Course

Machine Learning Engineering for Production (MLOps)

DeepLearning.AI

Learn to productionize machine learning with the Machine Learning Engineering for Production (MLOps) Specialization. This course, offered by DeepLearning.AI, provides comprehensive training in building and maintaining integrated systems for continuous production operation.

Through this specialization, you will delve into project scoping, data needs, modeling strategies, deployment requirements, and the establishment of model baselines. Gain insights into data pipeline construction, data lifecycle management, and best practices for maintaining and monitoring production systems.

Design an ML production system from end-to-end
Establish a model baseline and address concept drift
Build data pipelines and apply best practices for maintaining a production system
Understand machine learning modeling pipelines and deploy ML models in production
Learn progressive delivery techniques and model monitoring

Certificate Available ✔

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This Specialization covers the ML lifecycle, data lifecycle, modeling pipelines, and deploying ML models in production. Gain expertise in project scoping, data needs, modeling strategies, and deployment requirements.

Introduction to Machine Learning in Production

Identify the key components of the ML lifecycle and pipeline to compare the ML modeling iterative cycle with the ML product deployment cycle.

Understand the importance of performance on specific examples
Solve problems for structured, unstructured, small, and big data
Improve label consistency

Machine Learning Data Lifecycle in Production

Understand responsible data collection for building a fair ML production system and implement feature engineering, transformation, and selection using TensorFlow Extended. Leverage ML metadata and enterprise schemas to address rapidly evolving data.

Implement feature engineering and transformation with TensorFlow Extended
Leverage ML metadata and enterprise schemas for evolving data

Machine Learning Modeling Pipelines in Production

Apply techniques to manage modeling resources for serving batch and real-time inference requests. Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.

Manage modeling resources for serving batch and real-time inference requests
Address model fairness and explainability issues

Deploying Machine Learning Models in Production

Learn how to deploy ML models and make them available to end-users. Build scalable and reliable hardware infrastructure, implement workflow automation, and progressive delivery. Continuously monitor your system to detect model decay and remediate performance drops to keep your production system running.

Deploy ML models and make them available to end-users
Build scalable and reliable hardware infrastructure
Implement workflow automation and progressive delivery
Continuously monitor your system for model decay and performance drops

Course

Machine Learning Engineering for Production (MLOps)

Course Modules

Introduction to Machine Learning in Production

Machine Learning Data Lifecycle in Production

Machine Learning Modeling Pipelines in Production

Deploying Machine Learning Models in Production

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