Introduction to Azure Environment Training & Tuning ML Models using Azure Working with Data in Azure Training ML Models in Azure ML Tuning ML Models in Azure Azure Automated ML Deploying & Explaining ML Models Deploying ML Model for Real-Time Inferencing Deploying ML Models for Batch Scoring Responsible AI

Setting up AWS Sagemaker & Deploying Simple ML Model AIOps with AWS Sagemaker Studio

Overview of LangChain and Ecosystem Components: Chains, Agents, Tools, Memory, Prompts Integration with OpenAI, Anthropic, Cohere, HuggingFace Use Cases: Doc Chatbot, PDF QA, RAG Chatbots, Tools-based Agents Custom Chains and Agents

Best Corporate AI Training | Hands-On Agentic AI Training for Teams

Best Corporate AI Training | Ready to Build an AI-Capable Workforce?

BEPEC is a practitioner-led corporate AI training provider helping organizations turn their workforce into an AI-capable team. We don’t deliver generic slide decks, we deliver hands-on, role-based programs built around your tools, your data, and your business outcomes.

With 30,000+ professionals trained and a learning community of 300K+, BEPEC brings real industry credibility to every corporate engagement.

What We Cover In Our Corporate AI Trainings?

Stage-1: Beginner Skills

✅ Python ✅ SQL ✅ DSA ✅ GitHub ✅ Generative AI (intro) ✅ Prompt Engineering ✅ NLP ✅ Supervised & Unsupervised ✅ Deep Learning Algorithms ✅ Transformers ✅ Embeddings & Reranking ✅ Vector Databases ✅ Docker

Stage-2: Builder Skills

✅ LangChain ✅ LangGraph ✅ CrewAI ✅ AutoGen ✅ Multi-Agents ✅ Computer Vision ✅ NLUs ✅ Reinforcement Learning ✅ Hybrid RAG ✅ GraphRAG / Knowledge Graphs ✅ Structured Outputs & Tool Calling ✅ Agent Memory & State ✅ Model Context Protocol (MCP) ✅ Multimodal LLMs ✅ Fine-Tuning ✅ LoRA / QLoRA ✅ PEFT ✅ SLMs ✅ Local LLMs (Ollama / vLLM) ✅ LangSmith ✅ Snowflake ✅ AWS / Azure / GCP ✅ n8n / Workflow Automation ✅ Context Engineering

Stage-3: End-to-End Project Skills

✅ Agentic AI (production) ✅ DeepAgents ✅ Agent-to-Agent (A2A) Protocol ✅ Human-in-Loop Agents ✅ LangGraph Studio ✅ Voice Agents ✅ Guardrails & AI Safety ✅ RAG Evaluation ✅ Evals & LLM-as-Judge ✅ Agent Observability & Tracing ✅ Model Training & Evaluation ✅ RLHF / DPO ✅ Synthetic Data Generation ✅ Quantization & Inference Optimization ✅ Agent Cost & Token Optimization ✅ LLMOps / MLOps ✅ CI/CD ✅ Kubernetes ✅ Deployment ✅ AI Product Management

Introduction to AI, Agentic AI & Gen AI

Introduction to Artificial Intelligence and its real-world impact
Evolution of AI from rule-based systems to deep learning
Overview of Machine Learning, Deep Learning, and core algorithms
Understanding Generative AI and Foundation Models
Working with Large Language Models (LLMs) and Prompt Engineering
Exploring Retrieval-Augmented Generation (RAG) for contextual intelligence
Introduction to Agentic AI and autonomous intelligent systems
Key components of agents — Memory, Tools, Planning, and Decision Nodes
Overview of LangChain, LangGraph, AutoGen, CrewAI, and Semantic Kernel
Multi-Agent collaboration frameworks and real-world applications in automation
Demonstration of Planner–Executor–Evaluator architecture in multi-agent systems
Integration of Generative AI with Agentic AI for autonomous decision-making
Career pathways and skill roadmap in AI, Gen AI, and Agentic AI

