Machine Learning Operations (MLOps) & Feature Engineering

Engineered for Data Scientists, this specialized course focuses heavily on the production side of AI, showing you how to refine and standardize raw, chaotic business communications before they ever touch an LLM. You will master advanced feature engineering techniques , learning how to build automated pipelines that reduce text noise , handle complex subword tokenization , clean stopwords and punctuations , and dynamically correct spelling or typographical errors. Moving past text preparation, this class guides you through the full MLOps lifecycle—giving you a structured framework to securely code, train, validate, evaluate, serve, and monitor fine-tuned models via scalable production APIs.

Instructor-Led

20 hours

3 modules

Recommended Course Audience

Data Scientists ML Engineers

Pre-Required Skills

Experience: 2+ years of experience as a Data Scientist or Machine Learning Engineer.
Core Technical Skills: Proficient in Python; deep familiarity with standard ML frameworks (such as PyTorch, TensorFlow, or Scikit-Learn); solid command of statistical math.
Systems Exposure: Experience with model training pipelines, tokenizers, and model deployment tooling.

Course Highlights

3 modules

Implementing advanced text normalization workflows during the critical Feature Engineering phase. Developing programmatic routines to handle noisy text, cleanse input data, and isolate high-value words. Designing system overrides for handling spelling mistakes and typographical errors across corporate datasets. Building cleaning layers to systematically remove stopwords and punctuations to lower token cost overheads.

Mapping words to numbers: Designing text tokenization pipelines tailored for enterprise vocabulary sets. Implementing Subword Tokenization algorithms to process rare vocabulary, compound industry terms, and complex codes smoothly. Designing custom logic paths for handling special tokens and architectural markers inside training strings. Preparing normalized data fragments to feed into Sentence Transformers for seamless Vector Store ingestion.

Establishing structured engineering guidelines for the complete model lifecycle: Code, Train, Validate, and Evaluate. Organizing training data pipelines to track validation metrics and loss calculations over time. Managing Model Deployment frameworks: Moving trained file assets into production cloud configurations securely. Deploying scalable web APIs to let applications consume models instantly while running continuous performance telemetry monitoring.

Machine Learning Operations (MLOps) & Feature Engineering

$699.00

This course is coming soon. Enrollment is not yet open.

Level : Beginner

Modules : 3

Duration : 20 hours