MASTER MLOps, LLMOps & AI PRODUCTION ENGINEERING
From Machine Learning Models to Production AI Systems
Learn how to build, deploy, automate, monitor and maintain machine learning and Large Language Model applications in production environments.
Understand the complete journey from data and model development to model deployment, LLM application delivery, evaluation, monitoring and continuous improvement.
Understand the complete journey from data and model development to model deployment, LLM application delivery, evaluation, monitoring and continuous improvement.
Core Technology Stack: Python • SQL • Machine Learning • MLflow • Docker • FastAPI • Kubernetes • CI/CD • Cloud • LLMs • RAG • LLM Evaluation • Monitoring
- 6-8 Months
- 300+ Practical Hours
- Live Online Training
- Industry Practice Labs
What You Will Learn
The learning journey begins with programming and AI foundations, then progresses through agent development, business workflow automation, system integration, testing and production deployment.
Machine Learning & AI Engineering Foundations
Understand the AI Development Lifecycle
Learn the engineering foundations required to take machine learning models beyond experimentation.
- Python for AI engineering
- Data processing and validation
- Machine learning fundamentals
- Model training and inference
- Model evaluation and performance metrics
- Feature engineering concepts
- Reproducible experiments
- Model and dataset versioning
Understand the relationship between data, models, applications and production systems.
Data Pipelines & Experiment Tracking
Build Reproducible Machine Learning Workflows
Learn how data and experiments are organised to support reliable model development.
- Data ingestion and preprocessing
- Data validation and quality checks
- Training and validation datasets
- Experiment tracking
- Hyperparameter configuration
- Model comparison
- Dataset and model versioning
- Reproducible training workflows
Technology Stack: Python • Pandas • SQL • MLflow • Data Validation Tools
Model Packaging & Deployment
Convert Trained Models into Usable Applications
Understand how trained models are packaged and exposed to applications and users.
- Model serialisation and loading
- Model inference workflows
- REST APIs
- FastAPI
- Input and output validation
- Dependency management
- Docker containerisation
- Model deployment and testing
Understand:
Data → Trained Model → Model API → Application → Prediction
MLOps & Automated ML Pipelines
Automate the Machine Learning Lifecycle
Learn how engineering teams automate model training, validation, deployment and lifecycle management.
- MLOps architecture
- Automated training pipelines
- Model registry
- Model versioning
- CI/CD for machine learning
- Automated model validation
- Deployment approvals
- Model promotion and rollback
Technology Stack: MLflow • Git • CI/CD • Docker • Pipeline Orchestration Tools
Cloud & Production Infrastructure
Run AI Applications in Production Environments
Learn the infrastructure concepts required to deploy and operate AI applications reliably.
- Cloud infrastructure fundamentals
- Application configuration
- Environment and secrets management
- Container images and registries
- Cloud deployment
- Kubernetes fundamentals
- Scaling and resource management
- Access control and security
Technology Stack: AWS • Microsoft Azure • Google Cloud • Docker • Kubernetes
LLMOps & Generative AI Applications
Manage Large Language Model Applications Beyond Development
Learn how LLM applications are integrated, evaluated and operated in production.
- LLM application architecture
- Model APIs and structured outputs
- Prompt and configuration versioning
- Retrieval-Augmented Generation (RAG)
- Embeddings and vector search
- LLM application evaluation
- Model and token usage tracking
- Latency and cost optimisation
Technology Stack: Python • LLM APIs • RAG • Embeddings • Vector Databases • Evaluation Frameworks
AI Evaluation, Monitoring & Reliability
Understand Whether Production AI Systems Are Working Correctly
Learn how AI systems are evaluated and monitored after deployment.
- Model performance monitoring
- Data and prediction drift concepts
- LLM response quality evaluation
- Retrieval quality assessment
- Latency and error monitoring
- Logging and tracing
- Failure analysis
- Feedback and improvement workflows
Understand the difference between testing a model before release and monitoring its behaviour after deployment.
Production AI Engineering & Lifecycle Management
Connect AI Development, Deployment and Operations
Understand how machine learning and LLM applications are managed across their lifecycle.
- Version-controlled AI applications
- Reproducible environments
- Automated testing and release pipelines
- Model and prompt updates
- Deployment strategies
- Rollback and recovery
- Production incident investigation
- Continuous evaluation and improvement
Technology Stack: Python • Git • Docker • CI/CD • MLflow • Cloud Monitoring • LLM Evaluation Tools
Hands-On MLOps & Production Practice
Train. Package. Deploy. Scale. Monitor.
Master the end-to-end engineering workflow required to take AI and LLM models from experimentation into reliable, scalable production environments.