Python: Zero to Job-Level

Introduction to Python
Why Python, Value, Variable, Function, Library [Roadmap on Python]
IDE in Python, Different Data Types
List, Tuple, Set & Dictionary Overview
Different List Methods
Different Tuple Methods
Set & Frozenset
Dictionary & String Manipulations
Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda
Types of Operators, Conditional Statements
While Loop, List Comprehension, Break, Continue, Arguments
Functions, Escape Sequences, Lambda Functions
Hackathon-1
Iterators, Decorators & Generators
Modules in Python
Creating Custom Python Libraries
Lambda, Map, Filter, Reduce
Exception Handling
File Handling
Regular Expressions (Regex)
Web Scraping Basics
Introduction to OOPS
Instance Variable, Class Variable, Class Method
Association vs Composition & Aggregation
Oops Concept
Encapsulation, Inheritance
Polymorphism, Method Overloading, Method Overriding
Introduction to Pandas
Data Analysis using Pandas
Introduction to Numpy
Different Numpy Commands
Introduction to Data Visualisation
Data Visualisation using Matplotlib
Data Visualisation using Seaborn
Data Visualisation using Plotly
Why Data Cleaning?
Data Cleaning with Sklearn & pandas
Regular Expression Basics
Mastering Langchains
Mastering Tensorflow
Mastering PyTorch
Mastering Langgraph
Mastering Crew AI
Mastering Streamlit
Mastering Langsmith

Advanced Data Structures & Algorithms

Introduction to Advanced DSA
Non-Primitive Data Structures
Non-Linear Data Structures
What is an Algorithm
Theory & Code Implementation of Linked List
Stacks & Queues Assignment
Coding Stack Data Structure
Coding Queue Data Structure
Tree Data Structures
Types of Tree Data Structure
Tree Traversal
BFS Traversal
Bubble Sort Theory
Bubble Sort Code Implementation
Selection Sort Theory
Selection Sort Code Implementation
Insertion Sort Theory
Insertion Sort Code Implementation
Merge Sort Theory
Merge Sort Code Implementation
Quick, Merge Sort Performance
Quick Sort Theory
Linear Search & Bisection Search

AI Python Coding Interview Prep

Pandas DataFrames for Coding Interviews
Data Cleaning and Imputation Logic
GroupBy and Aggregation Coding
Merging, Joining, and Concatenating DataFrames
Reshaping Data (Melt, Pivot)
NumPy Array Broadcasting
Vectorized Operations vs Loops
Handling DateTime Objects in Python
String Manipulation with Regex

Hands-On SQL, Projects with Data Warehouse Concepts

- What is SQL, RDBMS, and Table Structure
- Understanding Sprint, Scrum and Agile Project Breakdown in SQL
- OLTP vs OLAP
- Data Warehousing Concepts
- Data types (INT, VARCHAR, DATE, BOOLEAN, etc.)
- ER Diagrams
- Data Models like Star Schema and Snowflake Schema
- DDL vs DML vs DCL vs TCL Commands
- Basic CRUD Operations — 41:21
- Different DDL Commands — 41:22
- Different DML Commands
- Upsert Operations
- Different DQL Commands
- Database Constraints
- Aggregate Functions, Date Functions and String Functions
- SQL Joins: Inner, Self, Cross, Left, Right and Outer Join
- SQL Grouping & Aggregations
- SubQueries and Types of SubQueries
- Window Functions in SQL

Hands-On Advance SQL Concepts

- Data Integrity & Referential Integrity
- Data Normalisation
- First & Second Normal Form
- Functional Dependency & Transitive Dependency
- Boyce Codd Normal Form
- Denormalization
- Temporary Tables, CTE, Recursive CTE
- When to Use Temporary Table, CTE, Recursive CTE
- SubQuery in MySQL
- Views in MYSQL
- Stored Functions
- Stored Procedures
- Triggers in MySQL
- Create Events
- In-depth DDL Commands
- Different Functions in MySQL

