• Understand the concept and scope of Automated Machine Learning.

• Learn how AutoML simplifies end-to-end ML workflows.

• Apply AutoML tools to automate model training and tuning.

• Explore meta-learning and neural architecture search concepts.

• Automate feature selection and data preprocessing.

• Evaluate AutoML models for accuracy and interpretability.

• Integrate AutoML into cloud-based AI platforms.

• Build and deploy production-ready AutoML pipelines.

• Address limitations, bias, and overfitting in automated systems.

• Prepare for data science and AI automation roles.

Course Syllabus

Module 1: Introduction to Machine Learning and Automation
Module 2: Overview of AutoML Concepts and Architecture
Module 3: Data Preprocessing and Feature Engineering Automation
Module 4: Model Selection and Hyperparameter Optimisation
Module 5: Neural Architecture Search (NAS) and Meta-Learning
Module 6: Tools and Frameworks – H2O.ai, Auto-sklearn, PyCaret, DataRobot
Module 7: Cloud-Based AutoML – Google Vertex AI and AWS SageMaker
Module 8: Evaluating and Interpreting AutoML Models
Module 9: Ethics, Fairness, and Bias in Automated AI
Module 10: Capstone Project – Build and Deploy an AutoML Workflow

Upon completion, learners receive a Certificate of Completion from Uplatz, validating their expertise in Automated Machine Learning (AutoML). This Uplatz certification recognises your ability to build, train, and deploy machine learning models automatically using industry-grade tools.

The certification aligns with modern enterprise needs for AI automation, rapid prototyping, and scalable model deployment. It’s ideal for data scientists, analysts, and business professionals who want to accelerate their AI workflows without deep algorithmic coding.

Holding this certification demonstrates that you can streamline the machine learning lifecycle — from data ingestion to deployment — using automation and intelligent orchestration.

The demand for AutoML specialists is growing rapidly as companies seek to integrate AI without expanding data science teams. Completing this course from Uplatz prepares you for roles such as:

• AutoML Engineer

• Machine Learning Engineer (Automation)

• AI Product Developer

• Data Scientist (Rapid Prototyping)

• MLOps Engineer

Professionals in this domain typically earn between $105,000 and $190,000 per year, depending on experience and region.

Career opportunities are abundant in finance, healthcare, retail, and cloud-based AI services, where automation reduces model development time and cost. This course empowers you to create end-to-end AI systems that learn, optimise, and deploy autonomously — the future of scalable machine intelligence.

1. What is AutoML?
   AutoML automates the end-to-end machine learning workflow including model training, tuning, and deployment.

2. What are the benefits of AutoML?
   Faster model development, higher accuracy, reduced manual effort, and accessibility to non-experts.

3. What tasks can AutoML automate?
   Data preprocessing, feature engineering, model selection, and hyperparameter tuning.

4. What are popular AutoML frameworks?
   H2O.ai, Auto-sklearn, PyCaret, DataRobot, Google Vertex AI.

5. What is neural architecture search (NAS)?
   An AutoML technique that automates the design of deep neural network structures.

6. What are common challenges of AutoML?
   Interpretability, bias control, and computational cost.

7. What is meta-learning in AutoML?
   Using previous learning experiences to improve future model selection automatically.

8. How does AutoML handle feature selection?
   Through embedded algorithms that evaluate and rank features based on importance metrics.

9. Is AutoML replacing data scientists?
   No — it augments their work by automating repetitive tasks while retaining human oversight.

10. What is the future of AutoML?
    Integration into enterprise MLOps pipelines for continuous, adaptive AI systems.