.

2. Introduction to Generative AI:

• Timeline: 1 week
• Resources:
• Introduction to Generative AI — Google Cloud Skills Boost (Free course): https://cloud.google.com/ai/generative-ai
• https://www.cloudskillsboost.google/paths/118
• Generative AI blog post by Google Cloud (Article): https://cloud.google.com/ai/generative-ai
• https://cloud.google.com/blog/products/ai-machine-learning/generative-ai-for-industries
• “Generative Deep Learning: Teaching Machines to Create” by David Foster (Book): https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947
• Courses: Take the “Introduction to Generative AI” microlearning course on Google Cloud Skills Boost.
• Blogs & Articles: Read Google AI blog posts and articles about Generative AI.
• Videos: Watch the “Generative AI | Google Cloud” video on YouTube.

Google Cloud Platform (1 month)

• Create a Google Cloud Account: Sign up for a free trial account with Google Cloud Platform.
• Learn about Vertex AI: This is Google Cloud’s machine learning platform for training and deploying AI models. Complete the Vertex AI introductory labs.
• Learn about Generative AI tools: Explore Generative AI support in Vertex AI and Generative AI App Builder. Complete the relevant introductory labs.

3. Python Programming:

• Timeline: 1 month
• Resources:
• Python for Everybody Specialization on Coursera (Self-paced): https://www.coursera.org/specializations/python
• Automate the Boring Stuff with Python by Al Sweigart (Book): https://automatetheboringstuff.com/2e/chapter0/
• https://www.amazon.com/Automate-Boring-Stuff-Python-Programming/dp/1593275994
• Python Programming Tutorials on W3Schools (Website): https://www.w3schools.com/python/

4.Deep Dive into Generative AI Technology (1–2 months)

• Choose a specific Generative AI technology to specialize in:
• Text generation with LaMDA
• Image generation with Imagen
• Code generation with PaLM
• etc.
• Take advanced courses and workshops: Google Cloud offers a variety of advanced courses and workshops on specific Generative AI technologies.
• Experiment and build projects: Start small and gradually build more complex projects using Generative AI tools.
• Contribute to open-source projects: Find open-source Generative AI projects on GitHub and contribute your code.
• Join online communities: Participate in online forums and communities dedicated to Generative AI.

5.Professional Development (ongoing)

• Stay up-to-date: Keep up with the latest advancements in Generative AI by reading research papers, attending conferences, and following experts in the field.
• Build your portfolio: Showcase your Generative AI projects on your website or portfolio.
• Network: Connect with other professionals in the field and attend industry events.
• Get certified: Consider obtaining relevant certifications to demonstrate your expertise.

Phase 2: Hands-on with Generative AI (Estimated Time: 3–4 Months)

1. Vertex AI for Generative AI:

• Timeline: 2 months
• Resources:
• Vertex AI for Generative AI Documentation (Google Cloud): https://cloud.google.com/vertex-ai
• https://cloud.google.com/generative-ai-studio
• https://medium.com/google-cloud/getting-started-with-generative-ai-studio-on-google-cloud-5c77dfa8d044
• Build Your First Generative AI Model Lab (Google Cloud):

https://cloud.google.com/blog/products/ai-machine-learning/generative-ai-for-businesses-and-governments

• https://cloud.google.com/vertex-ai/docs
• Get Started with Generative AI Studio || Google Cloud Skills Boost || Arcade Sept level 3 || Gen AI
• Generative AI Studio Documentation (Google Cloud): https://cloud.google.com/generative-ai-studio

