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City	Date	Duration	Price	Status
TunisTunis	September 1, 2026	21 days	€9,900	Confirmed date
SingaporeSingapore	September 10, 2026	21 days	€20,500	Confirmed date
GenevaSwitzerland	September 13, 2026	21 days	€10,780	Confirmed date
ParisFrance	September 19, 2026	21 days	€20,500	Confirmed date
LondonUnited Kingdom	September 24, 2026	21 days	£13,900	Confirmed date
IstanbulTurkey	September 26, 2026	21 days	€11,680	Confirmed date
AmsterdamNetherlands	September 27, 2026	21 days	€11,960	Confirmed date
Kuala LumpurMalaysia	September 28, 2026	21 days	€11,960	Confirmed date
DubaiUnited Arab Emirates	October 5, 2026	21 days	€13,900	Confirmed date
OnlineOnline	October 11, 2026	21 days	€9,990	Confirmed date
LisbonPortugal	November 26, 2026	21 days	€20,500	Confirmed date

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Course Information

Duration

3 Weeks

INTRODUCTION

Oil and gas organizations generate enormous volumes of production, subsurface, maintenance, sensor, operational, and unstructured document data. Creating business value from this information requires considerably more than building isolated analytical models or dashboards. Modern organizations need professionals who understand how data platforms, machine learning pipelines, distributed processing, generative models, and operational controls work together. This three-week program provides an integrated pathway from foundational architecture to production-ready artificial intelligence applications. The first week develops the capabilities required to build, monitor, govern, and continuously improve production machine learning systems. The second week focuses on distributed storage, processing, querying, and large-scale data engineering using modern big data architectures. The third week develops generative artificial intelligence applications using retrieval, vector search, intelligent agents, evaluation, guardrails, and operational monitoring. Participants progressively connect the outputs of each week into increasingly sophisticated oil and gas applications. The program ultimately enables technical professionals to bridge the gap between experimentation and dependable enterprise-scale deployment.

COURSE OBJECTIVES

Participants will achieve the following objectives by this course:

Design production-grade machine learning architectures for real oil and gas business problems.
Build structured data, feature engineering, training, inference, and monitoring pipelines.
Manage experiments, feature stores, model registries, deployment, and lifecycle governance.
Detect data drift and model degradation using quantitative monitoring techniques.
Understand distributed storage, computing, processing, and big data architecture principles.
Build data processing workflows using distributed query and computing technologies.
Develop scalable data pipelines for high-volume industrial and field datasets.
Build retrieval-augmented generative artificial intelligence systems grounded in organizational information.
Develop intelligent agents using controlled tool access, approval gates, and audit mechanisms.
Integrate machine learning, big data, and generative intelligence into governed production solutions.

TARGET AUDIENCE

This program targets a professional audience seeking to improve knowledge and skills:

Data scientists moving from exploratory models toward reliable production machine learning systems.
Data analysts supporting advanced analytics and operational intelligence across oil and gas organizations.
Reservoir engineers applying data-driven techniques to subsurface and production decision-making.
Production engineers developing predictive monitoring, optimization, and equipment reliability solutions.
Geoscientists working with large geological, seismic, reservoir, and operational datasets.
Data engineers designing scalable storage, processing, integration, and analytics pipelines.
Information technology professionals supporting enterprise data and artificial intelligence platforms.
Digital transformation specialists delivering advanced analytics and intelligent automation initiatives.
Technical team leaders evaluating machine learning, big data, and generative intelligence investments.
Engineering professionals seeking practical experience with modern production data systems.

COURSE OUTLINE

Day 1: Production Machine Learning Architecture and MLOps Foundations

Understanding model development versus production machine learning operations.
Examining continuous responsibilities across production machine learning environments.
Mapping components of a production machine learning architecture.
Translating business problems into technical system requirements.
Identifying data, pipeline, platform, and operations layers.
Evaluating technology choices for each architectural component.
Mapping architecture to an oil and gas equipment use case.
Developing a production architecture decision worksheet.
Comparing proposed architecture with reference implementation patterns.

