Certified Cloud AI Solutions Architect (CCASA) Crash Course

Explore AI and ML fundamentals, define core concepts and types, and map value propositions, use cases, and the landscape with OpenAI and AWS.

Explore core concepts in ai and ml, including deep learning, types and comparisons, nlp, open source libraries, algorithms, and languages like Python and R, with a whiteboard scenario.

Understand the difference between artificial intelligence and machine learning, their goals, and data-driven learning. See how AI and ML power real-world applications like content generation and recommendations.

Deep learning powers image recognition, natural language processing, and speech recognition by using multi-layer artificial neural networks to extract complex patterns from data.

Explore the three main machine learning types—supervised, unsupervised, and reinforcement learning—and understand when to use labeled versus unlabeled data for prediction, classification, and decision making.

Compare AI and ML, outlining scope and goals with examples like robotics and expert systems for AI and spam detection for ML, plus deep learning as a related concept.

Explore generative AI, predictive AI, and agentic AI, plus explainable AI and conversational systems. See examples like ChatGPT, Alexa, and dynamic routing.

Explore natural language processing, a branch of AI that structures unstructured data through tokenization and tagging to power machine translation, sentiment analysis, chatbots, voice assistants, and search engines.

Discover open source libraries and how non proprietary software, free access, and a large community accelerate AI development. Explore examples like TensorFlow, PyTorch, NLTK, Spacy, and Hugging Face.

Explore what algorithms are and how they learn from data through supervised, unsupervised, and reinforcement learning, then compare neural networks, including convolutional, recurrent, and transformer architectures with attention.

Explore ai-focused programming languages, with Python as the dominant library-rich option; compare R for statistics and data analysis, Java for enterprise nlp, and high-performance C++ for ai models.

Explore how AI models like Bard, ChatGPT, and Gemini operate using NLP, pre-training, and tuning, leveraging vast data sources and internet access to generate responses.

Explore how AI and ML create enterprise value by detailing benefits, four pillars of AI, data needs, and implementation strategies, with a high-level view of Google Cloud AI services.

Explore how AI and ML improve business decision making, predictive analytics, automation, and customer experience. Reduce costs and enable predictive maintenance while strengthening risk management and security.

Explore how artificial intelligence can enhance business processes across customer service, marketing, human resources, finance, and operations to boost efficiency, risk management, and faster go-to-market.

Explore AI/ML adoption challenges and limitations, from data quality and governance to integration and model drift. Learn to guide customers as a trusted advisor to anticipate risks and plan effectively.

Explore the four pillars of AI strategy—vision, value realization, risk management, and adoption plans—guided by Gartner, to map value, assess risks, and plan adoption for AI initiatives.

Data powers AI and ML, with quality, quantity, and characteristics driving training, accuracy, and reliable outputs, while proper acquisition, cleaning, and management prevent garbage in, garbage out.

Explore diverse data sources and APIs for ai/ml, learn to identify data types, quality, and formats, and connect to sources via api to feed ai training pipelines.

Learn to identify AI value, craft a value-driven roadmap, and visualize, quantify, and summarize ROI across measurable, strategic, and capability categories.

Explore Google Cloud's AI and ML value proposition with Vertex AI, a unified platform. Use notebooks, pre-trained models, and model registry for streamlined deployment on Google Cloud.

Demonstrates two free ai-powered value proposition canvas tools for crafting compelling proposals. Shows how to input the target market, obstacles, and solutions for rfp contexts, with a telecom software focus.

Explore the AI/ML landscape, key players, and cloud services like AWS, GCP, and Microsoft, with a whiteboard resume filtering demo and logistics and finance use cases.

Identify key players in the ai/ml market, including major tech giants, cloud providers, universities, and ai startups across use cases such as hr, logistics, and security.

Identify the three major cloud AI providers—AWS, Azure, and Google Cloud—and map their AI/ML service categories, including natural language processing, generative AI, and computer vision.

Analyze current and projected AI and ML market growth, compare CAGR trends, and assess market sizes from 2024 to 2030 to guide cloud AI solutions decisions.

Discover ai-powered resume filtering that uses NLP to compare resumes to job descriptions, score keyword matches, identify top candidates, and schedule interviews via zoom or online platforms.

Explore how AI and natural language processing filter resumes by comparing job description keywords to candidate resumes, generate match reports, rank candidates, and schedule interviews via zoom or online platforms.

