AI adoption is growing rapidly, and skilled MLOps professionals are highly sought after to deploy, monitor, and scale machine learning models efficiently. Our MLOps Online Training Course equips you with hands-on skills to manage AI workflows and advance your career in this fast-growing field.
Visualpath MLOps Training offers practical, hands-on learning with the Updated Curriculum. Students from Hyderabad and other regions gain real-world skills to manage ML pipelines confidently.
Our MLOps Online Course focuses on practical learning. You will learn how to:
Professionals from Bangalore often join this course to enhance their skills in enterprise ML workflows.
The MLOps & AIOps Online Training program is designed to bridge the gap between machine learning development and operations, while introducing you to AI-driven IT operations. This course helps you automate, monitor, and deploy ML models efficiently using MLOps and manage infrastructure intelligently using AIOps. Delivered by industry experts, this training includes hands-on labs, real-time projects, and essential tool integrations.
Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.
Description
MLOps (Machine Learning Operations) and AIOps (Artificial Intelligence for IT Operations) are transforming the way enterprises manage and scale AI/ML initiatives. This course provides in-depth knowledge and hands-on experience in automating the end-to-end lifecycle of machine learning models, including continuous integration, deployment, testing, and monitoring. You will also explore how AIOps leverages big data and ML to enhance IT operations, improve uptime, and reduce manual interventions.
Throughout the training, learners will work with leading tools like MLflow, Kubeflow, TensorFlow Extended (TFX), Jenkins, Docker, Kubernetes, Prometheus, Grafana, ELK stack, and more to deploy intelligent pipelines and manage ML and IT workflows efficiently.
Course Objectives
By the end of this MLOps & AIOps Online Training, you will be able to:
Understand the concepts and lifecycle of MLOps & AIOps
Implement CI/CD pipelines for ML models
Automate data workflows and model training
Monitor, deploy, and manage ML models in production
Use tools like MLflow, Kubeflow, TFX for MLOps
Apply AIOps practices to enhance IT observability and root cause analysis
Manage and deploy models using Docker and Kubernetes
Work with logging, monitoring, and alerting systems using ELK, Prometheus & Grafana
Develop real-time use cases and integrate ML models into scalable systems
Prerequisites
To get the most out of this training, learners should have:
Basic understanding of Python and Machine Learning
Familiarity with DevOps concepts (optional but beneficial)
Knowledge of cloud platforms like AWS, Azure, or GCP (recommended)
Prior experience in data science or IT operations is a plus