Day 2: Databases, Streaming, and Data Processing

Comparing relational, column-oriented, vector, and key-value databases.
Understanding indexed storage and query performance principles.
Comparing streaming and batch processing architectures.
Designing structured storage for industrial sensor data.
Building an indexed operational data repository.
Simulating real-time production alerts using streaming concepts.
Creating batch extraction and transformation workflows.
Aggregating high-frequency measurements into operational features.
Evaluating processing choices against field requirements.

Day 3: Machine Learning Pipelines and Production Engineering

Understanding machine learning pipelines as directed workflows.
Applying feature, training, and inference pipeline conventions.
Structuring preprocessing components for repeatable execution.
Developing modular feature engineering pipeline classes.
Building reproducible model training workflows.
Creating production-ready inference processes.
Training an equipment failure prediction model.
Evaluating prediction performance across operational scenarios.
Applying configuration-driven pipeline development practices.

Day 4: Feature Stores, Experiment Tracking, and Model Registry

Understanding offline and online feature store architectures.
Preventing inconsistencies between training and production features.
Establishing disciplined experiment tracking processes.
Comparing multiple model training experiments systematically.
Recording parameters, metrics, and model artifacts.
Understanding model registry lifecycle management.
Registering validated models for controlled deployment.
Applying safe model promotion and versioning mechanisms.
Managing production model governance and traceability.

Day 5: Data Drift, Monitoring, Testing, and Continuous Deployment

Understanding model degradation in changing operating environments.
Distinguishing data drift from concept drift.
Measuring distribution changes using stability indicators.
Detecting drift across incoming operational data.
Building automated tests for machine learning pipelines.
Designing continuous integration workflows for model systems.
Comparing machine learning deployment with traditional software deployment.
Monitoring production performance and triggering operational responses.
Completing a production machine learning monitoring workflow.

Day 6: Big Data Foundations and Distributed Computing

Defining big data through volume, velocity, variety, veracity, and value.
Understanding data workflows across federated industrial environments.
Identifying limitations of single-machine data processing.
Comparing linear and parallel computing approaches.
Examining distributed storage and processing principles.
Reviewing major big data ecosystem components.
Benchmarking sequential and parallel processing performance.
Designing a field-scale industrial data pipeline.
Determining when distributed infrastructure is genuinely required.

Day 7: Distributed Storage and Hadoop Architecture

Understanding distributed storage principles and architecture.
Examining cluster resource management and workload coordination.
Understanding distributed file system design and replication.
Examining node roles within distributed storage environments.
Understanding block storage and fault tolerance mechanisms.
Designing partitioned storage for large field datasets.
Building a local distributed-style data lake structure.
Practicing distributed file system command workflows.
Evaluating storage resilience and scalability requirements.

Day 8: Distributed Processing with MapReduce

Understanding the map, shuffle, and reduce processing model.
Breaking large computational tasks into distributed operations.
Designing mapper logic for industrial datasets.
Designing reducer logic for aggregated outputs.
Implementing a complete distributed processing workflow.
Validating distributed results against conventional calculations.
Evaluating performance limitations of traditional distributed processing.
Comparing batch processing approaches with modern alternatives.
Identifying appropriate use cases for distributed batch computing.

Day 9: Distributed Query Languages and Data Models

Understanding distributed query architecture and data warehouses.
Writing structured queries over partitioned datasets.
Creating distributed tables and data definitions.
Exploring scripting approaches for large-scale data transformation.
Understanding column-oriented nonrelational data models.
Designing keys and access patterns for industrial datasets.
Comparing distributed query and storage technologies.
Selecting appropriate technologies for operational scenarios.
Validating query results across partitioned field data.

Day 10: Apache Spark and Scalable Data Engineering

Understanding distributed computing drivers and worker processes.
Comparing resilient datasets with structured data frames.
Applying distributed structured query processing.
Comparing modern distributed computing with traditional batch processing.
Building distributed transformations, joins, and aggregations.
Applying window functions to engineering data.
Diagnosing data skew and repartitioning issues.
Building structured streaming workflows for incoming data.
Evaluating managed distributed analytics platforms.

