AI SOLUTIONS ARCHITECTURE
TUESDAYS & THURSDAYS
5 PM GMT
26 NOV 2024 - 6 FEB 2025
DURATION:
8 WEEKS
TUESDAYS & THURSDAYS
5 PM GMT
Advance your skill set. Get AI specific with solutions architecture, and gain a deeper understanding of leveraging machine learning algorithms and handling big data.
Explore AI Solution development with Toby Fotherby, Senior AI/ML Specialist Solutions Architect at AWS, for an AI Solutions Architecture course designed to help you formulate solutions and set up AI pipelines.
WHO THIS COURSE IS FOR
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YOU ARE A DATA SCIENTIST OR ENGINEER
Moving into AI Architecture from your current field could be the best career decision you make. Learn how to integrate AI into your work and gain a hands-on understanding of the capabilities, challenges and application patterns for AI/ML integrations.
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YOU ARE A TECHNICAL PROJECT MANAGER
Get equipped with the skills you need to pivot into a role requiring generative AI knowledge and get confident in your ability to offer AI solutions. Gain a deeper conceptual understanding of what it takes to create a quality product as you move into AI solutions architecture.
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YOU ARE A CLOUD SOLUTIONS ARCHITECT
You’re already working as a solutions architect, but how can you better use AI within your projects? Get the most up to date expert knowledge, and implement what you learn directly into your current role. Get the chance to complete a capstone project based on a solution proposal.
AI is projected to contribute over $15.7 trillion to the global economy.
Drive innovation in your business with an in-depth understanding of AI technologies and their applications. Learn to effectively identify issues and present AI solutions - optimising costs and enhancing performance.
Learn from industry experts. Enhance your skill set.
Live, online lessons will be delivered alongside 8 hands-on lab sessions and fireside chats with elite guest speakers. Complete a final capstone project and receive 1:1 mock interview practice, alongside the chance to network with other AI professionals.
Become efficient in identifying issues and proposing solutions. Complete 10 assignments designed to consolidate your learning around becoming an AI solution architect. You’ll get the opportunity to work on a capstone project divided into 4 key stages including developing a proof of concept and wrapping up with a final solution proposal.
Learn from some of the key players in this fast and continually evolving industry. In addition to twice weekly lessons with an industry expert, you’ll be exposed to guest speakers covering topics including designing, communicating and delivering solutions.
Proposing solutions is one thing, but gaining practical experience in order to execute your work effectively takes you to a whole new level. You’ll take part in 8 meticulously designed labs throughout the course to help you put your new skill set into practice.
Toby Fotherby
LinkedIn Profile- Senior AI/ML Specialist Solutions Architect at AWS
- Over 15 years experience developing software and data solutions
- Has worked for companies including Teradata and Autodesk
- Enjoys helping to generate sustainable value, deliver new revenue streams and delight end-users
- Has a keen interest in AI for Social Good
- Holds a Bachelor degree in Psychology, a Master’s Degree in AI with the University of Cambridge and is finishing a Master’s in Data Science at the University of Edinburgh
COURSE INTRODUCTION
Let’s connect! Meet your instructor, Toby Fotherby, Senior AI/ML Specialist Solutions Architect at AWS. Get familiar with the course and ask any questions you have.
- Instructor Introduction
- Course Objectives & Flow
- Q & A
Explore the diverse responsibilities of an AI Solutions architect - and start to understand how these vary across companies, sectors and specialisations. Grasp the importance of collaborating with peers including data engineers and data scientists.
- Role & Responsibilities of an AI Solutions Architect
- Peer roles
- Core skills & tools
- Exploring your goals
Assignment #1: Self assessment and identifying focus areas for growth
Dive into fundamental AI/ML algorithms and concepts, covering key topics such as classification and regression techniques. Solidify your understanding of foundational concepts and get hands-on with a lab session to set up your AI/ML environment.
- Classification
- Regression
- Lab: Setting up your AI/ML environment
Assignment #2: Establishing your AI/ML development environment
Get to the essence of supervised and unsupervised learning, delving into core concepts and tools essential for model training. Get practical experience in training a regression model.
- Supervised and unsupervised learning
- Model Training: Core concepts & tools
- Lab: Training a regression model
Assignment #3: Training a regression model: Predicting house prices
Lean in and focus on advanced concepts such as clustering, forecasting, recommendation engines and neural networks. Deepen your understanding of complex algorithms and their applications. Learn to train a clustering model, further enhancing your practical skills.