Applied Statistics (Guided Hands-On)

What is Data? Various Types of Data
Levels of Measurement
Categorical Variables & Visualisation Techniques
Continuous Variables and Visualisations
Measures of Central Tendency
Measuring Skewness
Measures of Dispersion
Covariance & Correlation Coefficient
Box & Whisker Plot
Outlier Identification
Treating Outliers
Sampling Techniques
Statistical Analysis Consideration
Types of Analytics
Sample vs Population
Introduction to Inferential Statistics
Law of Large Numbers
Central Limit Theorem
Probability Distributions
Conditional Probability
Continuous Probability Distribution & Discrete Probability Distribution
PMF, CDF, PDF, PPF in Probability
Binomial Distribution
Poisson Distribution
Normal Distribution
Exponential Distribution
Laplace Distribution
Log-Normal Distribution
Standard Error
Confidence Interval Calculations
Z-Table for Confidence Interval
T-Table for Confidence Interval
Hypothesis Testing
Null & Alternate Hypothesis
Type-1 Error & Type-2 Error
2-Sample T Test
ANOVA Test
p-Value Calculations
Chi-Square Test
Project Demonstration using all the Statistical Techniques

Statistics with Real-Time Project Demonstration on EDA

Basics of Statistics
Central Limit Theorem, Normal Distribution, Skewness
Real-Time Project Demonstration on EDA with Real-World Project
End-to-End Statistics using Excel

Probability Distribution, Hypothesis Testing

Probability Distribution, Random Variable, Binomial, Poisson, Exponential
Normal Distribution
Real-Time Project Demonstration on Probability
Hypothesis Testing Part-1
Hypothesis Testing Part-2
How to Calculate P-Value? Coding Hypothesis Testing
How to solve PDF, CDF, PMF?
Chebyshev’s, Log, Power Law, Q-Q, CLT

Feature Engineering Techniques

Feature Scaling Techniques
Normalisation
Standardisation
Min Max Scaler
Robust Scaler
Label Encoding Technique
One-Hot Encoding Technique
Dummies Technique
Ordinal Encoding Technique
Imputation Technique
Feature Selection Techniques
Filter Method: Feature Importance
Filter Method: Fishers Score Technique
Filter Method: Chi-Square Technique
Filter Method: Variance Threshold
Filter Method: Correlation Coefficient
Filter Method: Mutual Information
Wrapper Methods
Wrapper Method: Forward Selection Technique
Wrapper Method: Backward Selection Technique
Wrapper Method: Recursive Feature Elimination Technique
Wrapper Method: Exhaustive Feature Selection Technique
Wrapper Method: Sequential Feature Selection Technique
Embedded Methods
Embedded Methods: Regularisation
Embedded Method: Elastic Net Regression
Embedded Method: Decision Tree
Other Techniques
Outlier Treatment
Over Sampling Technique
Under Sampling Techniques

Applied Machine Learning (Basics To Job-Ready)

Types of ML: Supervised, Unsupervised, Reinforcement
Pipeline: Data Cleaning → Feature Selection → Modelling → Evaluation
Bias-Variance Trade-off
Train/Validation/Test Splits
Cross-Validation
Linear Regression (with gradient descent math)
Math behind OLS Technique
Assumptions of Linear Regression
Evaluation Metrics: RMSE, MAE, R² Score
Polynomial Regression
Coding Linear Regression Model
Regularisation Techniques
Why Logistic Regression
Math Behind Logistic Regression
Evaluation Metrics Behind Classification Algorithms
Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1, AUC-ROC
Coding Logistic Regression Model
Introduction to Decision Tree
Intuition Behind Decision Tree
Math Behind Decision Tree
Math Behind Decision Tree using GINI
Drawbacks of Decision Tree
Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost
Coding Decision Tree, Random Forest, GB
SMOTE Technique to Handle Imbalanced Dataset
Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques
Coding Feature Selection Techniques
Math Behind KNN
Math Behind SVM — 47:20
Plotting SVM
Introduction to PCA
Math Behind PCA
Coding PCA
AutoML Using Pycaret
Clustering: K-Means (Euclidean distance), Hierarchical Clustering
Dimensionality Reduction: PCA (Eigen decomposition), t-SNE
Anomaly Detection
Hyperparameter Tuning: GridSearchCV, RandomSearch
Pipelines with Scikit-learn

