This course is part of MLOps | Machine Learning Operations Specialization
ML Algorithms for MLOps Course will be AVAILABLE SOON.
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What you’ll learn
✔️ Understand how different ML algorithms behave in production, and why this affects deployment, monitoring, and resource usage.
✔️ Learn which metrics to track for different types of models, and how to check if a model is drifting or underperforming in real time.
✔️ Work with NLP and time series models in pipelines, with focus on latency, batching, and scaling in production environments.
✔️ Use explainability tools to make model decisions more clear and trusted during audits or incidents.
There are 12 modules in this course
- Introduction
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Time Series Forecasting
- Natural Language Processing (NLP)
- Statistical Modelling & Evaluation
- Model Selection and Optimization
- Algorithm Performance at Scale
- ML Algorithms and Monitoring
- Algorithm-Specific MLOps Challenges
- Case Studies
- Capstone Project
About this course
In this course, you will learn how Machine Learning algorithms work from an MLOps point of view. We will not focus on building models from scratch, instead, we will focus on how to work with them in production. You will see how the type of model affects deployment, monitoring, explainability, speed, and cost.
We will explore different kinds of algorithms, like regression, decision trees, clustering, NLP, and time series models, and discuss what MLOps Engineers need to know when these models are part of a real system. For example, how to choose the right evaluation metric, how to track model drift, how to scale NLP models, or how to explain model behavior during audits.
You will also learn how to support ML models in production – which metrics to monitor, how to test performance over time, and what to do when things go wrong. This includes both classic ML models and more modern solutions like transformer-based NLP models.
By the end of the course, you will have a strong understanding of how algorithms behave in production and what MLOps Engineers can do to support them. You will also see how model choices affect infrastructure, CI/CD, monitoring, and cost. This course is part of the full MLOps Specialization and connects directly to topics like Observability, Model Lifecycle, and LLMOps.
The goal is simple – to help you feel confident when working with ML models in real systems. Whether you’re part of a small startup or a large team, this course will show you how to support models in a smart, stable, and scalable way.
Start learning high demand tech skills today
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.
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