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 you break down complex ML systems into clear, reusable parts and communicate them using practical abstractions. You’ll learn how to separate ingestion, feature serving, inference APIs, and monitoring components while creating flowcharts and pseudocode that guide implementation. Using examples such as real-time fraud detection and feature store workflows, you’ll practice decomposing systems and designing abstractions engineers depend on. Through short videos, readings, hands-on practice, a coach-guided reflection, and a 45-minute ungraded lab, you’ll build skills used across ML engineering and MLOps roles. By the end, you’ll be able to confidently analyze ML systems and produce artifacts that support scaling, clarity, and production readiness.

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 intermediate-level course is designed for machine learning engineers and developers who want to move beyond experiments and ship reliable ML systems. Learners will learn how to apply core MLOps practices such as version control, pull requests, and CI/CD pipelines to keep an ML codebase healthy and production-ready. Learners will also design modular software components and build a FastAPI microservice that serves a transformer model through a clean, well-defined API.

Through short videos, guided coaching conversations, hands-on learning activities, and an ungraded lab, Learners will practice real workflows used by ML teams in industry. By the end of the course, Learners will be able to confidently collaborate on ML codebases, pass automated quality checks, and deploy machine learning models behind scalable APIs.

Document AI: Project & API Writing teaches you how to communicate AI systems with clarity, structure, and precision - skills that are essential for ML engineering in real organizations. In this course, you’ll learn to document model architectures, data schemas, training procedures, and evaluation summaries in ways that support onboarding, debugging, and reproducibility. You’ll also create developer-facing API documentation with request and response schemas, examples, error behaviors, and usage notes. Through hands-on practice and a full MkDocs documentation lab, you’ll build a complete, developer-ready documentation site for a prediction API. By the end, you’ll be able to turn raw ML projects into professional, discoverable, and maintainable technical documentation that teams rely on.

Machine learning systems shift over time, making structured testing essential. In this short course, you’ll learn how to evaluate ML pipelines using unit, integration, and smoke tests and how to detect data drift across critical features. You will also create automated regression test suites that compare new model outputs to golden datasets, helping you catch degradation early and deploy reliably. Through concise videos, readings, hands-on practice, and guided coaching, you’ll define meaningful ML test cases and configure nightly pytest suites. By the end, you will have a practical, reusable testing framework you can apply directly to real-world ML pipelines.

This short course helps you build and optimize machine learning workflows using TensorFlow 2.x. You’ll start by structuring an end-to-end pipeline that includes data ingestion with tf.data, model definition with Keras, and custom training with checkpointing for reliability. You’ll then learn how to optimize your models for deployment using TensorFlow Lite, including post-training quantization and latency benchmarking. Along the way, you’ll see how ML engineers measure performance, evaluate tradeoffs, and deploy models to mobile and edge devices. Through hands-on practice and real-world examples, you’ll learn to think like an applied ML practitioner who builds efficient, production-ready TensorFlow systems.