Unsupervised Learning with Real-Time Projects

Supervised vs Unsupervised Learning
Fundamentals of Machine Learning Part-1
Fundamentals of Machine Learning Part-2
Insights from Train & Test Accuracy
Associate Rules Part-1
Associate Rules Part-2
Problem Identification & Approach Designing
Associate Rules Scripting
Why to do Dimensionality Reduction? Project Implementation

Supervised Learning Algorithms

How to Prepare Machine Learning Math for Interviews
Why to use Linear Regression?
What is Linear Regression
Case Study on Linear Regression
Math Behind Linear Regression Part-1
Math Behind Linear Regression Part-2
Math behind OLS Technique
Assumptions of Linear Regression
Evaluation Metrics for Regression Model
Accuracy Improving Techniques
Coding Linear Regression Model
Regularisation Techniques
Why Logistic Regression
Math Behind Logistic Regression
Evaluation Metrics Behind Classification Algorithms
ROC & AUC Curve
Coding Logistic Regression Model
Introduction to Decision Tree
Intuition Behind Decision Tree
Math Behind Decision Tree
Math Behind Decision Tree using GINI
Drawbacks of Decision Tree
Random Forest and Gradient Boosting
Coding Decision Tree, Random Forest, GB
SMOTE Technique to Handle Imbalanced Dataset
Filter Method, Wrapper Method, Embedded Method Feature Selection Techniques
Coding Feature Selection Techniques
Math Behind KNN
Math Behind SVM
Plotting SVM
Additional Topics
Introduction to PCA
Math behind PCA
Coding PCA
AutoML Using Pycaret

Natural Language Processing

What is NLP? Applications in industry
Tokenization, Stopwords, Lemmatization vs Stemming, Normalization
Regular Expressions (Regex) in NLP
Bag of Words (BoW)
TF-IDF Vectorization (Term Frequency–Inverse Document Frequency)
Word embeddings: Word2Vec (CBOW, Skip-Gram), GloVe
Cosine Similarity
N-Gram models (Unigram, Bigram, Trigram)
Language Modeling: Probability of a word sequence
Smoothing techniques (Laplace, Good-Turing)
Parts-of-Speech Tagging (Rules + Statistical)
Named Entity Recognition (NER)
Chunking and Chinking
Using spaCy’s NER pipeline

MLOps - CI/CD, Docker, K8s

End-to-End Deployment Using MLOps & Streamlit
Setup Virtual Environment
Docker Installation
How to Activate Virtual Environment
Docker Desktop
Regression Model using Pycaret
Regression Model using Pycaret Part-2
Interpretability of Model using SHAP
What is SHAP
Application Development using Gradio
Creating API using FASTAPI
Creating Docker Image
Model Versioning using Pycaret & MLFlow
End-to-End ML Proof of Concept

Azure ML Studio

Introduction to Azure Environment
Training & Tuning ML Models using Azure
Working with Data in Azure
Training ML Models in Azure ML
Tuning ML Models in Azure
Azure Automated ML
Deploying & Explaining ML Models
Deploying ML Model for Real-Time Inferencing
Deploying ML Models for Batch Scoring
Responsible AI

AWS Sagemaker Studio

Setting up AWS Sagemaker & Deploying Simple ML Model
AIOps with AWS Sagemaker Studio

