Our MLOps course delves into the full lifecycle of machine learning projects, from algorithm development to deployment and maintenance. Participants will learn to build robust ML models using industry-standard tools and frameworks, ensuring high performance and scalability. The course covers essential deployment strategies, including containerization and orchestration with Kubernetes, to facilitate seamless integration into production environments. Emphasis is also placed on monitoring and maintaining models post-deployment, with best practices for continuous integration and delivery (CI/CD) in ML workflows. By the end of the course, students will be equipped to manage and optimize end-to-end ML operations, ensuring reliability and efficiency in real-world applications.
MLOps Training
Jan 5, 2026 - Mar 27, 2026
AI Career Accelerator
What you'll learn:
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Learn to integrate AI/ML techniques into traditional ETL pipelines to build intelligent data workflows.
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Gain proficiency in machine learning algorithms such as regression, classification, and clustering using Python libraries.
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Build a strong foundation in linear algebra and statistics tailored for real-world machine learning applications.
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Perform exploratory data analysis (EDA) using Matplotlib, Seaborn, and Plotly to uncover actionable insights.
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Apply advanced feature engineering techniques to enhance model input quality and improve performance.
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Design scalable training and testing pipelines that prevent data leakage and are ready for production deployment.
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Develop and validate ML models using frameworks like Scikit-Learn, TensorFlow, and PyTorch.
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Optimize model performance with evaluation metrics, hyperparameter tuning, and interpretability tools like SHAP and LIME.
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Deploy ML models using MLOps best practices, including continuous integration, monitoring, and drift detection.
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Explore deep learning applications in NLP, computer vision, and agentic AI with hands-on experience in transformers and LLMs.
Why Choose Our Program?
Our guiding principle is clear and impactful: we deliver hands-on, end-to-end execution of four real-time, industry-standard projects across four different industries — from defining the problem statement to building ML models live in class, all the way through to deploying them into production.
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Showcase your skills by adding a GitHub portfolio with these projects directly to your resume, demonstrating your practical ML/AI expertise.
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Engage in intensive, hands-on labs designed around real-world challenges to build confidence and competence.
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Benefit from our comprehensive Interview Strategy module, equipping you with the skills and preparation needed to successfully pivot into the AI career landscape.
Course Content:
Introduction to ML/AI for Data Engineers
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Overview of ML/AI Concepts
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Mapping Data Engineering to ML
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Course Roadmap & Expectations
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Weekly Mini Project
Fundamentals of ML, Linear Algebra and Statistics
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ML Basics & Common Algorithms
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Linear Algebra Essentials
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Statistics & Hypothesis Testing
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Integration and Wrap-up
EDA for ML
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Data Visualization Techniques
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Statistical Summaries & Descriptive Analytics
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Outlier Detection & Data Quality Assessment
Feature Engineering for ML
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Feature Extraction Techniques
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Automated Feature Engineering Tools
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Practical Feature Engineering Applications
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Weekly Mini Project
Data Pipelines for ML
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Designing ETL/ELT Processes for ML
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Advanced Data Cleaning & Transformation
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Integrating Real-Time Data Flows
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Weekly Mini Project
Model Selection & Algorithm overview
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Model Selection & Algorithm Overview
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Hands-On Model Training
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Best Practices in Training
Hyper-parameter Tuning and Model Evaluation
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Hyper-parameter Tuning Techniques
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Model Evaluation Metrics Part 1
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Practical Evaluation Exercise
ML Ops & Model Deployment
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Deployment Strategies & CI/CD for ML
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Monitoring & Maintenance
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Hands-On Deployment Exercise
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Weekly Mini Project assignment & conclusion
Introduction to Deep Learning Concepts
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Deep Learning Fundamentals
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Building and Training a Simple Neural Network
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Deep Learning vs. Traditional ML
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Privacy and Data Governance in ML
Deep Learning Domain Applications
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Introduction to Domain Applications
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Case Studies and Practical Examples
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Weekly Mini Project Introduction and Wrap-up
Specialized NLP, Transformers, LLM and GenAI
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Advanced NLP Techniques
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Transformers & Attention Mechanisms
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Applications of Large Language Models
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Weekly Mini-Project Introduction
Specialized NLP
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NLP Fundamentals Recap
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Sequence Modeling & Embedding
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NLP with Transformers
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Mini-Project Introduction
Agentic AI
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Introduction to Agentic AI
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Core Architectures & Planning Strategies
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Agent Frameworks in Practice
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Mini-Project Introduction
Interview Strategy
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Behavioral Interview Preparation & Strategy
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Resume Makeover with Expert Guidance
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Professional Branding for AI/ML Roles
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Live Mock Interviews with Feedback
Who Should Apply:
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Engineers seeking to pivot into ML/AI roles.
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Professionals aiming to deepen their AI expertise.
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Anyone passionate about leveraging AI in real-world scenarios
Modular Course Fee:
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ML Technical + ML Ops Track + Gen AI + Agentic AI ($599) – Master core AI/ML skills
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Behavioral Interview Prep ($199) – Ace soft skills & storytelling
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Professional Resume Writing ($199) – Get a job-ready resume
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FULL Program ($999) – End-to-end career support (Best value!)
Limited Spots Available. Contact us on whatsapp for more details
