AI Engineer Study Plan for Working Professionals
This 12-week study plan is designed for working adults who have completed Andrew Ng’s Machine
Learning Specialization and want to advance into AI Engineering and AWS AI certifications. The
plan balances foundational understanding, hands-on practice, and certification preparation,
ensuring sustainable weekly progress.
Phase 1 — Build AI Engineering Foundations (Weeks 1–4)
• Study: “AI Engineering: Building Applications with Foundation Models” by Chip Huyen.
• Complement: “AI and Machine Learning for Coders” by Laurence Moroney.
• Focus Areas:
– Learn MLOps concepts and foundation model architecture.
– Understand model deployment pipelines and API integrations.
– Practice hands-on TensorFlow implementations from Moroney’s book.
• Goal: Understand how AI models are engineered and deployed in production environments.
Phase 2 — AWS AI Practitioner Certification (Weeks 5–6)
• Course: “Ultimate AWS Certified AI Practitioner (AIF-C01)” by Stephane Maarek on Udemy.
• Focus Areas:
– Core AWS AI/ML and generative AI services (SageMaker, Bedrock, Comprehend, Rekognition).
– Applying AI ethics, security, and responsible AI design.
– Review all section quizzes and take the full practice exam.
• Goal: Pass the AIF-C01 exam and understand AWS’s AI ecosystem from a high-level architectural
view.
Phase 3 — Hands-On ML and AWS Integration (Weeks 7–10)
• Study: “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” (O’Reilly).
• Focus Areas:
– End-to-end ML pipeline: data preparation → model → deployment.
– Experiment tracking and hyperparameter tuning.
– Transition to SageMaker workflows for model hosting.
• Goal: Gain the confidence to build, deploy, and evaluate models on AWS infrastructure.
Phase 4 — Deepen Specialization (Weeks 11–12)
• Continue exploring advanced AI/ML concepts:
– LLMs and prompt engineering.
– Model evaluation, versioning, and continuous learning.
– Explore “How AI Works: From Sorcery to Science” for conceptual depth.
• Optional: Prepare for the AWS Machine Learning Associate or Specialty certifications.
• Goal: Move toward applied AI Engineering or ML Engineer roles.
Weekly Commitment: 6–8 hours (divided into 3–4 study sessions).
Suggested Routine:
– 2 hours on weeknights (technical reading / video lessons)
– 3–4 hours on weekends (hands-on practice / quizzes / projects)
Outcome: By the end of 12 weeks, you’ll be equipped with practical AI engineering skills, AWS AI
certification readiness, and a strong portfolio foundation for cloud-based AI roles.