2. Generative AI App Builder:

• Timeline: 1 month
• Resources:
• Generative AI App Builder Documentation (Google Cloud): https://cloud.google.com/ai/generative-ai
• https://cloud.google.com/blog/products/ai-machine-learning/generative-ai-for-businesses-and-governments
• https://cloud.google.com/blog/products/ai-machine-learning/create-generative-apps-in-minutes-with-gen-app-builder
• Build Your First Generative AI App Lab (Google Cloud): https://cloud.google.com/blog/products/ai-machine-learning/create-generative-apps-in-minutes-with-gen-app-builder
• Get Started with Generative AI Studio || Google Cloud Skills Boost || Arcade Sept level 3 || Gen AI
• Generative AI App Builder Templates (Google Cloud): https://github.com/GoogleCloudPlatform/generative-ai

3. Advanced Generative AI Techniques:

• Timeline: 1 month
• Resources:
• “Generative Pre-trained Transformers 3: A Primer”: https://arxiv.org/abs/2305.10435
• “Diffusion Models: A Primer”: https://vsehwag.github.io/blog/2023/2/all_papers_on_diffusion.html
• Google AI Blog: https://blog.research.google/

Phase 3: Professional Development (Estimated Time: 6–12 Months)

1. Specialization in a Generative AI Domain:

• Timeline: 6–12 months
• Options:
• Natural Language Processing (NLP): https://nlp.stanford.edu/
• Computer Vision: https://opencv.org/
• Music and Audio Generation: https://arxiv.org/abs/1612.04928
• Code Generation: https://github.com/DeepCodeAI
• Scientific Data Generation: https://arxiv.org/abs/2112.03528
• Choose a specific domain based on your interests and career goals.

2. Build your Portfolio:

• Timeline: Ongoing
• Activities:
• Participate in Generative AI competitions and hackathons.
• Build personal projects showcasing your generative AI skills. *Contribute to open-source generative AI projects.
• Kaggle: https://www.kaggle.com/
• AIcrowd: https://www.aicrowd.com/
• OpenML: https://www.openml.org/

3. Network and Build Connections:

• Timeline: Ongoing
• Activities:
• Attend generative AI conferences and workshops.
• Join online communities and forums related to generative AI.
• Connect with generative AI researchers and practitioners.
• Timeline Disclaimer: This timeline is just an estimate and may vary depending on your individual learning pace and background.
• Google AI Events: https://www.androidpolice.com/how-to-watch-live-from-paris-google-event/
• Neural Information Processing Systems (NeurIPS): https://proceedings.neurips.cc/paper/2022
• International Conference on Learning Representations (ICLR): https://iclr.cc/Conferences/2022
• Online communities: https://www.reddit.com/r/MachineLearning/

Tips for Success:

• Be patient and persistent, learning generative AI takes time and effort.
• Focus on building strong foundational knowledge in AI, ML, and Python programming.
• Practice hands-on with the tools and techniques through labs and projects.
• Stay up-to-date with the latest research and advancements in generative AI.
• Network with other generative AI enthusiasts and experts.

By following this step-by-step plan and dedicating yourself to continuous learning, you can achieve a professional level of expertise in generative AI within GCP. Remember, the key to success is passion, persistence, and a willingness to learn from both your successes and failures.

Additional Resources:

• Google Cloud Generative AI resources: https://cloud.google.com/ai/generative-ai
• Google AI Blog: https://blog.research.google/
• Kaggle: https://www.kaggle.com/
• TensorFlow tutorials: https://www.tensorflow.org/tutorials and https://blog.tensorflow.org/
• Open-source Generative AI projects: https://github.com/topics/ai
• Google Cloud AI Blog: https://cloud.google.com/blog/products/ai-machine-learning
• https://blog.research.google/
• Google AI Research: https://ai.google/discover/research/
• TensorFlow: https://www.tensorflow.org/
• PyTorch: https://pytorch.org/
• Google AI Website: https://en.wikipedia.org/wiki/Google_Brain
• https://ai.google/discover/research/
• TensorFlow Blog: https://blog.tensorflow.org/
• PyTorch Blog: https://pytorch.org/blog/
• PyTorch Blog: https://blog.paperspace.com/ultimate-guide-to-pytorch/
• Papers With Code: https://paperswithcode.com/