The convergence of artificial intelligence and data engineering is redefining how organizations build, manage, and optimize their data pipelines. Traditional pipelines focus on ingestion, transformation, and storage, but modern enterprises now require pipelines that are designed to support machine learning (ML) and deep learning (DL) workloads at scale. This course provides a comprehensive exploration of AI-powered data engineering, equipping participants with the skills to design, orchestrate, and deploy intelligent data pipelines tailored for ML and DL applications.
Participants will gain deep insights into the integration of data engineering with artificial intelligence frameworks. They will learn how to architect robust systems that feed ML models with clean, timely, and high-quality data. The course emphasizes the importance of data preparation, feature engineering, model deployment, and continuous integration of AI workflows into enterprise ecosystems.
The training also covers advanced tools and platforms used in building AI-driven pipelines, including TensorFlow Extended (TFX), MLflow, Apache Airflow, Kubeflow, and cloud-native AI services from AWS, Azure, and Google Cloud. By combining traditional engineering approaches with modern MLOps strategies, learners will be able to build end-to-end pipelines that enable rapid experimentation and scalable deployment of AI solutions.
Practical, hands-on exercises form the core of this program. Participants will build AI-ready pipelines capable of handling structured, semi-structured, and unstructured data, integrating seamlessly with both cloud and on-premise environments. Real-world use cases ranging from predictive analytics and computer vision to NLP and recommendation engines will illustrate how AI pipelines drive business transformation.
Beyond technical design, the course addresses governance, compliance, and ethical AI, ensuring learners understand how to deliver pipelines that are not only efficient and scalable but also transparent and responsible. This balanced perspective empowers participants to implement solutions aligned with both technical excellence and organizational strategy.
By the end of the course, learners will be equipped to design AI-powered pipelines that streamline ML and DL workflows, reduce operational overhead, and accelerate innovation in industries such as finance, healthcare, manufacturing, and e-commerce.
Who Should Attend
- Data Engineers seeking to build pipelines optimized for ML and DL workflows.
- Machine Learning Engineers integrating data pipelines into model lifecycles.
- Data Scientists aiming to automate feature engineering and model deployment.
- Cloud Engineers managing AI-driven workloads on AWS, Azure, and GCP.
- AI Architects designing end-to-end intelligent systems.
- DevOps/MLOps professionals implementing CI/CD for ML pipelines.
- IT Managers overseeing enterprise AI strategies.
- Software Developers transitioning into AI and data engineering roles.
- Business Intelligence professionals seeking AI integration with analytics.
- Professionals preparing for certifications in AI and MLOps.
Duration
10 days
Course Objectives
By the end of this training, participants will be able to:
- Understand the principles of AI-powered data pipelines for ML and DL.
- Design and implement pipelines that support data ingestion, transformation, and model training.
- Automate feature engineering and preparation for machine learning models.
- Integrate data pipelines with AI frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Orchestrate AI pipelines using tools like Apache Airflow, Kubeflow, and MLflow.
- Deploy models into production environments with continuous monitoring.
- Implement MLOps practices for scalable, reliable, and automated AI workflows.
- Optimize performance and resource allocation in AI-driven pipelines.
- Manage unstructured data (images, video, text) for deep learning applications.
- Apply governance, compliance, and ethical AI principles in pipeline design.
- Enable real-time AI pipelines using streaming technologies like Kafka and Flink.
- Prepare for AI and MLOps certifications through hands-on project work.
Comprehensive Course Outline
Module 1: Introduction to AI-Powered Data Engineering
- Evolution from traditional data pipelines to AI pipelines.
- Role of data engineering in ML and DL ecosystems.
- Key challenges in AI data workflows.
- Overview of AI-driven data tools and frameworks.
Module 2: Data Ingestion for AI Pipelines
- Ingesting structured and unstructured data.
- Streaming vs. batch ingestion for ML workloads.
- Integration with IoT, APIs, and real-time data sources.
- Hands-on with Kafka and cloud-native ingestion tools.
Module 3: Data Preparation and Feature Engineering
- Data cleaning and preprocessing for ML and DL.
- Automated feature extraction and transformation.
- Tools for feature stores and feature pipelines.
- Handling imbalanced and sparse data.
Module 4: AI Model Training Pipelines
- Designing end-to-end training pipelines.
- TensorFlow Extended (TFX) and Kubeflow pipelines.
- Distributed training with GPUs and TPUs.
- Case study: automated ML pipeline for NLP.
Module 5: Deep Learning Data Workflows
- Preparing image, video, and text data for DL.
- Augmentation and preprocessing strategies.
- Specialized DL pipelines for computer vision and NLP.
- Integrating with PyTorch and TensorFlow models.
Module 6: Orchestration of AI Pipelines
- Workflow orchestration with Airflow and Kubeflow.
- MLflow for experiment tracking and model management.
- Managing dependencies and pipeline DAGs.
- Scheduling and monitoring AI workflows.
Module 7: Real-Time AI Pipelines
- Streaming AI with Apache Flink and Kafka Streams.
- Real-time feature engineering for ML predictions.
- Low-latency pipelines for fraud detection and IoT.
- Hands-on project: real-time recommendation engine.
Module 8: Model Deployment and Serving
- Deploying models with TensorFlow Serving and TorchServe.
- Containerization with Docker and Kubernetes.
- CI/CD pipelines for AI applications.
- Scaling model serving in production.
Module 9: Monitoring and Model Drift Management
- Monitoring pipeline performance and model accuracy.
- Detecting and managing model drift.
- Retraining strategies for evolving datasets.
- Logging and alerting for AI pipelines.
Module 10: MLOps and Continuous Integration
- Principles of MLOps for production-ready AI.
- Automating retraining and redeployment.
- Collaboration between data scientists and engineers.
- Tools for continuous delivery of AI models.
Module 11: Governance, Compliance, and Ethical AI
- Implementing transparency and auditability in AI pipelines.
- Bias detection and fairness in ML workflows.
- Compliance with GDPR, HIPAA, and ISO standards.
- Ethical challenges in AI-driven data systems.
Module 12: Multi-Cloud and Hybrid AI Pipelines
- Deploying AI pipelines across AWS, Azure, and GCP.
- Hybrid cloud strategies for AI workloads.
- Interoperability challenges and solutions.
- Case study: enterprise hybrid AI pipeline.
Module 13: Advanced AI Pipeline Optimization
- GPU/TPU optimization for large-scale workloads.
- Cost-efficient scaling of AI systems.
- Data caching and performance tuning.
- Optimizing pipeline resource management.
Module 14: AI Integration with Business Intelligence
- Connecting AI pipelines to BI dashboards.
- Augmented analytics with AI-driven insights.
- Real-time decision support systems.
- Case study: AI in predictive business analytics.
Module 15: Industry Case Studies
- AI in healthcare: diagnostic imaging pipelines.
- AI in finance: fraud detection workflows.
- AI in retail: recommendation engines.
- AI in manufacturing: predictive maintenance systems.
Module 16: Project and Certification Preparation
- Building an AI-powered data pipeline from ingestion to deployment.
- Peer collaboration and review.
- Certification exam preparation with practice tests.
- Future roadmap for AI-powered data engineering careers.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.
Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment
Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
