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.

Machine learning systems fail in ways that traditional software does not—data changes, schema mismatches, and model assumptions all create unique bugs. This course teaches you how to trace, fix, and validate these issues using a structured debugging workflow. You’ll write targeted unit tests, interpret stack traces and logs, patch defects, and confirm resolutions through regression testing. Each lesson includes concise videos, practical readings, hands-on work, and a realistic ungraded lab. By the end, you’ll know how to diagnose ML failures quickly, prevent regressions, communicate your fixes clearly, and build more reliable ML codebases.

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 teaches you how to design, evaluate, and operate reliable machine learning data pipelines in production. You’ll learn how daily ETL and ELT pipelines feed feature stores, how orchestration supports reproducible feature engineering, how to handle upstream schema changes without breaking downstream systems, and how to evaluate pipeline health using freshness, lag, and SLA metrics. Designed for data engineers, analytics engineers, and ML practitioners, the course builds job-ready judgment for delivering timely, trustworthy, and resilient data to ML systems.

This course teaches you how to build a fully automated machine learning pipeline using scikit-learn. You will learn to scale numeric features, encode categorical variables, train a logistic model, and optimize it using GridSearchCV. The course then guides you in packaging the workflow as a reusable module that fits real-world ML engineering and MLOps practices. Through concise videos, structured readings, two 15-minute Coach interactions, a combined 25-minute hands-on activity, and a 45-minute ungraded lab, you will practice constructing and refining an end-to-end pipeline. By the end, you will have a polished, automated workflow you can reuse, adapt, and integrate into your ML projects or production systems.

In real-world machine learning work, building a model is only half the job. Knowing how to evaluate it, explain its weaknesses, and defend improvements is what makes your work trustworthy. In this course, you will learn how to evaluate regression and classification models using the right metrics, diagnose where models systematically fail, and determine whether performance differences actually matter.

You will practice selecting RMSE and MAE for reporting housing-price models, analyzing confusion matrices to uncover false-positive patterns in spam filters, and using bootstrapping to test whether AUC improvements are statistically significant. Through short videos, guided coaching conversations, hands-on activities, and an ungraded lab, you will build confidence in interpreting model performance the way it is done on real teams. By the end of the course, you will be able to justify your evaluation choices and make evidence-based model decisions.

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.

Take your machine learning skills beyond the notebook and into production. In this short, practical course, you’ll learn how to turn trained models into reliable RESTful inference services, automate deployment pipelines, and monitor real-time performance like a professional MLOps engineer. You’ll build a /predict API using FastAPI, integrate it with GitHub Actions for CI/CD, and then simulate traffic with Locust to evaluate latency and optimize for a 100 ms SLA target.

Whether you’re an aspiring MLOps engineer or a data scientist ready to bridge into deployment, this course gives you the hands-on confidence to deliver production-grade ML services that scale. You’ll strengthen the technical and analytical skills that modern AI teams need — automation, performance optimization, and service reliability — to stay competitive in the evolving ML operations landscape. By the end, you’ll not only deploy your own model confidently but also gain the credibility to manage real-world ML systems end-to-end.

Containerization is more than a deployment tool—it’s the backbone of reliable, scalable machine learning systems. In this intermediate-level course, you’ll learn how to package, deploy, and manage ML models using Docker and Kubernetes. You’ll start by exploring why containerization matters—how it ensures reproducibility and stability across environments. Then, you’ll move into orchestration, learning how Kubernetes automates deployment, scaling, and monitoring for real-world applications.

Through concise videos, guided readings, and a hands-on project, you’ll write a Dockerfile, publish your image to an internal registry, and deploy it to a cluster using a Kubernetes configuration file. You’ll also practice testing and reflecting on your deployment process to strengthen your operational mindset. By the end, you’ll be able to build, deploy, and manage containerized ML applications confidently—skills essential for engineers, data scientists, and anyone bringing AI models into production.

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 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 intermediate-level course is designed for machine learning engineers, data scientists, and ML Ops practitioners who are responsible for releasing and maintaining models in production. Building a model is only the beginning. To deliver reliable business value, models must be validated on unseen data, compared against baselines in live environments, and continuously monitored for drift.

In this course, The learner will learn how to validate release candidates using hold-out datasets, analyze A/B test and shadow deployment results to quantify performance improvements, and monitor data and prediction drift using practical indicators like PSI. Through short videos, guided coach conversations, and hands-on learning activities, I will practice decision-making that mirrors real production workflows. By the end, The learner will be ready to support safe model releases and long-term model health.

Integrate, Scale, and Monitor ML Microservices” is a hands-on course designed for learners who want to build reliable and scalable machine-learning services. You’ll explore how ML models fit into modern microservice architectures, learning to design clear service boundaries, integrate prediction services effectively, and choose communication patterns that improve resilience. The course also guides you through asynchronous workflows and scaling strategies used in real production systems. Finally, you’ll develop practical troubleshooting skills by interpreting logs, metrics, and distributed traces to diagnose performance issues. Through demos, reflective coach activities, and a realistic analysis project, you’ll gain the technical judgment and practical intuition needed to operate ML microservices with confidence.

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.