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Catalog / All levels / AI Data Deployment: AI Data Specialist 401 (ADS-401)
Mammoth Club All levels 3 sections 12 lectures

AI Data Deployment: AI Data Specialist 401 (ADS-401)

Created by Alex Kropf
·
12 lectures
01
Skill level
All levels
02
Sections
3
03
Lectures
12
04
Instructor
Alex Kropf
What's inside

This course includes.

✓
3
Sections
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content

Curriculum & lectures.

3 sections · 12 lectures
+ Module 1 – Building AI-Ready Datasets 5 lectures Preview
Introduction to AI Dataset Deployment Lifecycle – How data moves from lab to production. Locked
Production Dataset Standards and Validation – Schema enforcement, data quality checks, and metadata management. Locked
Data Versioning and Tracking – Tools like DVC and LakeFS for managing evolving datasets. Locked
Synthetic Data Generation for Scalability – Use AI to create balanced and compliant datasets. Locked
Hands-On: Prepare a Production-Grade Dataset – Validate and document dataset readiness. Locked
+ Module 2 – Deploying AI Datasets for Real Projects 5 lectures
Data Integration into ML Pipelines – Connecting datasets to model training and inference stages. Locked
APIs and Data Access Management – Deliver datasets securely via REST, GraphQL, or cloud storage. Locked
Continuous Data Delivery (DataOps) – Automate updates using CI/CD principles for datasets. Locked
Deploying to Cloud Data Platforms – AWS S3, Azure Data Lake, and Google Cloud Storage workflows. Locked
Hands-On: Deploy an AI Dataset to a Cloud Repository – Implement an automated dataset deployment workflow. Locked
+ Module 3 – Maintaining and Updating AI Production Data Pipelines 2 lectures
Data Drift and Model Monitoring – Detect shifts in data distributions over time. Locked
Pipeline Maintenance and Troubleshooting – Monitor logs, failures, and dependency health. Locked
Description

About this course.

AI Data Specialist 401 is an advanced course that examines how datasets are prepared, deployed, managed, and maintained throughout an AI system’s lifecycle. Rather than focusing on technical configuration, the course explores the conceptual models, decision frameworks, and architectural principles behind successful AI data deployment in real-world environments.

Students will develop a deep understanding of how datasets must be structured, validated, governed, and monitored to support reliable AI development and production systems. By focusing on theory, learners gain the strategic and analytical perspective needed to oversee AI deployment operations at a high level.

Instructors

Taught by people who ship.

Alex Kropf

Alex Kropf

Mammoth Club's CLO, public speaker, consultant, IT author and Senior Software Developer. Alex has produced best-selling courses, books and workshops for Mammoth Club, Course Pro and our clients since 2016.

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