Advanced Deep Learning using Tensorflow & PyTorch

Building Blocks of Artificial Neural Networks
What are Optimisers and Types of Optimisers
Math Behind Feed Forward Neural Network
Math Behind BackPropagation
Why Activation Functions & Importance
Introduction to Tensorflow
FFNN – Regression Script using Tensorflow
FFNN – Classification Script Using Tensorflow
Introduction to CNN & Why CNN Algorithm
What is Object Detection & Object Classification
Intuitive Understanding Behind CN
Math Behind CNN
CNN Coding using Tensorflow
CNN Coding using Keras
Types of CNN Networks
What is Transfer Learning
Tensorflow Hub & Various Pre-Trained Models
Using Fre-Trained Models for Object Classification
Deploying Image Classification Model in AWS
Project Demonstration. Image Classification Project

Computer Vision Mastering Using Tensorflow

Introduction to Object Detection vs Object Classification
Introduction to CNN
Convolution Matrix
Max Pooling Layer
Point-Wise & Element-Wise Convolution
Batch Normalisation
Strides & Padding
Convolution Filters
End-to-End Computer Vision

Natural Language Understanding

What is NLU & Why NLU
What is POs tagging
Case Study: Coding POS Tagging
What is Dependency Parsing?
What is Constituency Parsing?
Case Study: Coding Dependency & Constituency Parsing
What is NER? & Usage of NER
Case Study: Coding NER
What is Coreference Resolution?
History of NLU Models
Project Demonstration: NLU Classification Model using LSTM
What is Encoder & Decoder Model
Math Behind Encoder-Decoder Models

Transfer Learning OpenCV & YOLO

Introduction to Transfer Learning
Headless & With Head Transfer Learning
Object Detection using YOLO
Object Detection using Open CV
Transfer Learning with Tensorflow Hub

Recurrent Neural Networks & LSTMs

What is RNN & Why RNN?
Limitations of RNN & Why LSTM
Intuitive Understanding Behind RNN & LSTM
Math Behind RNN
Math Behind LSTMS
Math Behind GRUS
Coding RNN, LSTM, GRU using Tensorflow
Project Demonstration: Forecasting Project using LSTMS
Project Demonstration: Resume Scoring

Transformer Models BERT, T5, ELMO

introduction to Transformers
Scaled Dot Product Attention
Multi-Headed Attention
Introduction to Transfer Learning
Introduction to PyTorch
Fine-Tuning Transformers using PyTorch
Introduction to BERT
Wordpiece Tokenisation
Embeddings of BERT
Masked Language Modelling Task
Project Demonstration: Sentence Prediction Task
Types of BERT
BERT for Sequence Classification
BERT for Question & Answering

Fundamentals of Generative AI

What is Generative AI? Applications and Industry Use-Cases
Evolution: GANs → VAEs → Transformers → Diffusion → LLMs → MoEs
Key Architectures: GPT, BERT, Diffusion, UNet, U-ViT
Metrics: FID, BLEU, ROUGE, perplexity, hallucination rate

Generative AI, Prompt Engineering using LangChains

What is Generative AI? Use cases in text, image, code, and conversation
Foundation Models: GPT, Claude, PaLM, Gemini, Mistral, LLaMA
Introduction to LangChains
Using LLMs using Lang Chains
What is Prompt Engineering
Prompt Engineering Principles
Zero-shot, Few-shot, Chain-of-Thought (CoT)
Best Way to Improve LLMs Accuracy
Doing Prompt Engineering using Lang Chains
PromptTemplate best practices with LangChain
Using ChatPromptTemplate, SystemMessagePrompt,

LangChain Framework

Overview of LangChain and Ecosystem
Components: Chains, Agents, Tools, Memory, Prompts
Integration with OpenAI, Anthropic, Cohere, HuggingFace
Use Cases: Doc Chatbot, PDF QA, RAG Chatbots, Tools-based Agents
Custom Chains and Agents

