MLOps: Machine Learning in Production

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand supervised and unsupervised learning
• Understand model training and evaluation

Planned outline shown above (sample metadata). The detailed module and lesson list for this course hasn't been published yet.

Description

Learn to build, deploy, and monitor ML systems at scale. Master Kubernetes, Airflow, MLflow, Prometheus, and CI/CD for AI to deliver reliable production ML systems.

What this course covers: MLOps: Machine Learning in Production works through supervised and unsupervised learning, model training and evaluation, feature engineering and data preparation and deep learning frameworks (TensorFlow / PyTorch).

Skills you will build: tuning model performance, building and evaluating ML models and preparing data for training.

Practical applications: prediction and classification systems, computer vision and NLP applications and AI features inside products.

Who should take this course: developers, data analysts and engineering students moving into machine learning and AI. It is designed for learners with solid experience in machine learning and AI who want advanced, in-depth practice.

Learning outcomes: by the end of the course you should be able to apply supervised and unsupervised learning and tuning model performance to your own work or projects, and know which areas to practise next.

Requirements

• Solid working knowledge of machine learning and AI
• Comfort with intermediate concepts
• A computer with internet access

Your instructor

• D
  Dev P. ★★★★★ 5.0/5 Sample review

  The section on feature engineering and data preparation was explained step by step, and the examples made deep learning frameworks (TensorFlow / PyTorch) much easier to follow. Loved that model evaluation got as much attention as model building.

• K
  Keerthi L. ★★★★★ 5.0/5 Sample review

  MLOps: Machine Learning in Production covered model training and evaluation in more depth than I expected. The pace was right for my level. The deployment section filled a gap most ML courses skip.

• F
  Farhan Q. ★★★★★ 5.0/5 Sample review

  Well organised. Revisiting the model training and evaluation part a second time really helped it click. The hands-on notebooks made backpropagation finally make sense to me.

Sample reviews are illustrative demo content, not verified purchases. Ratings from learners will appear here once submitted.