About the Client
Tiger Analytics is a leading global analytics and AI consulting firm, partnering with Fortune 500 companies to drive data-driven transformation. With a strong presence across the US, India, and key global markets, the company specializes in advanced analytics, machine learning, and AI-led business solutions across industries such as retail, BFSI, healthcare, and CPG. Known for its deep domain expertise, strong engineering capabilities, and outcome-focused delivery, Tiger Analytics has established itself as a trusted partner in enterprise-scale analytics and AI transformation.
Workshop Objective
The objective of this program was to build advanced capability within the Azure team to design, develop, and deploy Generative AI solutions using cloud-native architectures and modern machine learning frameworks.
The program focused on:
- Strengthening understanding of GenAI architectures and Azure-based AI ecosystems
- Enabling participants to build and deploy scalable ML and AI pipelines
- Developing expertise in working with large language models and transformer-based architectures
- Enhancing capability in data processing, model training, and deployment workflows
- Building hands-on experience in integrating AI models into enterprise applications
- Driving production-ready AI implementation aligned to business use cases
Workshop Summary
This 40-hour virtual program (delivered in structured 4-hour sessions) was designed as a deep, technical, and application-driven intervention for Azure teams. The workshop followed a progressive learning approach starting from foundational AI and ML concepts, advancing into model building, and culminating in deployment and real-world application. The learning methodology emphasized hands-on coding, guided labs, and real-world use cases, ensuring participants could build and deploy AI solutions independently. The program focused on enabling execution at scale, rather than just conceptual understanding.
Key Highlights:
- Hands-on labs on building and training machine learning models using TensorFlow and Azure ML
- Real-world applications of GenAI models including BERT and transformer-based architectures
- Practical exercises on designing and deploying end-to-end ML pipelines
- Simulation-based activities for model optimization, evaluation, and performance tuning
- Exposure to AutoML techniques for faster model development and experimentation
- Exercises on integrating AI models into enterprise workflows and applications
- Group-based problem solving to address real-world data and AI challenges
- Development of production-ready solutions aligned to business use cases
Workshop Details
- Mode: Virtual / Online
- Audience: Azure Team (Data & AI Engineers)
- Batch Size: 30 Participants
- Duration: 40 Hours Program (4 Hours per Session)
- Customized Training Modules
- Certificates for all participants
Trainer Profile
Ganapathy Shankar is a highly experienced AI and machine learning expert with over 30 years of experience in advanced analytics and deep learning.
- Extensive expertise in building custom ML models, layers, and loss functions using TensorFlow
- Strong experience in designing and deploying ML pipelines using advanced architectures such as BERT
- Expertise in AutoML, deep learning, and computer vision applications
- Experience in AI applications across domains including healthcare and enterprise analytics
- Certified across leading platforms including Coursera, Udacity, and DeepLearning.AI
- Proven ability to deliver deep technical training programs focused on real-world implementation
- Known for enabling teams to move from experimentation to production-ready AI solutions
His deep technical expertise combined with practical implementation experience makes him highly relevant for organizations aiming to scale AI adoption and build robust, enterprise-grade AI systems.