Day 11: Generative AI and Large Language Model Foundations

Understanding large language model architecture at a practical level.
Examining tokens, context windows, and model limitations.
Comparing major commercial and open model families.
Applying zero-shot and few-shot prompting techniques.
Developing structured prompts for technical domain tasks.
Understanding generation cost, latency, and usage limitations.
Connecting applications to language model interfaces.
Comparing prompting strategies using identical operational tasks.
Evaluating generated technical summaries against defined criteria.

Day 12: Retrieval-Augmented Generation for Industrial Knowledge

Understanding embeddings and vector representations.
Examining vector search and similarity retrieval.
Comparing vector storage technologies and deployment choices.
Designing document chunking strategies for technical materials.
Indexing operational and engineering documentation.
Retrieving relevant evidence for user questions.
Combining retrieval with grounded language generation.
Measuring retrieval hit rate and relevance.
Optimizing retrieval settings for industrial knowledge bases.

Day 13: Agentic AI and Controlled Tool Use

Understanding intelligent agent architectures and tool calling.
Applying iterative reasoning and action patterns.
Designing multi-step workflows for operational tasks.
Connecting agents to production data sources.
Developing controlled database query tools.
Applying threshold-based anomaly detection within agent workflows.
Implementing human approval before recommended actions.
Creating comprehensive audit logs for agent activity.
Applying safety controls to autonomous capabilities.

Day 14: Production Generative AI and LLMOps

Managing prompts through structured version control.
Building evaluation frameworks for generative systems.
Measuring response relevance, grounding, and reliability.
Monitoring language model cost and latency.
Applying caching strategies to reduce operational expense.
Designing safety guardrails for enterprise use.
Monitoring production generative intelligence applications.
Understanding standardized model integration protocols.
Building evaluation and operational monitoring workflows.

Day 15: Integrated Oil and Gas AI Capstone

Combining machine learning, retrieval, and agent capabilities.
Connecting generative intelligence with production monitoring systems.
Retrieving relevant field and operational documentation.
Querying structured production information through controlled tools.
Generating grounded responses to operational questions.
Applying human approval gates to recommended actions.
Logging model retrievals, tool calls, outputs, and decisions.
Evaluating system quality, safety, cost, and latency.
Presenting an integrated production artificial intelligence solution.

COURSE DURATION

Duration: 3 Weeks

This intensive professional program is delivered over three consecutive training weeks comprising fifteen full training days, with each day combining theory, practical implementation, and hands-on laboratory activities. The three weeks cover production machine learning operations, big data and distributed processing, and generative artificial intelligence with retrieval and intelligent agents. Participants progressively build interconnected technical capabilities and complete practical outputs throughout the program, culminating in an integrated oil and gas artificial intelligence solution. The source curriculum specifies five days per track and fifteen days for the complete three-track journey.

INSTRUCTOR INFORMATION

The program is delivered by an internationally certified expert with extensive practical and consulting experience in artificial intelligence, machine learning operations, big data engineering, distributed computing, generative artificial intelligence, data platforms, industrial analytics, digital transformation, and production deployment within complex technical environments.

FREQUENTLY ASKED QUESTIONS

It is designed for data, engineering, information technology, analytics, and digital transformation professionals.

CONCLUSION

Production AI, MLOps, Big Data and Generative AI for Oil & Gas provides an integrated pathway from data engineering and machine learning deployment to advanced generative intelligence. Participants develop practical capabilities across production machine learning, distributed computing, retrieval, intelligent agents, governance, monitoring, and automation. The three-week structure enables progressive learning while connecting each technical discipline to realistic oil and gas applications. Hands-on laboratories ensure that participants move beyond conceptual understanding toward building executable and operationally relevant systems. Upon completion, professionals will be prepared to contribute to enterprise-scale data and artificial intelligence transformation initiatives.

Business & Management

Technical Courses

Netherlands

United Arab Emirates

Switzerland

Production AI, MLOps, Big Data and Generative AI for Oil & Gas