Amazon Bedrock & GenAI on Cloud

What is Amazon Bedrock?
Using Claude, Titan, and Meta Models via Bedrock
Prompt Chaining, Guardrails, Evaluators in Bedrock
Comparison with Vertex AI (Google), Azure AI Studio
Custom Fine-Tuning and Hosting on Bedrock

Retrieval-Augmented Generation (RAG) with LangChain

Why RAG? Token limits, context injection
Vector Embeddings: OpenAI, HuggingFace, Cohere
Vector DBs: FAISS, Pinecone, Chroma, Weaviate, Milvus
LangChain’s RetrievalQA Chain
Chunking strategies and TextSplitter
Hybrid search: Dense + Metadata filtering
SequentialChain vs SimpleSequentialChain
RunnablePassthrough, RunnableLambda (LCEL)
Creating Tool abstractions with @tool decorator
Integrating APIs (e.g., SerpAPI, Weather, Calculator)
Output parsers: StrOutputParser, StructuredOutputParser

Agentic AI Development

What is Agentic AI?
Agent Architecture: Planning → Tool Use → Memory → Reflection
Tools: AutoGPT, BabyAGI, SuperAgent, CrewAI, AgentLabs
Key Capabilities: Decision Trees, Recursive Planning, Dynamic Memory
Toolformer & Tool Use via Function Calling
Types of agents in LangChain: ReAct, ConversationalReAct, Plan-and-Execute, OpenAIFunctionsAgent
Agent memory (ConversationBufferMemory, SummaryMemory)
Task Decomposition and Planning
Use Cases: Code generation, Auto-documentation, Research Assistants

LangGraph – Multi-Agent DAG Framework

What is LangGraph? (Directed Acyclic Graphs for LLM Workflows)
Graph Node Design: Single-Agent vs Multi-Agent Nodes
Persistent Memory and State Sharing Between Agents
Use Case: Build a Modular Multi-Agent PDF Analyst
Integration with OpenAI & Anthropic Models
What are Multi-Agent Systems (MAS)?
Types: Cooperative, Competitive, Mixed-Mode Agents
Examples: ChatDev, AgentVerse, CAMEL, OpenDevin
Communication Protocols between Agents
Real-world Multi-Agent Workflow Simulations

Model Context Protocol (MCP)

Definition & Role of MCP in LLM systems
Standardising LLM Input/Output via JSON Schemas
Tool Usage: Tool Metadata, Function Signatures
Comparison: MCP vs LangChain Schemas
What is Model Context Protocol (MCP)
Context-aware decision making
Prompt-as-function vs Function-as-prompt
Designing structured workflows (LangGraph overview)
Communication between agents
Build an MCP-based RAG agent
Implement agent-to-agent communication where one retrieves, another summarizes, and a third decides actions

Fine-Tuning and Pretraining Language Models

Difference between Pretraining, Fine-Tuning, Instruction Tuning
Fine-Tuning Open Source Models (LLaMA, Mistral, Falcon)
PEFT Techniques: LoRA (Low-Rank Adaptation) Adapters, Prefix-Tuning
Tools: HuggingFace Transformers, PEFT, DeepSpeed, bitsandbytes

LLMOps & Production Deployment

Model Lifecycle: Dev, Eval, Test, Deploy, Monitor
LLMOps Stack: LangSmith, PromptLayer, WandB, Evidently, TruLens
Logging, Prompt Versioning, Evaluation Metrics
CI/CD for Prompts and Chains
RLHF, Feedback Loops, Guardrails
Combining RAG, Agentic AI, and Fine-tuned models
Deploying LangChain applications on: Streamlit, Solara, FastAPI, Gradio
CI/CD Pipelines for LLMOps
Monitoring and Cost Management (OpenAI + LangSmith + Tracing)
Build an End-to-End Custom Knowledge Assistant:
Ingest PDFs/Docs → Chunk and Embed → Vector DB Query using Conversational Agent (RAG + Tools + Planner) → Deploy using Solara

Corporate Clients

Why Companies Choose BEPEC?

