Machine Learning Pipelines Courses and Certifications

Edureka Career Certificate

MLOps: Build & Deploy ML Systems

Move models from experiment to production: track experiments, version data, automate CI/CD and model registries, scale training on Kubernetes and Kubeflow, and monitor deployed models for drift.

• Coursera
• 8 weeks, 5 hours a week
• Self-Paced
• Paid Course
• 3 courses

Coursera

Automate Financial Analysis with AI Pipelines

Compare Random Forest, XGBoost and neural network models for credit-risk classification with F1 and AUROC, then automate a pipeline that pulls SEC filings, retrains models and refreshes dashboards.

• Coursera
• 2 hours 11 minutes
• Self-Paced
• Paid Course

Coursera

RAG Systems and Production Operations

Build production-grade retrieval-augmented generation systems: implement Corrective, Self, and Agentic RAG patterns, secure vector databases with TLS and RBAC, tune latency, and migrate vectors between stores.

• Coursera
• 5 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Apply Test-Driven ML Code

Apply test-driven development to machine learning code: build modular components, test-driven DataLoader classes and training loops, comprehensive test suites, and CI/CD integration workflows.

• Coursera
• 2 weeks, 1 hour a week
• Self-Paced
• Paid Course

Edureka

Feature Engineering and Feature Stores for AI and ML

Engineer features for machine learning: apply scaling, encoding, and skewness handling, build Databricks Feature Stores, track datasets with MLflow, and automate pipelines with Databricks Jobs.

• Coursera
• 4 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Automate, Optimize, and Monitor ML Models

Keep production models from decaying: evaluate deployed model performance, detect and remediate drift, and build automated end-to-end ML pipelines with hyperparameter optimization.

• Coursera
• 2 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Build & Optimize TensorFlow ML Workflows

Build an end-to-end TensorFlow 2.x pipeline with tf.data, Keras and custom training loops, then optimize models via TensorFlow Lite post-training quantization and latency benchmarking for edge deployment.

• Coursera
• 2 hours 19 minutes
• Self-Paced
• Paid Course

Coursera

Build Predictive & Supervised Models

Build cross-validated random forest models to business accuracy targets, monitor model drift with statistical metrics, standardize pipelines across supervised algorithms, and assess feature selection methods.

• Coursera
• 4 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Building Vision and NLP Workflows with TensorFlow pipelines

Build computer vision and NLP pipelines with TensorFlow, Keras and transformers: tokenization, embeddings, ROUGE evaluation, tf.data workflows, and TensorFlow Lite quantization for deployment.

• Coursera
• 7 weeks, 1 hour a week
• Self-Paced
• Paid Course

Board Infinity

Applied Anomaly Detection with Machine Learning

Build anomaly detection systems for tabular and time-series data using Isolation Forest, autoencoders, PR-AUC evaluation, ensemble scoring, and FastAPI deployment.

• Coursera
• 2 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Choose Optimal Data Structures for ML

Implement arrays, hash maps, trees, heaps, graphs, tries, segment trees and sparse matrices in Java, benchmarking 2x-10x speedups in ML preprocessing and model-serving pipelines.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Parse & Normalize Data for ML Pipelines

Build Java preprocessing pipelines for ML: parse large CSV/TSV files with OpenCSV and Apache Commons CSV, apply Min-Max and Z-score normalization, and handle outliers and missing values.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Test & Debug Java ML Pipelines

Write JUnit 5 and Mockito tests for Java ML pipelines, debug flaky nondeterministic runs with fixed seeds, and automate coverage checks through GitHub Actions and Jenkins.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Packt

The AI Optimization Playbook

Optimize the enterprise AI lifecycle—from strategy and proof of concept to production, measurement, GenAI operations, and responsible governance—using practical frameworks and case studies.

• Coursera
• 16 weeks, 1 hour a week
• Self-Paced
• Paid Course

AI & Analytics Operations

Build MLOps pipelines using Azure ML, MLflow, GitHub Actions, and Azure Blob Storage to deploy, monitor, and optimize machine learning systems.

• Coursera
• 11 weeks, 1 hour a week
• Self-Paced
• Paid Course