Advanced Machine Learning Algorithms

You can learn how ML Algorithms work from MLOps standpoint

In this course, you will learn Advanced ML Algorithms from the MLOps perspective. We won’t focus on research, but on how to run modern models in production, and how their type affects serving at scale, latency, safety, and cost.

We’ll explore transformers and NLP models, time series forecasting and anomaly detection, recommender systems, and probabilistic & Bayesian models. You’ll learn how to run and monitor LLMs, manage performance and cost, track drift and quality, and solve real incidents with hands-on labs and a capstone.

You’ll also see how advanced model behavior connects to infrastructure, CI/CD, observability, and budget planning. This course prepares you for senior MLOps work with complex, high-impact models in real products.

Course Content

  • Course Introduction
  • About Your Instructor
  • Course Structure
  • ML Algorithms in day-to-day activities of MLOps
  • Global usage trends in ML models
  • Overview of ML Models
  • GitHub repositories
  • What is Deep Learning
  • What is Transformers
  • Examples of Transformers
  • Ops Features of Transformers
  • Sentiment analysis on customer reviews, Hands-on Lab
  • What is NLP Models
  • Examples of NLP Models
  • Ops Features of NLP Models
  • Privacy-Safe Customer Support, Hands-on Lab
  • What is Time Series & Anomaly Detection
  • What is Time Series Forecasting
  • Examples of Time Series Forecasting
  • Ops Features of Time Series Forecasting
  • Energy demand prediction, Hands-on Lab
  • What is Anomaly Detection
  • Examples of Anomaly Detection
  • Ops Features of Anomaly Detection
  • Fraud detection in financial transactions, Hands-on Lab
  • What is Recommender Systems
  • Types of Recommender Systems
  • Examples of Recommender Systems
  • Ops Features of Recommender Systems
  • Movie recommendation with matrix factorization, Hands-on Lab
  • What is Specialized Models
  • What is Probabilistic & Bayesian Models
  • Probabilistic & Bayesian Models Examples
  • Ops Features of Probabilistic & Bayesian Models
  • Uncertainty-aware medical diagnosis, Hands-on Lab
  • What factors should you consider when choosing the right model
  • Performance aspects of different ML models
  • MLOps Problems Related to Specific Algorithms
  • Trade-off matrix for ML models
  • Selecting models for three real-world case studies, Hands-on Lab

Start learning high demand tech skills today

About Your Instructor

Hi, I’m Alex and I’ve spent over 20 years helping well known startups and enterprises introduce innovations. I also developed and taught Cloud&DevOps part for a Master’s Degree at the University.

In this course, I’ll show you what MLOps looks like in practice – step by step, with real tools and clear guidance.

You don’t need to be an expert. If you want to understand how to start or enforce your career as MLOps Engineer, not just in theory, but in real life, this course is for you. Let’s get started.

All courses are developed by experienced instructors with over 10 years of real-world industry expertise. We focus on delivering practical, up-to-date content – not just collecting enrollments, so that every course gives you real value.

Our courses meet high academic standards, and we’re actively working on certification to ensure they align with recognized best practices.

Each course includes video lectures, hands-on labs with screen recordings, quizzes, reading materials, GitHub repository with real project code, and a capstone project. This structure is designed to help you build practical, in-demand skills and knowledge that employers care about.

However, if you’re not satisfied for any reason, you can request a refund in accordance with our Refund Policy – your satisfaction matters to us.

It’s not just skills. It’s your next chapter.

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