Reservoir Engineering Courses

Board Infinity

Agent Foundations and Prompt Engineering

Design AI agents with LLMs and craft prompts using few-shot, chain-of-thought, and role patterns, then chain them into Python workflows for automated research and summarization.

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

Coursera

Geospatial Data Engineering

Build end-to-end geospatial data pipelines: launch AWS EC2 and GDAL raster environments, automate PostGIS ETL with Airflow, stream GPS over MQTT, and analyze NetCDF climate data.

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

Packt

Build with LLMs: Prompt Engineering & Real AI Projects

Learn tokenization, log probabilities, and context windows, then apply few-shot and chain-of-thought prompting to build an AI-powered Git commit message generator.

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

Edureka

Data Engineering Workflow Orchestration with Airflow

Build production-ready data pipelines with Apache Airflow: design DAGs, use operators, sensors and the TaskFlow API, and add retries, monitoring, and CI/CD deployment.

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

Packt

Prompt Engineering Foundations

Build AI-powered tools by calling OpenAI and Anthropic APIs, using the OpenAI Python library, managing API keys, costs and model parameters, and simplifying calls with LiteLLM.

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

LearnKartS

Prompt Engineering for ChatGPT: Beginner to Advanced

The prompt, not the chatbot, sets the quality: write ChatGPT prompts using priming, root prompts, Chain-of-Thought, Few-Shot, and Persona and Recipe patterns.

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

Edureka

Observability Engineering: Metrics, Logs, and Traces

Instrument distributed systems with metrics, logs, and traces: collect telemetry using Prometheus and PromQL, build Grafana dashboards and alerts, and trace requests with OpenTelemetry and Jaeger.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Coursera

Advancing Your Career in AI and Machine Learning Engineering

Explore machine learning engineer roles and MLOps career paths, then present projects as portfolio-ready artifacts, highlight technical contributions on a resume, and prepare for technical interviews.

• Coursera
• 14 minutes
• Self-Paced
• Paid Course

Coursera

Advancing Your Career in Computer Vision Engineering

Turn computer vision projects such as inference pipelines, detection KPIs, and segmentation outputs into portfolio artifacts, resume-ready achievements, and clear interview explanations of engineering trade-offs.

• Coursera
• 33 minutes
• Self-Paced
• Paid Course

Coursera

Career Development For Open Source Data Engineering

Turn hands-on Airflow, dbt, and Spark experience into a GitHub portfolio, resume, and LinkedIn profile, rehearse interviews, and build a 30-day job-search plan.

• Coursera
• 1 hour 45 minutes
• Self-Paced
• Paid Course

Coursera

Data Engineering & Pipeline Reliability for Machine Learning

Turn messy datasets into model-ready feature stores: encode categoricals by cardinality, validate with Great Expectations, build ETL/ELT pipelines, and track schema drift and SLA metrics.

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

Coursera

Data Modeling & Warehousing Fundamentals in Data Engineering

Design star schema data warehouses: model dimensions, normalize to Third Normal Form with indexing, write SQL window functions, configure streaming replication, and build MERGE-based incremental loads.

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

Coursera

DevOps and CI/CD for Data Engineering Performance

Build a DevOps workflow for data systems: resolve Git merge conflicts, containerize with multi-stage Dockerfiles, automate servers with Ansible, deploy CI/CD pipelines, and tune query performance.

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

Coursera

Production ML Engineering: Packaging, APIs, and Testing

Turn ML models into production services: build reusable Python packages, serve models through APIs, apply CI/CD and version control, write developer docs, and automate pipeline tests.

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

Foundations of Site Reliability Engineering Training

Implement SRE practices: define SLIs, SLOs and error budgets, build Prometheus and Grafana observability, run blameless RCA, and test resilience with chaos engineering on Kubernetes.

• Coursera
• 7 weeks, 3 hours a week
• Self-Paced
• Paid Course