- Clustering
- Lab: Training a clustering model
- Forecasting & recommendation engines
- Neural networks, CNNs & Transformers
Explore Natural Language Processing and document processing techniques then engage in a lab session designed to consolidate your understanding. Delve into core concepts and tools for image processing to broaden your skill set in both textual and visual data analysis.
- Natural Language Processing
- Document processing
- Lab: Document processing
- Image processing: Core concepts & tools
Assignment #4: Document processing
Uncover the significance of embeddings and their utility in various applications. Take a dive into vector databases, gaining insights into efficient data storage and retrieval techniques in high-dimensional spaces.
- What are embeddings and why are they useful?
- Sentence embeddings
- Lab: Comparing sentences using embeddings
- Vector databases
Explore and assess the landscape of quick wins and challenges within generative AI applications. Cover application architecture fundamentals and engage in a hands-on lab session focused on prompt engineering.
- Quick wins and challenges
- Application architecture fundamentals
- Lab: Prompt engineering
Assignment #5: Prompt engineering: sentiment analysis and entity detection
Get familiar with the fundamentals of application integration, essential for deploying generative AI solutions effectively. Participate in a lab session focusing on retrieval augmented generation techniques, and explore the development of GenAI chatbots and agents.
- Application integration: fundamentals
- Lab: Retrieval augmented generation
- GenAI chatbots
- GenAI Agents
Focus on how to achieve scalability and operational excellence in machine learning operations. Learn strategies and best practices to streamline ML workflows and ensure efficient and reliable operations throughout the model lifecycle.
- ML Ops, scalability and operational excellence
- Data pre-processing
- Model training
- Model evaluation
- Model deployment & monitoring
Navigate the principles and good practices crucial for successful AI/ML solutions. Explore what success entails in solution design and how to map requirements effectively to AI/ML components. Validate your designs and develop proof of concepts to ensure alignment with project objectives and stakeholders' needs.
- What does success look like?
- Mapping the requirements to AI/ML components
- Scale, pattern of use & level of detail
- Validating the design
- Developing a proof of concept
Assignment #6: Capstone project: Solution Proposal
Equip yourself with strategies for building resilient AI systems and optimising their performance and cost-effectiveness. Explore techniques for seamless model training and deployment, managing model and data drift, and enhancing inference processes.
- Model training and deployment pipeline
- Model and Data Drift
- Inference serving & pipelines
- Cost Optimisation
Assignment #7: Evaluating model price/performance
Address crucial aspects of data security and system scalability in AI applications. Understand how to secure data throughout the model training and deployment pipelines, as well as in application pipelines. Grasp the importance of factoring in scalability considerations, including handling big data in AI projects and facilitating interactive transactions at scale.
- Data security
- Scalability
- Big data and AI
- Interactive transactions at scale & autoscaling
Assignment #8: Capstone project: Develop proof of concept
Explore and engage. Explore the integration of cloud platforms and AI services to streamline model training and deployment processes. Leverage AI/ML tools available on cloud platforms for various tasks, and engage in a lab session focused on deploying models and conducting inference in a cloud-based environment.
- Cloud Platforms & AI Services
- AI/ML tools for model training and deployment
- Generative AI AI/ML tools for model training and deployment
- Lab: Cloud based model deployment and inference
Critical topics surrounding AI include ethical considerations, regulations and bias in models. Explore the ethical implications of AI technologies and regulations governing their use. Investigate methods for detecting and mitigating bias to ensure fairness in AI model development and deployment.
- Ethical Considerations & Regulations
- Bias and Fairness in AI Models
Assignment #9: Capstone project: Solution Proposal - Develop proof of concept
Hone your skills in effectively communicating AI solutions to stakeholders. Get pitch perfect with presentation techniques tailored for showcasing AI solutions. Explore strategies for successful solution delivery, ensuring alignment with stakeholders' expectations and project objectives.
- Communicating AI Solutions to Stakeholders
- Presentation Skills for AI Solutions
- Demo: Presenting an AI Solutions Architecture
- Delivering solutions
Assignment #10: Capstone project: Solution Proposal - Wrapping it up!
Get ahead with current trends and future outlooks in AI/ML and Generative AI fields. Receive the guidance you need to tailor your CV and interview preparation for AI roles. Engage in Q&A sessions to showcase and discuss your capstone project, preparing you for success in your AI career journey.
- AI/ML and Generative AI: Trends and outlook
- Interview & Resume Guidance
- Ongoing Self-Education
- Capstone Project Q&A
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