Explore current AI and ML trends, from agentic AI and explainable AI to AutoML and interoperability, and learn how solutions architects address customer challenges with privacy, governance, and enterprise-focused insights.

Learn how JPMorgan Chase uses AI and ML for real-time fraud detection in finance, combining anomaly detection, biometrics, and NLP to improve accuracy, speed, and cost efficiency.

Explore AI foundations, machine learning types, and subfields like deep learning, generative and predictive AI, plus explainable and agentic AI. Emphasize data quality, AI project pillars, and business applications.

Review how AI mimics human intelligence, explainable AI, and deep learning, then the four pillars of AI strategy—vision, value, risk management, adoption—and the three ROI types: measurable, strategic, capability.

Explore sales engineering skills by defining sales engineering, examining procurement requirements and RFP fundamentals, and identifying sales engineering activities, especially in the US federal sector.

Explore what sales engineering is, including pre-sales and solutions engineering, and dive into cloud fundamentals for a pre-sales architect, covering service models, infrastructure, compute, network, security, and APIs.

Learn how sales engineering blends deep technical expertise with a hybrid, consultative approach to craft cloud and hardware and software solutions for targeted verticals, including demos and architecture design.

Compare pre-sales and solutions engineering to understand how they present value and pricing and how they deploy, train, and ensure customer success.

Explore cloud fundamentals: what the cloud is, service and deployment models, virtualization, and building blocks, and learn how cloud enables faster time to market and cost savings.

Explore cloud infrastructure concepts, including data location, regions and zones, availability zones, edge locations, and multi-zone deployments to achieve high availability and fault tolerance.

Explore cloud APIs as the on ramp to cloud services, and how API gateways, REST and SOAP standards, and cloud-native versus cloud-first design shape integration of on-prem and cloud environments.

Navigate cloud considerations in customer conversations, covering deployment and service models, privacy and costs, low ops options, and the shared security model with governance, integration, and migration strategies.

Learn to respond to customer procurement requirements by understanding requests for proposal, exploring proposal types and sections, and designing cloud fit solution architectures that align with customer needs.

Explore requests for proposals and the federal procurement process, including bidder and solicitor roles, the sections of an rfp, and how to craft a compelling technical response and pricing.

Develop a solid technical response to RFPs by tailoring proposals to the customer, asking the right questions, and aligning technical approach, procurement guidelines, and costing with sales and writers.

Design effective cloud solution architectures by collaborating with enterprise architects, ensuring compatibility with hybrid environments, open protocols, flexible costing, and strict procurement rules.

Define what a cloud fit is and how to match customer requirements to a cloud service, considering legacy data sources and on-prem integrations.

Develop analytical thinking by mastering active listening, precise problem framing, and collaborative problem solving with SMEs to identify causes and propose the right cloud solution the first time.

Explore why the U.S. federal procurement differs from commercial services by outlining government needs, market research, and justification, and how RFI, RFQ, and RFP shape proposals and the contract award.

Explore the roles and structure of proposal teams, including pink, green, blue, and white teams, and the pre-sales engineer's and SME's functions in crafting responses to an RFP.

Translate complex security needs into clear proposal language as a subject matter expert, focusing on federation, identity and access management, and integrating multiple solutions for government customers.

Search for federal contract opportunities on sam.gov and understand why this site is essential for a federal solutions architect pursuing federal government contracts.

Drive sales engineering activities for product and solution deals, align customer expectations with clear meeting agendas, demos, whiteboarding, and the Minto period, and address vendor cost optimization and collateral.

Close a solutions deal by aligning customer priorities with a clear sales plan and champion advocacy. Execute the plan and maintain revenue through ongoing support.

define expectations with customers, distinguish technical from business expectations (like pricing), meet what you can as a trusted advisor, and document commitments as the technical contact.

Define customer discussion agendas to meet expectations in every meeting. Set the agenda, cover topics like migrating from on prem to AWS, and break ice to build rapport.

Apply the pyramid principle to craft concise, jargon-free messages that lead with a core message, supported by key arguments and details addressing latency and scalability in enterprise networks.

Learn to design and validate proofs of concept (POCs) with a customer-focused workflow, scope, approvals, and resource planning to demonstrate a proposed cloud ai solution.

Demonstrate AWS cost management tools to show features, costs, and savings. Explore cost explorer, budgets, and anomaly detection, and review rightsizing with compute optimizer.

Master whiteboarding as an SE by setting a clear objective, using visuals, and communicating directly in short bursts, whether in person or online with Lucidchart, Zoom, or Miro.

