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MLOps Training

Information

Our MLOps course delves into the full lifecycle of machine learning projects, from algorithm development to deployment and maintenance. Participants will learn to build robust ML models using industry-standard tools and frameworks, ensuring high performance and scalability. The course covers essential deployment strategies, including containerization and orchestration with Kubernetes, to facilitate seamless integration into production environments. Emphasis is also placed on monitoring and maintaining models post-deployment, with best practices for continuous integration and delivery (CI/CD) in ML workflows. By the end of the course, students will be equipped to manage and optimize end-to-end ML operations, ensuring reliability and efficiency in real-world applications.

$999.99

This course includes

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  • 20 hours of LIVE classes​

  • 2 articles to help you onboard

  • ​5 downloadable resources

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Why Choose Our Program?

  • Intensive, hands-on labs with real-world projects.

  • Mentorship from seasoned AI practitioners Step-by-step guidance to build a standout.

  • AI portfolio Networking with industry leaders and peers

  • Mentorship from seasoned AI practitioners Step-by-step guidance to build a standout.

  • AI portfolio Networking with industry leaders and peers

What you'll learn:

  • Build generative AI systems with OpenAI, RAG, and LLM Agents
  • Build artificial neural networks with Tensorflow and Keras
  • Implement machine learning at massive scale with Apache Spark's MLLib
  • Classify images, data, and sentiments using deep learning
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Data Visualization with MatPlotLib and Seaborn
  • Understand reinforcement learning - and how to build a Pac-Man bot
  • ​Clean your input data to remove outliers
Course Content:

Introduction to ML/AI for Data Engineers

  • Overview of ML/AI Concepts

  • Mapping Data Engineering to ML

  • Course Roadmap & Expectations

  • Weekly Mini Project

Fundamentals of ML, Linear Algebra and Statistics

  • ML Basics & Common Algorithms

  • Linear Algebra Essentials

  • Statistics & Hypothesis Testing

  • Integration and Wrap-up

EDA for ML

  • Data Visualization Techniques

  • Statistical Summaries & Descriptive Analytics

  • Outlier Detection & Data Quality Assessment

Feature Engineering for ML

  • Feature Extraction Techniques

  • Automated Feature Engineering Tools

  • Practical Feature Engineering Applications

  • Weekly Mini Project

Data Pipelines for ML

  • Designing ETL/ELT Processes for ML

  • Advanced Data Cleaning & Transformation

  • Integrating Real-Time Data Flows

  • Weekly Mini Project

Model Selection & Algorithm overview

  • Model Selection & Algorithm Overview

  • Hands-On Model Training

  • Best Practices in Training

Hyper-parameter Tuning and Model Evaluation

  • Hyper-parameter Tuning Techniques

  • Model Evaluation Metrics Part 1

  • Practical Evaluation Exercise

ML Ops & Model Deployment

  • Deployment Strategies & CI/CD for ML

  • Monitoring & Maintenance

  • Hands-On Deployment Exercise

  • Weekly Mini Project

Model Explainability & Ethics

  • Explainable AI (XAI) Techniques

  • Fairness in Machine Learning

  • Privacy and Data Governance in ML

Capstone Project

  • Capstone Project Kickoff

  • Project Work Time

  • Final Presentations and

  • Feedback

Industry Applications & Next Steps

  • Industry Case Studies

  • Career Pathways in ML

  • Course Wrap-up and Reflection

Who Should Apply:

  • Engineers seeking to pivot into ML/AI roles.

  • Professionals aiming to deepen their AI expertise.

  • Anyone passionate about leveraging AI in real-world scenarios

🔗 Limited Spots Available

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