MICRO DEGREE
AI Solutions Architect
Become a professional AI Solution Architect
in just 8 weeks!
Application closes on 10 Oct 2025
Become a professional AI Solution Architect
in just 8 weeks!
100% Moneyback Guarantee
Reserve your spot today!
Basic Info
Select Offers
Application closes on:10 Oct 2025
Get instant access of pre-course material!
What is in it for you?
Shape the future with AI Solution Architect
Live Classes
Instructor-led Live Sessions
Attend 4 weeks of instructor led live classes at the lowest price ever
Projects & Case Studies
Projects & Case Studies
Gain hands-on experience with projects and real-world case studies for impactful learning
Verified Certificate
Verified Certificate
Earn a industry recognized certificate and kick start your career
Session Recordings
Session Recordings
Revisit older chapters anytime with recorded sessions
Flexible Schedule
Flexible Schedule
Choose live classes from different cohorts that fit your availability.
Study Materials
Study Materials
Access comprehensive study materials designed to enhance your learning experience
Grab your slot before the offer expires
Discount applicable for next
10 candidates only
Grab your slot before the offer expires
Discount applicable for next 10 candidates only.
Reserve your spot today!
Basic Info
Select Offers
Application closes on:10 Oct 2025
Get instant access of pre-course material!
Top 1% Industry Experts – Sr. Managers, VPs, CXOs, Directors & Founders from companies shaping the future.
Combo Offers
Create Your Own Combo
Reserve your spot today!
Curriculum
A Curriculum designed for your success
Live Sessions Schedule 
Sat - Sun (Weekends Only) 
Timing: 8 AM - 10 AM EST / 9 AM - 11 AM EST
- The evolution of AI roles: from data scientist to solution architect
- Responsibilities, deliverables, and stakeholder management
- Translating business needs into AI technical solutions
- AI project lifecycle and success metrics (KPIs, ROI, SLAs)
- Common architecture archetypes and solution design patterns
- Skills matrix and career roadmap for an AI Solution Architect
- Identifying and prioritizing AI opportunities within organizations
- Business case development and ROI analysis for AI initiatives
- Stakeholder alignment and communication frameworks
- Defining problem statements, constraints, and measurable success criteria
- Building AI adoption roadmaps and maturity models
- Risk, compliance, and ethical trade-off decisions at design time
- Data sourcing: internal, external, and synthetic data pipelines
- Data quality, profiling, and validation automation
- Data governance, lineage, metadata, and cataloging tools
- Designing data architectures: data lake vs data warehouse vs vector store
- Feature engineering, feature stores, and reusable feature patterns
- Privacy, compliance, and data security best practices
- Model taxonomy: ML, DL, foundation models, multimodal models
- LLMs and RAG (Retrieval-Augmented Generation) architectures
- Model selection criteria: accuracy, interpretability, latency, and cost
- Evaluation methods: offline metrics, online testing, A/B, and drift evaluation
- Model optimization: pruning, distillation, quantization, and acceleration
- Explainability and bias detection: SHAP, LIME, and fairness metrics
- AI system design principles: scalability, fault tolerance, and modularity
- Core architecture styles: microservices, event-driven, streaming, and APIs
- Batch vs online vs hybrid AI workflows
- Designing RAG pipelines: vector stores, embedding caches, retrieval APIs
- Interfacing with external systems and APIs (CRM, ERP, IoT, etc.)
- Security design: IAM, zero trust, encryption, and auditability
- Compute options: CPU, GPU, TPU, distributed and edge compute
- Infrastructure-as-Code (Terraform, CloudFormation, Bicep) for reproducibility
- Storage and data management layers for AI (object, block, vector)
- Containerization and orchestration (Docker, Kubernetes, Ray)
- Cost modeling, resource optimization, and elasticity in cloud infra
- Multi-environment design: Dev → QA → Prod isolation and governance
- MLOps lifecycle: data, model, and code versioning
- CI/CD for ML pipelines: build, test, deploy, promote
- Experiment tracking and reproducibility (MLflow, Vertex Pipelines, etc.)
- Model registry, approval gates, and governance integration
- Deployment strategies: shadow, canary, blue-green, and rollback
- Feedback loops: monitoring, retraining, and human-in-the-loop systems
- Model serving architectures: batch, real-time, streaming, and edge
- Serving frameworks: FastAPI, Triton, KFServing, Ray Serve
- Scaling patterns: autoscaling, caching, sharding, and load balancing
- Latency and throughput optimization: batching and vectorization
- Versioning and traffic management for experiments and rollouts
- Edge inference and hybrid deployments for IoT and low-latency apps
- Metrics collection: data, model, and business KPIs
- Drift detection: data drift, concept drift, and anomaly detection
- Logging, tracing, and distributed observability best practices
- Incident management, SLOs, and post-mortems
- Monitoring frameworks: Prometheus, Grafana, Evidently AI, etc.