Identify the right vendors for the right situation at the right cost by establishing preferred vendors and clear categories to optimize integration and margins.

Identify and use solution collateral, such as white papers, data sheets, case studies, graphics, and ebooks, to aid customer understanding and choose the right collateral for the right use case.

Explore the fundamentals of cloud computing, including NIST SP 801-45 characteristics, service and deployment models, virtualization, the shared responsibility model, and RFP processes and pre-sales proposal best practices.

Review cloud concepts via questions on service and deployment models, plan a POC with the account executive, define scope and success metrics, and recall API specification and interface.

Develop skills to interact with customers as a pre-sales solution expert, respond effectively, and deliver value during calls or site visits, while building trust to become a trusted advisor.

Explore customer management fundamentals for pre-sales success, covering customer acquisition, engagement, events, buyer types, discovery for procurement, and a win-win, solution-selling mindset.

Learn how to manage customers effectively by balancing introverted technical strengths with customer satisfaction, using a documented CRM-based strategy, training for the sales team, and defined interaction processes.

Discover how customer acquisition drives revenue by understanding the five-stage life cycle—awareness, appeal, act, advocacy—along with channels, acquisition strategy, and key metrics like sales pipeline, funnel, and forecast.

Enhance customer engagement through superior customer service, brand loyalty, and positive employee experience, using metrics like customer lifetime value, churn, and retention across the four-stage engagement lifecycle.

Attend customer events with confidence by engaging with attendees, gathering requirements, and presenting demonstrations; learn to use whiteboarding to outline solutions and address customer challenges.

Identify the ideal buyer and potential customers across SMBs, large enterprises, and government or educational sectors, then align sales focus to pursue viable deals.

Perform discovery to qualify sales leads using the Bant framework (budget, authority, need, timing) during discovery calls and ask about cloud environment, pain points, timeline, budget, and roadblocks.

Identify and qualify opportunities using the Bant framework; evaluate leads, prospects, budgets, and problems to determine if a customer becomes a sales qualified opportunity.

Learn to create a win win situation in negotiations by cooperating to meet interests and delivering value for both customer and vendor, including creative incentives like training or professional services.

Adopt the solution selling mindset to focus on customer needs, understand their organization, industry, and pain points, and assemble the right security tools and services that meet goals.

Become a trusted advisor by establishing customer trust through daily activities, handling objections, presenting business use cases and value propositions, and tailoring messages to executives with storytelling and enablement tools.

Become a trusted advisor who collaborates with clients to form strategic partnerships, delivering long-term value through insights, goal alignment, and exceeding needs to boost retention and forecasting.

Develop trusted advisor skills by applying enterprise experience, personable communication, reliability and promptness, and integrity, and honestly assessing capabilities to build strong client relationships.

Master objection handling by applying the four P's of no—personalization, perceived value, performance value, and proof—while listening, validating concerns, and using social proof and references.

Explain what a business use case is, why it matters for decision makers, and how it showcases costs, risks, TCO, and ROI to secure funding and inform procurement.

Study uplift metrics to shape business use cases that influence deals, using numbers, descriptive outcomes, industry jargon, conservative targets, and client collaboration with case studies like reduced tco by 70%.

Engage subject matter experts to address gaps beyond your knowledge, preparing ahead and bringing in the right SMEs for AI, insurance processes, DevOps, or mainframes. Leverage vendor resources and industry-specialized SMEs to anticipate questions and strengthen pre-sales credibility.

Deliver direct, metrics-driven executive messaging to the C-suite and board. Use visuals, targeted numbers, and a macro view of value within budget and timeframes while avoiding jargon.

Learn storytelling to engage customers, understanding their challenges and delivering a concise four-step process - hook, problem, sinker, and net - driving a demo, a POC, or a quote.

Explore sales enablement tools and SaaS CRMs like Salesforce, Zoho, and Monday.com, plus collaboration tools such as Miro and MS Teams, and ROI tools like Apptio or cloud ROI calculators.

Review key concepts in customer management, acquisition, trusted advisor roles, sales opportunities, and business cases with uplift metrics, objection handling, four P's, and storytelling.

Sign up for the certification exam on the digital institute site and complete ai ml fundamentals, solutions development and proposals, and customer relationship management.

Review key concepts from module review questions, including storytelling, the four p's of value, becoming a trusted advisor, and identifying the authority in a cloud migration scenario.