Training > AI/Machine Learning > Rust for Machine Learning Operations (LFWS306)

INSTRUCTOR-LED COURSE

Position yourself for high-impact roles in MLOps and ML platform engineering by building, optimizing, and deploying end-to-end ML pipelines in Rust. Through Rust-based MLOps workflows, you’ll move beyond Python-centric systems to deliver the performance, reliability, and deployment simplicity production ML requires.

Who Is It For

ML engineers, MLOps engineers, backend developers, and platform engineers who want to take ownership of production ML systems. Ideal for professionals ready to move beyond Python-centric workflows and build faster, more reliable ML pipelines in Rust.

read less read more

What You’ll Learn

Develop the skills to build high-performance ML pipelines in Rust using the PAIML stack. You’ll apply SIMD-accelerated tensors, pipeline orchestration, experiment tracking, and model deployment techniques to deliver faster inference and simpler, more reliable production ML systems.

read less read more

What It Prepares You For

Designing and deploying production ML infrastructure in pure Rust and removing Python dependencies from critical pipelines. You’ll be ready for roles like ML Platform Engineer, MLOps Engineer, and Rust Infrastructure Developer as demand grows for performance-focused ML systems.

read less read more

Course Outline

Expand All

Collapse All

Course Introduction

Trueno Tensor Fundamentals

Lab 2.1. Implement matrix multiplication benchmark. Compare trueno::matmul vs naive loops

Data Loading with Trueno-DB

Lab 3.1. Load 1M row dataset, validate schema, convert to tensors. Measure throughput

Pipeline Orchestration with Batuta

Lab 4.1. Build ETL pipeline: load → validate → transform → export. Add retry logic for failures

Experiment Tracking with Entrenar

Lab 5.1. Track training run: log loss curve, save model checkpoint, record hyperparameters

ML Algorithms with Aprender

Lab 6.1. Train classifier on tabular dataset. Compare linear vs tree. Log results to Entrenar

Text Processing with Trueno-Text

Lab 7.1. Train BPE tokenizer on corpus. Tokenize dataset, measure vocabulary coverage

Visualization with Trueno-Viz

Lab 8.1. Visualize training loss curve and feature distributions from Entrenar run

WASM Deployment with Presentar

Lab 9.1. Deploy trained Aprender model to browser. Build inference demo with <100ms latency

Capstone: End-to-End Pipeline

Course Summary

Prerequisites

Knowledge/Skills Prerequisites:

Learners should have general programming experience, command line familiarity, and basic ML knowledge. No prior Rust experience is required. Rust is introduced progressively, while experienced Rust users will move faster and focus on ML-specific patterns.

Lab Environment Prerequisites: