Enterprise AI Solutions Architecturein Ahmedabad

40 Hours/5 daysLive virtual trainingIntermediate

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Ahmedabad, India. Dates, seating capacity, facilities and the final venue are confirmed before booking.

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Ahmedabad combines regional culture, business activity and growing demand for professional skills.

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Explore Enterprise AI Solutions Architecture

Explore enterprise AI strategy, architecture design, and intelligent system integration with StepMerit's Enterprise AI Solutions Architecture course. Learn to design scalable AI solutions, integrate Generative AI and large language models, evaluate cloud platforms, and address security, governance, and responsible AI requirements. Develop practical knowledge to align AI technologies with business objectives and enterprise architecture standards through industry-focused training in Ahmedabad, India.

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Learning from Ahmedabad

Ahmedabad combines regional culture, business activity and growing demand for professional skills.

Course curriculum

Module 1: Introduction to Enterprise AI Solutions Architecture
- Understand enterprise AI architecture concepts, business use cases, and the role of AI in digital transformation.
- Explore the relationship between business strategy, enterprise architecture, and AI adoption.
Module 2: AI and Generative AI Technology Foundations
- Learn the fundamentals of artificial intelligence, machine learning, large language models (LLMs), and Generative AI.
- Explore common enterprise AI applications, capabilities, and technology limitations.
Module 3: Enterprise Requirements and AI Use Case Identification
- Identify business problems that can be addressed through AI-powered solutions.
- Translate business requirements into functional, technical, and operational architecture requirements.
Module 4: Enterprise AI Architecture Patterns
- Explore common architecture patterns for AI applications, intelligent assistants, predictive systems, and enterprise automation.
- Understand the roles of data sources, models, APIs, application services, and user interfaces.
Module 5: Data Architecture for Enterprise AI
- Examine structured and unstructured data sources, data pipelines, data preparation, and data quality.
- Understand data integration, metadata management, data access, and governance considerations.
Module 6: Large Language Model and Generative AI Integration
- Explore methods for integrating LLMs into enterprise applications through APIs and model services.
- Understand prompt management, context handling, model selection, and response validation.
Module 7: Retrieval-Augmented Generation and Enterprise Knowledge Systems
- Learn the architecture of Retrieval-Augmented Generation (RAG) systems for enterprise knowledge access.
- Explore document ingestion, embeddings, vector databases, retrieval pipelines, and grounded response generation.
Module 8: Cloud and Hybrid AI Architecture
- Understand cloud-based AI services, compute resources, storage, networking, and deployment options.
- Explore public cloud, private infrastructure, and hybrid architecture considerations for enterprise AI workloads.
Module 9: API Management and Enterprise System Integration
- Learn how AI services communicate with enterprise applications through APIs and integration layers.
- Explore authentication, service orchestration, data exchange, and interoperability considerations.
Module 10: AI Security, Privacy, and Responsible AI
- Examine identity management, access controls, data protection, and secure AI application design.
- Understand bias, explainability, transparency, governance, and responsible AI practices.
Module 11: AI Deployment, Monitoring, and Lifecycle Management
- Explore deployment strategies, application observability, performance monitoring, and error handling.
- Understand version management, model updates, reliability, and continuous improvement.
Module 12: Scalability, Reliability, and Cost Optimisation
- Learn architecture considerations for workload scaling, availability, latency, and fault tolerance.
- Evaluate infrastructure usage, model costs, resource allocation, and operational efficiency.
Module 13: Enterprise AI Architecture Documentation and Evaluation
- Develop architecture diagrams, component descriptions, integration flows, and technology decision records.
- Assess solutions against business requirements, security expectations, scalability, and operational constraints.
Module 14: Capstone Project – Design an Enterprise AI Solution
- Develop a reference architecture for a business use case, such as an AI-powered knowledge assistant or customer support solution.
- Identify data sources, AI services, integration points, security controls, deployment options, and evaluation criteria.
- Present the architecture design, key trade-offs, implementation roadmap, and proposed success measures through StepMerit's Enterprise AI Solutions Architecture training in Ahmedabad, India.