- End-to-end health monitoring dashboards for AI systems
- Principles of responsible AI: fairness, transparency, and accountability
- Explainability frameworks and governance documentation (model cards)
- Regulatory frameworks: GDPR, CCPA, HIPAA, and ISO standards
- Bias detection, mitigation, and ethical review workflows
- Auditability and reproducibility pipelines for compliance
- Governance artifacts: risk matrix, audit logs, and decision documentation
- Cloud AI landscape overview: AWS SageMaker, Azure ML, GCP Vertex AI
- Mapping cloud services for compute, data, orchestration, and monitoring
- Designing ML pipelines using cloud-native components
- Security, IAM, and compliance across providers
- Multi-cloud orchestration, portability, and cost optimization strategies
- Cross-cloud reference architectures and real-world design trade-offs
- Building the AI business case: value realization and ROI modeling
- Organizational models for AI scale-up (Center of Excellence, federated)
- Procurement, vendor evaluation, and make-vs-buy decisions
- Cost, sustainability, and ethical considerations at scale
- AI delivery documentation: architecture deck, cost sheet, compliance report
- Capstone Project: Design an end-to-end AI solution from data to deployment
Course Includes
LIVE Interactive Sessions
Quizzes, Assignments & Projects
Study Materials & Session Recordings
Certificate
Instructors
Kunal S
20+ Years, Sr. Engineering Manager, Amazon
Ankit B
10+ Years, Ex-Amazon, Startup Founding Team
Course Includes
LIVE Interactive Sessions
Quizzes, Assignments & Projects
Study Materials & Session Recordings
Certificate
Course Pre-requisites
.ffb97545.png&w=16&q=75&dpl=dpl_V8TNZb3GgThjz7Yiya4C79wnb91S)
AI knowledge is required for this course.
Skills You Will Learn
- Designing end-to-end AI architectures that align with business goals and technical constraints.
- Building effective data strategies, pipelines, and governance frameworks for AI projects.
- Selecting and optimizing machine learning, deep learning, and LLM-based models for enterprise use cases.
- Implementing MLOps workflows including CI/CD, versioning, model registry, and automated deployment.
- Designing and managing AI infrastructure using cloud-native tools, containers, and Kubernetes clusters.
- Architecting and deploying AI solutions across AWS, Azure, and Google Cloud platforms.
- Ensuring responsible AI practices with fairness, explainability, and regulatory compliance.
- Monitoring, scaling, and maintaining production AI systems with observability and drift detection frameworks.
for successfully completing the 'AI Solutions Architect' course conducted from 14 Aug 2025 to 09 Oct 2025
Add a Industry Recognized
Certificate To Your Resume
Industry Recognized
Certificate
Learn the best from the best
Career Advancements
Elevate your career with a respected certificate
Industry Respect
Gain credibility in the field
Networking
Connect with experts and peers
Opportunities
Attract exciting job prospects and promotions
for successfully completing the 'AI Solutions Architect' course conducted from 14 Aug 2025 to 09 Oct 2025
100% Moneyback Guarantee
Top 1% Recruiters - Get interview access to 550+ Companies
Recommendations
Hear from our Learners
Looking for help? Here are our most frequently asked questions
What is a EdYoda Micro Degree?
EdYoda Micro Degree is an online, Live classroom based short-term course, where you get Live Sessions conducted by industry experts. It is designed to help you acquire practical & job-relevant skills quickly.
How do I register for the micro degree?
To register, visit the micro degree details page and fill up the registration form and make the payment to reserve your seat before the application closing date.
What happens after I register and pay?
After successful registration and payment, you will receive a confirmation email with instructions on how to access the online micro degree classes
Are there any pre-requisites?
All you need is a PC or Laptop to attend the online live classes and your commitment of 4 weeks. Apart from that there are no prerequisite for the Micro Degree.
What if I miss a live session?
We've got you covered! The session recording will be added automatically on the classroom platform after the session is ended.
Will I get a certificate after completion?
Yes. After successful completion of curriculum you will be provided a digital certificate which you can download and share with others.
What is a EdYoda Micro Degree?
EdYoda Micro Degree is an online, Live classroom based short-term course, where you get Live Sessions conducted by industry experts. It is designed to help you acquire practical & job-relevant skills quickly.
How do I register for the micro degree?
To register, visit the micro degree details page and fill up the registration form and make the payment to reserve your seat before the application closing date.
What happens after I register and pay?
After successful registration and payment, you will receive a confirmation email with instructions on how to access the online micro degree classes
Are there any pre-requisites?
All you need is a PC or Laptop to attend the online live classes and your commitment of 4 weeks. Apart from that there are no prerequisite for the Micro Degree.
What if I miss a live session?
We've got you covered! The session recording will be added automatically on the classroom platform after the session is ended.
Will I get a certificate after completion?
Yes. After successful completion of curriculum you will be provided a digital certificate which you can download and share with others.
General FAQs
Everything you need to know
