Traditional courses take months. Get job-ready in weeks.
Human-curated paths ready to go: AWS, GCP, ML, AI
AI tutors create custom paths for your career goals
Real sessions with AI coaches, not boring videos
Upload CV + job spec: tailored learning path
No time for a live session today? Listen to a podcast instead!
Every session is with an expert AI tutor, available 24/7. No scheduling, no waiting. Learn when you want, at your pace, with immediate personalized feedback.
🎯 Built by professionals, for professionals who want to grow
40 ready-made plans, created by professionals for professionals. Import and start today.
Can't find what you're looking for? We can create custom tailored paths just for you.
Introduction to the ML Lifecycle and AWS's high-level AI services. This lesson will cover the strategic choice between using pre-built AI services versus building custom models. We will explore managed services for vision (Amazon Rekognition for image/video analysis) and language (Amazon Comprehend for NLP, Amazon Translate for translation), focusing on their common business use cases, API-driven nature, and how they accelerate project delivery for professionals without deep ML expertise.
This lesson introduces the core challenge of maintaining model performance in production: model drift. We will define the concept of drift and explore its four primary types as identified by AWS: Data Drift (changes in input data statistics), Model Drift (degradation of prediction quality), Bias Drift (changes in fairness metrics), and Feature Attribution Drift (shifts in feature importance). We will then introduce Amazon SageMaker Model Monitor as the primary service to combat this, covering its automated workflow: establishing a baseline from training data, capturing live inference data, and running scheduled monitoring jobs to compare production data against the baseline to detect deviations.
This lesson introduces the core concepts of data visualization within the AWS ecosystem, focusing on Amazon QuickSight. It begins by identifying different types of data consumers (from data experts to business users) and their needs. We will then dive into QuickSight as a cloud-native, serverless BI service, exploring its key differentiators, such as the SPICE in-memory engine for high performance. The session will cover how to connect to various AWS and third-party data sources, the distinction between a "Data Source" and a "Dataset", and the fundamental steps of data preparation within the QuickSight UI. You will learn the basics of the analysis workspace, including the concepts of dimensions and measures, and how to use AutoGraph to create your first visual.
✨ Immersive AI-powered learning experience
Login to discover more. For ambitious professionals ready to grow
All available skills:
✨ Personalized study plans for every subject, adapted to your level
You can also create your own custom topic!
Personalized training that adapts to your pace and career goals
No generic pre-recorded videos. Only live personalized sessions for you: deep theory or practical assessment. You choose.
Spotted an interesting job posting? Upload CV + job spec: we prepare you with targeted questions, gap analysis and realistic simulations.
Create personalized podcasts from your topics and learn while doing other things: commuting, gym, lunch break.
Every session knows you better. The system remembers your level, gaps and goals across topics and sessions. Becomes increasingly professional and relevant.
Each lesson enriches your profile. Didaxa becomes smarter and more personalized for you.
Real growth stories
"Needed to learn TypeScript for a new role. 4 focused sessions with Didaxa and I was shipping code in a week. Would've taken me a month with YouTube tutorials."
David Martinez
Software Engineer
"Prepared for a tech interview using CV analysis and mock sessions. Got the job at Amazon. The personalized approach saved me weeks of generic prep."
Jennifer Wu
Product Manager
"Learning data analytics while working 60-hour weeks seemed impossible. On-demand sessions fit perfectly into my chaotic schedule. Now I'm leading data projects."
Damien Thompson
Finance Professional