Lvl.3 -> MLOps

Pre-requisite: Lvl.1 Machine Learning Apprentice and optionally Lvl.2 Machine Learning Engineer
Approx. Time Required: 2 months

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This MLOps career path provides a comprehensive journey through advanced machine learning techniques and the end-to-end machine learning lifecycle. It begins with Machine Learning Level 2, where you will dive deep into Gradient Boosting models such as XGBoost, LightGBM, and CatBoost. You will master the intuition and application of these models for solving real-world regression and classification problems, while also covering key concepts like tree pruning, K-fold cross-validation, and hyperparameter tuning. Moving into Machine Learning Level 3, the focus shifts to building ensemble models and automated machine learning pipelines using cloud computing tools like Amazon SageMaker and AWS Lambda. You will gain practical experience with cloud-based pipelines, preparing you to work efficiently in production environments. The final course, MLOps: From Zero to Hero, equips you to manage the entire machine learning workflow. From data preparation and pipeline automation to model deployment and monitoring, this course ensures that you can streamline and scale machine learning workflows, bridging the gap between development and production to ensure seamless transitions and efficient operations.

1.Machine Learning Level 2 (Skip if you already completed the Lvl.2 Machine Learning Engineer career path)

Dive into the world of Gradient Boosting models with this hands-on, project-focused course. This course is your gateway to understanding and applying Gradient Boosting models, specifically focusing on XGBoost, LightGBM, and CatBoost for regression and classification problems.

  • Learn the intuition and mathematics behind Decision Trees, Random Forests, and Gradient Boosting models.
  • Master XGBoost by building models to tackle real-world regression and classification problems, understand tree pruning, learning rate adjustments, and advanced concepts like K-fold cross-validation and the bias-variance tradeoff.
  • Explore LightGBM, leveraging histogram-based splits, exclusive feature bundling, and gradient-based one-side sampling to build efficient models with real case studies.
  • Delve into CatBoost, discovering the power of target encoding, ordered boosting, and symmetric trees through hands-on projects designed to solve practical challenges.

Equipped with practical experience and an in-depth understanding of three of the most powerful machine learning frameworks, you’ll be ready to tackle complex machine learning challenges, optimize model performance, and significantly improve prediction accuracy in your projects. This course prepares you for advanced machine learning roles by providing you with the skills to make informed decisions on which ensemble technique to apply for optimal results.

2. Machine Learning Level 3

Dive into the world of Ensemble Models and Machine Learning Pipelines built with Cloud Computing through this hands-on and project-focused course. This course is your gateway to developing Ensemble Models and Machine Learning Pipelines by leveraging Cloud Computing with powerful tools like Amazon SageMaker and AWS Lambda for Regression and Classification problems.

  • Learn the intuition behind Ensemble Models and Machine Learning Pipelines.
  • Master Ensemble Models with SageMaker Canvas and SageMaker Studio Classic.
  • Delve into Machine Learning Pipelines, discovering the power of AWS Lambda, designed to build automated machine learning pipelines.

Equipped with practical experience and an in-depth understanding of probably the most powerful machine learning frameworks, you’ll be ready to tackle complex machine learning challenges, optimize model performance, and significantly improve prediction accuracy in your projects. This course prepares you for advanced machine learning roles by providing you with the skills to make informed decisions on which ensemble technique to apply for optimal results.

3. MLOps: From Zero to Hero

This comprehensive course is designed to provide a deep dive into the world of MLOps (Machine Learning Operations), equipping you with the skills and knowledge to manage the end-to-end lifecycle of machine learning projects. Whether you’re a data scientist, engineer, or developer, this course will guide you through the key stages of MLOps, from data preparation to model deployment, with a focus on scalability and efficiency.

  • Get introduced to the fundamentals of MLOps, exploring how it bridges the gap between machine learning development and production, ensuring smooth workflows, collaboration, and reliability.

  • Learn the essential steps to building and automating data pipelines for machine learning. This module covers data cleaning, transformation, and optimization, ensuring your data is ready for model training.

  • Gain practical experience with the entire model development lifecycle. You’ll train, evaluate, and fine-tune models, and learn how to deploy them into production environments with best practices for monitoring and iteration.

  • A collection of industry-standard tools, techniques, and workflows to master MLOps. Learn about the latest advancements in automation, monitoring, and model maintenance, enabling you to scale machine learning applications efficiently.

This course is ideal for anyone looking to streamline and automate machine learning workflows, ensuring smooth transitions from model development to production.

👉 Remember to join the Mentorship Program [here] to grow even faster!