Founded and led by industry practitioners, BEPEC began as a specialist Data Science and Generative AI training institute and has grown into a trusted partner for companies building in-house AI capability. Our trainers aren’t career lecturers — they’re engineers and data scientists who build production AI systems and teach from real experience.

That practitioner-first DNA is what sets us apart as a corporate AI training provider. When your teams learn from people who’ve actually shipped LLM applications, RAG pipelines, and agentic systems, the learning sticks — and translates into results.

Practitioner-led instruction: taught by people who build AI, not just teach it.
Role-based programs: tailored tracks for executives, managers, engineers, and non-technical staff.
Hands-on from day one: real projects and tools, not theory.
Customized to your business: curriculum mapped to your stack, data, and industry.
Proven scale: 30,000+ professionals trained and 500+ hiring partners.
Cutting-edge curriculum: Generative AI, Agentic AI, LangChain, LangGraph, RAG, and more.
End-to-end support: from discovery to post-training follow-up.

Our Corporate AI Training Programs

Generative AI for Business Teams A non-technical, hands-on program teaching employees to use generative AI to write, summarize, analyze, and automate everyday work — safely and effectively. No coding required.

Agentic AI & LLM Engineering A deep technical program for developers and data teams covering large language models, retrieval-augmented generation (RAG), multi-agent systems, LangChain, LangGraph, evaluation, and observability — built on production-grade projects.

Data Analytics & Data Engineering A deep focus on analytics and data engineering to build analytical and ETL Pipelines customised for your teams

AI for Leaders & Decision-Makers A focused executive program on AI strategy, high-ROI use cases, governance, risk, and building an organization-wide AI roadmap.

Custom Corporate Workshops Have a specific goal? We design the curriculum around your stack, data, and industry — from a half-day workshop to a multi-week cohort.

How We Work

Discovery call — We learn your team, tools, and goals.
Custom curriculum design — We map a program to your specific outcomes.
Delivery — Live online or on-site, with hands-on labs and projects.
Measurement & support — Assessments, feedback, and follow-up so skills last.

Meet Your Trainer: Kanth

International Corporate Trainer with 10+ years of extensive experience delivering advanced training across Generative AI, Agentic AI, Machine Learning, and Deep Learning, empowering professionals to build and deploy real-world AI solutions.
Designed and delivered customised corporate training programs for Fortune 500 clients and global EdTech partners, covering Generative AI, Agentic AI, Machine Learning, Data Engineering, and Big Data ecosystems.
Conducted hands-on workshops for Virtusa, Merck, Walmart, Bank of America and Barclays, enabling teams to build robust Data Science, Generative AI, Deep Learning, AI, ML pipelines, adopt MLOps best practices, and deploy scalable models in regulated environments.
Led cloud migration and data modernisation bootcamps for Walmart and EY, upskilling teams in Azure Data Factory, Databricks, Snowflake, and Google Cloud Platform, resulting in improved project efficiency.
Trained Suzlon’s engineering teams on leveraging Big Data frameworks (Hadoop, Spark) for real-time analytics and operational excellence in renewable energy data streams.
Enabled EXL Services and The Math Company data professionals to master Data Science, Deep Learning, Advanced Data Analytics, BI tools (Power BI, Tableau), and SQL optimisation, enhancing client reporting capabilities.
Delivered Generative AI and LLM Orchestration sessions using LangChain, Prompt Engineering, and RAG pipelines using Amazon Bedrock empowering participants to build Agentic AI solutions for intelligent automation.
Served as a Lead Trainer for Purdue University PG Program, mentoring post-graduate learners in AI, Data Science, and Cloud Computing, bridging the gap between academic theory and industry practice.