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.
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.
Introduction to firewall concepts and the legacy `iptables` tool. This lesson covers the fundamentals of layered security, the role of the `netfilter` kernel framework, and the structure of `iptables` (tables, chains). The main activity will be to construct a basic, stateful IPv4 firewall script that allows loopback traffic, established and related connections, and opens the SSH port (22) to maintain server access.
This lesson provides a foundational overview of the history and core concepts of data analytics, leading into the modern AWS data analytics pipeline and the concept of a data lake, based on the provided document 'History of Analytics and Big Data'. The session will start by covering the historical evolution from traditional data warehousing to the current 'New World Order' driven by technologies like Hadoop and cloud computing. We will then dissect the modern analytics pipeline, covering the key stages: Collection, Storage (hot, warm, cold data), Processing, and Visualization (referencing the Gartner Analytics Maturity Model). The majority of the lesson will focus on mapping these conceptual stages to the AWS Big Data Reference Architecture, identifying core services like Amazon S3, AWS Glue, Amazon Kinesis, and Amazon QuickSight. Finally, the lesson will define the data lake concept and briefly outline the five steps to building one on AWS, positioning AWS Lake Formation as a service that simplifies this process. The lesson will conclude with a recommendation to attempt the assessment questions from the chapter to self-evaluate understanding.
✨ 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