Exam details

Exam Level: Intermediate – Enterprise AI Solutions Architecture
Exam Type: Online assessment consisting of multiple-choice questions (MCQs) and scenario-based architecture questions.
Number of Questions: 40 questions.
Exam Duration: 90 minutes.
Passing Score: 70% (28 out of 40 questions).
Exam Language: English.
Scenario-Based Evaluation: Assess the ability to analyse enterprise requirements, select suitable AI architecture patterns, identify integration dependencies, and evaluate scalability, security, and operational considerations.
Evaluation Criteria: Conceptual understanding, architectural decision-making, technology selection, risk awareness, and alignment of AI solutions with business requirements.
Certification: StepMerit course-completion certificate, subject to the applicable course and assessment requirements.
Exam Preparation: Review enterprise AI architecture patterns, RAG workflows, cloud deployment options, data governance, responsible AI, system integration, and architecture documentation.
Training and Assessment Support: StepMerit provides learning guidance and assessment preparation through its Enterprise AI Solutions Architecture training in Ahmedabad, India.

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Who this course is for

Enterprise Architects AI Solutions Architects AI and Machine Learning Engineers Generative AI Developers Cloud Architects Software Architects Data Architects IT Consultants Technology Managers Technical Project Managers Digital Transformation Professionals Business Analysts Enterprise Application Developers IT Professionals Seeking Enterprise AI Architecture Skills in Ahmedabad, India

Before you begin

Enterprise Architecture Fundamentals – Understand basic enterprise architecture concepts, business capabilities, application architecture, and technology planning. Artificial Intelligence Fundamentals – Familiarity with AI concepts, common AI applications, and the role of AI in business operations. Generative AI and Large Language Models (LLMs) – Understand the fundamentals of Generative AI, LLMs, prompt engineering, and AI-powered applications. Cloud Computing Fundamentals – Basic knowledge of cloud platforms, computing resources, storage, networking, and cloud-based application deployment. Software Architecture and Design Principles – Familiarity with application components, APIs…

Learning outcomes

Problem Statement A banking organisation receives a large volume of loan applications and supporting documents, including income statements, identity records, credit reports, and financial declarations. Processing these applications involves multiple systems, manual document reviews, compliance checks, and coordination between banking teams. The bank wants to explore an Enterprise AI solution architecture that can support document classification, information extraction, employee assistance, and workflow automation while maintaining data privacy, regulatory compliance, security, and human oversight. The architectural challenge is to design a solution that integrates AI services with existing…

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Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course expertise

Professional Experience: 5 years of relevant experience in AI, machine learning, data engineering, or related technologies. Training Experience: 150+ hours of technical training delivered. Candidates Trained: 200+ learners trained in AI, machine learning, Python, databases, or related technologies. Technical Expertise: Vector databases, embeddings, similarity search, vector indexing, metadata filtering, and semantic retrieval. Platform Knowledge: Pinecone, Weaviate, and Milvus, based on the trainer's actual hands-on experience. RAG Integration: Understanding of how vector databases support document retrieval, context enrichment, and LLM-powered applications. Practical Training Approach…

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Insights for Enterprise AI Solutions Architecture

Learning insight 01

Enterprise AI Solutions Architecture: Designing Scalable AI Systems for Modern Businesses

Artificial intelligence is transforming how enterprises manage information, automate workflows, analyse data, and improve business operations. As organisations adopt Generative AI, large language models (LLMs), and intelligent automation, they need well-designed architectures that integrate AI capabilities with…

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Learning insight 02

SAFe® for Architects Certification: Insurance Industry Case Study on Enterprise Architecture Transformation

Modern enterprises depend on scalable technology platforms, integrated applications, and reliable software delivery processes to meet changing business requirements. As organizations adopt Agile methodologies and digital transformation initiatives, they need experienced architects who can align technical architecture…

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