AI/ML Systems Engineering Training

Duration: 8 hours

Alpinum’s AI/ML Systems Engineering Training helps engineers move beyond basic model development and understand how AI/ML systems are built, optimised and integrated into practical engineering workflows. The course is designed for software engineers, systems engineers, AI/ML engineers and technical teams working with real-time data, inference pipelines and hardware acceleration.

This training combines Python-based machine learning workflows with performance-focused implementation using C++, Rust and GPU acceleration concepts.

Who Should Attend?

This course is suitable for:

• • Engineers developing AI/ML-enabled systems

• • Software teams moving into machine learning engineering

• • Systems engineers integrating AI inference into production workflows

• • Technical teams working with computer vision, telemetry or real-time analytics

What the Course Covers

Participants learn how AI/ML workflows are built, accelerated and integrated into real engineering systems.

Key topics include:

• • NumPy and Pandas

• • Scikit-Learn workflows

• • PyTorch or TensorFlow

• • OpenCV for computer vision

• • CUDA and TensorRT concepts

• • C++/Rust acceleration

• • AI infrastructure and inference coordination

Practical Labs

The course includes labs on Python data science and deep learning, as well as high-performance AI acceleration. Participants work with data preparation, model training, computer vision processing, GPU-accelerated inference and infrastructure concepts.

Lab 1: Python Data Science and Deep Learning

Learning Objectives: Master data manipulation, preprocessing, and model development using industry-standard Python ecosystems.

Tasks:

• • Utilize NumPy and Pandas to clean and engineer feature sets from raw datasets.

• • Implement machine learning workflows using Scikit-Learn (e.g., classification, regression).

• • Develop and train neural network models using PyTorch or TensorFlow frameworks.

Extension Tasks:

• • Optimize model performance using hyperparameter tuning and cross-validation techniques.

• • Containerize the training environment to ensure reproducibility.

Lab 2: High-Performance AI Acceleration (C++/Rust)

Learning Objectives: Optimize AI inference using hardware acceleration, computer vision libraries, and distributed infrastructure.

Tasks:

• • Deploy computer vision algorithms using OpenCV for real-time image preprocessing.

• • Implement GPU-accelerated inference pipelines using CUDA and TensorRT for optimized throughput.

• • Develop robust AI infrastructure in Rust for telemetry monitoring and distributed inference coordination.

Extension Tasks:

• • Benchmark inference latency between standard CPU execution and TensorRT-optimized GPU execution.

• • Implement a distributed inference service to handle concurrent requests across multiple nodes.

Project:

The course can include a Real-Time Object Detection Pipeline project, combining video preprocessing, inference execution and real-time telemetry.

Learning Objectives: Integrate hardware-accelerated inference, data processing, and vision libraries into a coherent pipeline.

Description: Develop an end-to-end “Real-time Object Detection Pipeline” capable of processing high-resolution video streams for classification and localization.

Tasks:

• • Preprocessing: Use OpenCV and NumPy to prepare video frames for model ingestion.

• • Inference: Execute a detection model via TensorRT/CUDA for real-time performance.

• • Analytics: Process detection outputs using Rust-based infrastructure for real-time telemetry and logging.

Coverage Matrix

Assessment

• • Hands-on Exercises: Interactive labs provided for each technology stack.

• • Quizzes: Knowledge checks integrated throughout the module to validate understanding of AI systems engineering concepts.

Learning Outcomes

By the end of the course, participants will understand AI/ML system components, data preparation, model training workflows, inference acceleration and practical deployment considerations.

AI/ML Systems Engineering Training FAQs