Accelerated AI: A Practical Course on AutoML Tools & Applications

Introduction

The field of machine learning has long required extensive expertise in data science, feature engineering, and model tuning. However, the rise of Automated Machine Learning, or AutoML, has democratized AI by making it accessible to a much broader audience. AutoML platforms automate many of the time-consuming and complex steps in the machine learning workflow, allowing both experts and non-experts to build high-performing models quickly and efficiently.

This course is your entry point into the transformative world of AutoML. Over five days, you'll learn how to leverage powerful automated tools to streamline your AI projects. We will cover the core concepts of AutoML, explore leading platforms, and walk through practical applications for common business problems. By the end of this training, you will be able to harness the power of automation to accelerate your model development and deployment.

Duration 5 days

Target Audience This course is for data analysts, business intelligence professionals, and developers who want to apply machine learning to their work without the need for extensive data science expertise. It is also suitable for data scientists looking to increase their efficiency.

Objectives

  • To understand the core concepts and workflow of Automated Machine Learning.
  • To recognize the benefits and limitations of using AutoML tools.
  • To gain hands-on experience with a leading AutoML platform, such as Google Cloud AutoML or H2O.ai.
  • To learn how to prepare and import data for use in an AutoML pipeline.
  • To be able to effectively interpret the results and insights provided by AutoML.
  • To understand the different types of problems that AutoML is best suited for.
  • To explore techniques for fine-tuning automated models.
  • To learn how to deploy an AutoML-generated model for production use.
  • To recognize the ethical considerations and potential biases in automated models.
  • To develop a strategy for integrating AutoML into a larger MLOps pipeline.

Course Modules

Module 1: What is Automated Machine Learning?

  • The traditional machine learning workflow.
  • The "black box" problem and how AutoML addresses it.
  • The key components of an AutoML system: feature engineering, model selection, hyperparameter tuning.
  • A conceptual overview of how AutoML automates these steps.
  • The promise and reality of AutoML.

Module 2: A Tour of AutoML Tools

  • A comparison of commercial AutoML platforms.
  • An overview of open-source libraries.
  • The user interface and workflow of a chosen platform (e.g., Google Cloud AutoML).
  • A brief discussion on platform pricing and scalability.
  • Choosing the right AutoML tool for your project.

Module 3: Data Preparation for AutoML

  • The importance of clean, structured data.
  • Common data formats and how to load them.
  • Handling missing values and data inconsistencies.
  • Feature engineering vs. automated feature generation.
  • The crucial step of data splitting (train/test).

Module 4: Building Your First Automated Model

  • A guided, hands-on project using an AutoML platform.
  • Importing your dataset.
  • Setting up the training parameters.
  • Running the automated pipeline.
  • Interpreting the training dashboard and results.

Module 5: Interpreting Results and Explainability

  • Understanding the leaderboards of different model configurations.
  • What is model explainability?
  • A deep dive into feature importance scores.
  • Visualizing model predictions and performance.
  • Ethical AI: understanding and mitigating bias in automated models.

Module 6: Model Evaluation and Fine-Tuning

  • Beyond accuracy: using precision, recall, and F1-score.
  • The confusion matrix for classification tasks.
  • Identifying overfitting and underfitting in AutoML models.
  • How to manually adjust parameters to improve performance.
  • A case study on iterative model improvement.

Module 7: The AutoML Deployment Pipeline

  • The seamless transition from training to deployment.
  • A walk-through of a model deployment process.
  • Serving your model as an API endpoint.
  • The concepts of online and batch prediction.
  • A discussion on model latency and throughput.

Module 8: AutoML for Different Problem Types

  • Classification: when to use AutoML for this task.
  • Regression: predicting continuous values.
  • An introduction to automated computer vision and natural language processing.
  • Time series forecasting with AutoML.
  • The limitations of AutoML for complex or custom problems.

Module 9: Integrating AutoML into MLOps

  • The role of AutoML within a larger MLOps framework.
  • Automating the entire model lifecycle from data to deployment.
  • Versioning your models and datasets.
  • Setting up a feedback loop for continuous improvement.
  • A discussion on the future of MLOps and AutoML.

Module 10: Advanced Topics & Best Practices

  • A discussion on ensemble methods.
  • The importance of data leakage.
  • Best practices for model validation.
  • How to monitor a deployed AutoML model.
  • A review of real-world success stories.

Module 11: Case Studies in Business

  • A case study on customer churn prediction.
  • Using AutoML for fraud detection.
  • A case study on inventory optimization.
  • Applying AutoML to optimize marketing campaigns.
  • A discussion on the return on investment (ROI) of AutoML.

Module 13: The Future of AutoML

  • An overview of the latest advancements in the field.
  • The rise of large language models (LLMs) and foundation models.
  • The ongoing debate: AutoML vs. expert data scientists.
  • A look at the next generation of automated tools.
  • Final Q&A and course wrap-up.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Accelerated Ai: A Practical Course On Automl Tools & Applications in Namibia
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