MLOps Fundamentals

Course Code: IT 1898

6 Course Visits

MLOps Fundamentals

Course Sector:

Information Technology

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Course Dates and Locations

Choose a date and location to book your seat

No.

Date

Days

Location

Fees

Enrollment

04 - 06 Aug 2025

3 Days

Online, Virtual

$1,550

12 - 16 Oct 2025

3 Days

Riyadh, KSA

$3,250

24 - 26 Nov 2025

3 Days

Dubai, UAE

$3,250

Introduction

Training course introducion / brief

In modern AI workflows, building accurate models is only half the battle. The real challenge lies in reliably deploying, scaling, monitoring, and automating machine learning (ML) models in production. This course introduces the fundamentals of MLOps—Machine Learning Operations—a set of practices that bring DevOps principles to ML systems. Over three intensive days, participants will learn how to design reproducible workflows, automate training pipelines, monitor deployed models, and adopt industry-standard MLOps tools for building scalable AI solutions.

By the end of this course, participants will be able to:

Understand the core principles and lifecycle of MLOps
Build reproducible ML pipelines with versioning and tracking
Automate training, testing, and deployment workflows
Deploy models to production using CI/CD practices
Monitor model performance and data drift in production
Use tools like MLflow, DVC, Kubeflow, GitHub Actions, and Docker/Kubernetes

Foundations of MLOps and ML Lifecycle Automation

What is MLOps? Why is it important?
ML system lifecycle: development vs production
MLOps vs DevOps: similarities and differences
Reproducibility and versioning challenges in ML
Introduction to tools: Git, MLflow, DVC

Building and Automating ML Pipelines

Anatomy of an ML pipeline: preprocessing, training, evaluation

Pipeline orchestration tools: Kubeflow Pipelines, Apache Airflow

Parameter tuning and automation

Introduction to CI/CD in ML (training → testing → deployment)

ML pipeline as code (YAML, DSL)

Model Deployment, Monitoring, and Scaling

Serving models: REST APIs, batch vs real-time inference

Deployment strategies: blue-green, canary, shadow testing

Tools: Docker, FastAPI, Flask, TensorFlow Serving, TorchServe

Monitoring: model performance, drift, data quality

Scaling with Kubernetes and model registry

Boost certificate of completion

BOOST's Professional Attendance Certificate “BPAC” is always given to the delegates after completing the training course, and depends on their attendance of the program at a rate of no less than 80%, besides their active participation and engagement during the program sessions.

ENDORSED EDUCATION PROVIDER

Flexible deadlines

Customized dates accordance to your schedule

Shareable Certificate

Earn certificate upon completion

COURSE METHODOLOGY

Our Training programs are implemented by combining the participants' academic knowledge and practical practice (30% theoretical / 70% practical activities).

At The end of the training program, Participants are involved in practical workshop to show their skills in applying what they were trained for. A detailed report is submitted to each participant and the training department in the organization on the results of the participant's performance and the return on training. Our programs focus on exercises, case studies, and individual and group presentations.

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TRAINING COURSE: MLOps Fundamentals