Define the AI Use Case & Prepare Data
- Identify a practical business problem for an AI application
- Collect and inspect the required dataset
- Clean data and handle missing or inconsistent values
- Validate data quality and prepare training inputs
- Define baseline metrics and acceptance criteria
Train & Evaluate the Machine Learning Model
- Build a baseline machine learning model
- Prepare training and validation workflows
- Track experiments and model configurations
- Evaluate predictions using suitable metrics
- Select and version the model for deployment
Package the Model & Build an API
- Prepare the model for repeatable inference
- Build an API using FastAPI
- Validate application inputs and outputs
- Test prediction endpoints and error handling
- Package the application and its dependencies
Automate the MLOps Pipeline
- Store application code in Git
- Automate model and application validation
- Track experiments and register model versions
- Configure repeatable build and deployment steps
- Validate model promotion and rollback workflows
Containerise & Deploy the AI Application
- Create a Docker image for the AI application
- Configure environment variables and secrets
- Publish the image to a container registry
- Deploy the application to a cloud environment
- Verify application availability and prediction responses
Build an LLM Application with RAG
- Connect an LLM to a practical application
- Prepare and chunk knowledge documents
- Generate embeddings and configure retrieval
- Build and test a RAG question-answering workflow
- Evaluate answer relevance and supporting sources
Evaluate & Monitor Production AI
- Test model and LLM application behaviour
- Measure response quality, latency and errors
- Inspect logs and traces to investigate failures
- Track model performance and LLM usage costs
- Identify issues requiring correction or retraining
Release & Improve the Production AI System
- Prepare a controlled release of the AI application
- Validate the release against acceptance criteria
- Monitor the deployed application
- Investigate failures and implement corrective changes
- Update versions and repeat the evaluation cycle
MLOps, LLMOps & AI Production Engineering
What the Job Market Is Telling Us
Companies need professionals who can move AI models beyond experimentation into reliable, deployed, monitored and production-ready systems.
Tools & Technologies
Programming & Data
Python • SQL • Pandas • NumPy
Machine Learning
Scikit-learn • Model Evaluation • Feature Engineering
Experiment Tracking & MLOps
MLflow • Model Registry • Pipeline Automation
Application Development
FastAPI • REST APIs • Git
Containers & Deployment
Docker • Kubernetes Fundamentals • Cloud Platforms
Cloud
AWS • Microsoft Azure • Google Cloud
Generative AI & LLMOps
LLM APIs • Prompt Management • RAG • Embeddings • Vector Databases
Evaluation & Monitoring
Model Metrics • LLM Evaluation • Logging • Tracing • Latency and Cost Monitoring
Who is this Program For?
Python & Software Developers
Learn how to integrate AI models into applications and deploy them through production-oriented workflows.
Machine Learning & Data Science Learners
Progress from model training and experimentation toward deployment, automation and monitoring.
AI & Generative AI Developers
Develop the engineering skills required to evaluate, deploy and maintain LLM applications.
DevOps & Cloud Engineers
Understand the additional workflows needed to support machine learning models and LLM-based systems in production.
Your Outcome
Build, Integrate & Deploy AI Agents
By the end of the program, your practical learning should enable you to
Why Primebit Solution?
17 Years of Training Experience
Learn with an established training company with 17 years of experience in technical education.
In-Depth Technical Training
Build a strong understanding of concepts, tools and implementation through detailed, step-by-step teaching.
100% Practical Lab-Based Learning
Apply your knowledge through hands-on labs, real-world workflows and practical implementation exercises.
Career & Placement Support
Get career guidance, interview preparation and placement assistance for relevant national and international opportunities.
Global Alumni & Career Opportunities
Connect with a learning community spanning the USA, UK, India, Dubai, Canada, Australia and other regions.
Global Companies with Relevant Telecom, Cloud and DevOps Roles
Google, Microsoft, Amazon, NVIDIA, IBM, Oracle, Meta, OpenAI, Anthropic, Databricks, Snowflake, Adobe, Salesforce, Accenture, Deloitte, Capgemini, Infosys, TCS, HCLTech, Wipro.
These companies operate in areas related to AI, cloud, machine learning, enterprise software or consulting.
PROGRAM AT A GLANCE
Duration & Practical Hours
Flexible 6/8 months
Mode
Live Hands-on Training
Core Skill
ML Engineering • MLOps • LLMOps • AI Deployment • Production Monitoring
Practical Focus
Model deployment • Pipeline automation • LLM application development • Production evaluation • Monitoring
Program
MLOps, LLMOps & AI Production Engineering
Learning Journey
Python & Data → Machine Learning → Experiment Tracking → Model Deployment → MLOps → Cloud & Containers → LLM Applications → RAG → LLMOps → Evaluation & Monitoring
Technologies Stack
Python • MLflow • FastAPI • Docker • Cloud • LLM APIs • RAG • Evaluation Tools
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Frequently Asked Questions
Learners with basic Python knowledge will find it easier to follow. Those new to machine learning should complete the foundational modules before progressing to MLOps and LLMOps.
MLOps applies engineering, automation and operational practices to the machine learning lifecycle, including training, versioning, deployment and monitoring.
LLMOps focuses on the development and operation of applications powered by Large Language Models, including evaluation, prompt management, retrieval, monitoring, latency and cost control.
Yes. The learning path includes API development, containerisation, cloud deployment and production monitoring concepts.
Yes. The course includes the foundations of Retrieval-Augmented Generation, document processing, embeddings, retrieval and evaluation.
MLOps commonly manages trained machine learning models and their lifecycle. LLMOps addresses additional requirements of LLM applications, such as prompt and response evaluation, retrieval quality, token usage and model response behaviour.
BUILD YOUR CAREER IN PRODUCTION AI ENGINEERING
Train Models. Deploy AI. Monitor. Improve
Develop practical skills across machine learning, LLM applications, deployment automation and production AI operations.
Move beyond AI experimentation and learn the engineering lifecycle behind production AI systems.
Primebit Solution — Practical Training for Modern Engineering Careers
- MLOps
- LLMOps
- Model Deployment
- Pipeline Automation
- Production AI
Train the model. Deploy the system. Build the future of production-ready AI software.