Training Type
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Dur:
3
months
Course fee :
25000
/-
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.
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.
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
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
This course is ideal for:
Machine Learning Engineers looking to streamline deployment
DevOps Engineers aiming to integrate ML workflows
Data Scientists interested in model lifecycle management
IT Operations Teams seeking automation through AIOps
Software Engineers who want to work with intelligent systems
Cloud Engineers working on ML infrastructure
Freshers & Enthusiasts with a passion for ML/AI automation
Average package of course (MLOps & AIOps)
Elevate your career with essential soft skills training for effective communication, leadership, and professional success.
Learn from trainers with extensive experience in the industry, offering real-world insights.
Enjoy round-the-clock access to course materials and resources for flexible learning.
Benefit from specialized programs focused on securing job opportunities post-training.
Learn by doing with hands-on practice, mastering skills through real-world projects
State-of-the-art lab facility, guided by experienced mentors, ensures hands-on learning excellence in every session
The MLOps course was a game-changer, covering deployment, monitoring, and tools like Docker and Kubernetes with hands-on labs. A must for AI engineers!
Subhasmita Pradhan
Top 5 Technologies to learn
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