This short, hands-on course helps learners adapt and optimize deep learning models for real-world use. Learners begin by exploring how transfer learning accelerates model development when data is limited. Through guided practice, they fine-tune a pretrained model, adjust freezing and unfreezing strategies, and troubleshoot common training challenges. The course then shifts to evaluating model configurations for deployment, focusing on accuracy, latency, memory footprint, and efficiency. Learners experiment with optimization methods such as hyperparameter tuning and quantization, compare multiple model setups, and make evidence-based recommendations for production environments. By the end, learners can confidently balance accuracy and performance constraints to choose the right model for their needs.

This short course helps you build and validate ML-ready data pipelines with confidence. You’ll start by learning how to design ETL workflows that ingest, clean, and partition large datasets using tools like Airflow and Spark. You’ll see how real teams manage click-stream logs, handle nulls, and prepare partitioned training data at scale. Next, you’ll evaluate data quality, governance, and lineage so your pipelines remain trustworthy and reproducible. You’ll work with practical techniques like schema drift checks, expectations suites, and audit-ready lineage records. Through short videos, applied readings, hands-on practice, and a final graded assessment, you’ll walk away knowing how to engineer reliable pipelines and validate them for production use.

This course helps learners transform scattered AI preprocessing code into clean, reusable, and testable Python utilities that meet modern MLOps expectations. Across two focused lessons, learners explore advanced programming constructs—such as generators, decorators, and structured logging—that make ML workflows modular and maintainable. They then apply software-engineering principles to design standards-compliant Python packages that integrate smoothly into real AI pipelines. Through videos, readings, hands-on exercises, and a guided Coursera Lab, learners practice refactoring preprocessing steps, structuring packages using current Python packaging standards, managing dependencies, and writing unit tests with pytest. By the end of the course, learners will have the skills to build and test a functional Python package suitable for internal PyPI publishing and production-ready machine learning work.

This short course helps you deploy and optimize scalable machine learning workloads in the cloud using managed AI services. You’ll start by learning how distributed training jobs work on platforms like Amazon SageMaker. Then you’ll configure training pipelines using Spot Instances and autoscaling features, gaining hands-on experience with real-world deployment patterns. Finally, you’ll dig into monitoring and optimization: reading GPU utilization logs, exploring CloudWatch metrics, and making recommendations that balance performance and cost. By the end, you will know how to right-size an ML workload, select efficient instance families, and justify architecture changes based on data.

This intermediate course teaches you how to design scalable, reliable AI systems that work in real-world production environments. You’ll learn how to build end-to-end architectures that meet throughput, latency, and fault-tolerance goals, and you’ll move from conceptual design to detailed component diagrams and interface specifications. Using industry patterns adopted by modern ML teams, you’ll practice estimating QPS, defining autoscaling rules for the inference layer, structuring data flow between the feature store and model API, and instrumenting your system with a monitoring stack. By the end of the course, you will have created a complete architecture document—including diagrams and interface definitions—that engineering teams can use to implement a scalable AI product.

"Integrate and Optimize AI Services Seamlessly" is an applied, intermediate-level course designed for engineers and ML practitioners who want to build reliable, production-ready AI systems. Across focused, hands-on lessons, the course explores how real-world services communicate using APIs, message queues, and structured serialization formats. Learners gain practical experience integrating prediction services with gRPC and protobuf, improving consistency, performance, and cross-language compatibility. The course also guides participants through deployment health essentials, including interpreting Prometheus metrics, spotting early warning signs during canary releases, and making safe decisions to stabilize or roll back new versions. Through real scenarios, interactive activities, and expert-led demos, students develop the confidence to ship AI services that are fast, resilient, and operationally sound in modern distributed environments.