Lokesh Learning Journals

Studying for the Claude Architect - Foundations certification is an eye-opening experience. It shifts your perspective from seeing AI as a simple conversational "chatbot" to understanding it as a highly structured, agentic thinking partner capable of tackling complex, multi-layered enterprise workflows.  Claude, developed by Anthropic, is built from the ground up to be helpful, harmless, and honest. Rather than simply generating text, Claude is engineered to act as an active collaborator that can integrate directly into your codebase, connect seamlessly with your data, and scale securely. In this article, we will look into:  what is Claude? How does the core concept of "AI Fluency" redefine our human-AI collaboration? And how can we leverage workspaces, skills, and connectors to build an optimal environment for Claude to work alongside us? 1. AI Fluency: Frameworks and Foundations To get the most out of AI, we must shift our mindset from treating it as a basic utili...

 The Claude Architect path is designed to move you beyond basic prompting into the realm of professional AI architecture. It bridges the gap between high-level conceptual understanding and technical deployment.Recommended Learning Path: Foundations: Mastering the "AI Fluency" framework and mindset. Claude 101: Deep dive into Claude’s unique capabilities. Infrastructure: Integrating Claude with AWS Bedrock and GCP Vertex AI. Development: Building with the Claude API and exploring the Model Context Protocol (MCP). Practical Application: Putting it all together with Claude Code in real-world development scenarios. In this article, we will look into: What is AI Fluency? How does the Claude Architect framework shift your mindset from viewing AI as a simple tool to a collaborative partner? And what are the core pillars of effective, ethical, and safe AI interaction? Decoding AI Fluency Many view AI as a "hammer"—a tool you pick up to hit a nail and put down. AI Fluency ...

Cloud computing has transformed how we build, deploy, and manage applications. At the center of this transformation is Amazon Web Services (AWS), the world’s most comprehensive and broadly adopted cloud platform. This guide provides a concise overview of the core components that make up the AWS ecosystem. In this article, we will look into the fundamental questions that define your journey with the AWS Cloud:  What is compute, and how do you choose between virtual machines, containers, or serverless options? How does networking define the structure of your virtual environment? What are the differences between block, file, and object storage, and which one does your application need? Finally, how do you determine which database solution—relational or purpose-built—is the right fit for your specific use case? 1. The Foundation: Regions and Security Every cloud application relies on physical infrastructure—data centers and network connectivity. AWS organizes these into Regions , which...

Data has always been dubbed as the "new oil". A very fitting anology. Just as with oil, data is useless  and even dangerous in its raw form.  Just like with oil, which needs to  be refined, transported safely, and delivered to the right place at the right time in order to useful, data must go through the similar process. This is where the Data Engineer comes in. While Data Scientists are like chefs who create a masterpiece meal (the insights and AI models), Data Engineers are the architects and contractors who build the industrial kitchen. They ensure the water lines are pressurised, the electricity is stable, and the ingredients arrive fresh and sorted every morning. In this article, we will have a quick look into:  What is data engineering? what are data pipelines? what is ETL and ELT? why is data quality important? etc. 1. The Core Mission: Building the Pipes The primary responsibility of a data engineer is to build the infrastructure and reliability required f...

RAG( Retrieval Augmented Generation) is an important component and enabler of efficient Gen AI solutions nowadays. Foundation Models are essentially learned or pre-trained Deep Neural Networks. They can act as the thinking brain but they have some significant disadvantage.   One is "hallucinations" whereby they generate confident but false information,  and the second is the "knowledge cutoffs" (a lack of awareness of events or data after their training period). In this article, we will look into What is RAG? Why do we need it? what advantage do they serve? what is the typical process of a RAG looks like? What is RAG? RAG resolves both these issues by allows the FMs (brain) to ensure that whatever information that it is retrieving and generating is based on grounded organisational document. For example, if we are building a chatbot for a hotel today on our hotel policies, the model itself would not know about the hotel or its policies, building a RAG on top of it wh...

In modern analytics, efficiently organising and connecting business data is essential for meaningful insights. Dimensional Modelling schemas such as the star and snowflake schema are power data models that can be used to building high-speed analytical databases in platforms like snowflake.  In this article, we will look into: what is dimensional modelling? What are the core components of dimensional models? What are the different schemas in dimensional modelling? When to use which schema? What is Dimensional Modelling? Dimensional modelling: - is a design methodology specifically geared for data warehouses and business intelligence (BI) systems - were the main goal is to organise data so it can be queried quickly and intuitively - empowering both simple reporting and complex analytics.  Key Features of dimensional modelling 1. Simplifies data structures for end-users 2. Supports efficient aggregations and slicing/dicing of data 3. Optimised for read-heavy analytical workloads ...

Data Ingestion and data engineering is an important aspect of being a good AI Engineer. Data is fundamental for AI/ML projects today.  Snowflake is an important asset or toolkit for AI Engineering and other data analytics projects.                                In this article, we will look into: What is Snowflake? What is data modelling? Why do we need data modelling and what is the importance of it? What are the layers in data modelling? What is data normalisation and the steps in it? What is Database Design? What is Snowflake? Snowflake is a cloud-based data warehouse platform crafted for modern analytics. Its strengths: - Handles both structured and semi-structured data - Separates compute and storage for scalability and cost efficiency Data can reach Snowflake via: 1. Batch loads (e.g., COPY INTO from cloud storage) 2. Continuous ingestion (Snowpipe) 3. ETL/ELT tools (Fivetran, dbt, Informatica) 4...

 AI Engineer is probably one of the most popular roles right now. Many people aspire to be AI Engineer without fully understanding on what the role is about.  In this article, we will look at: What is an AI Engineer? What an AI Engineer does? How is different from the other AI roles that people might get confused with? AI Engineer: - is responsible for designing and developing AI Models that can be used in various applications - work across different stages of the AI Development life cycle, including data collection, pre-processing, model training, and deployment - integrate the AI models into existing systems and ensuring they function correctly with a broader architecture - also fine-tune models for efficiency and accuracy What is the value that AI Engineers bring to an Organisation? That question must be answered by what is the value that AI brings to an organisation. Ultimately, for any organisation where AI is not the product or service that they provide, it is about 2 th...

We are probably at one of the most crucial moments of human history. Like the moments before final showdown between the Allies and Axis Power. The birth and death of Jesus.  AI might be the defining moment of the human race. How far we go? What decisions we make? I have been reading and learning about AI for the past few months. This article is on my understanding and take on AI. What is AI? What is Intelligence? Is AI truly an Intelligence? What is AGI? Can AI reach the level of true intelligence? What is AI?  AI is the an branch/application of data science which mimics human cognitive functions by processing vast amounts of data to identify statistical patterns. At its core, an AI is a complex mathematical function. A network of billions of "weights" (learned patterns) that determine how input (a question) is transformed into output (like an answer).  So, is AI truly an intelligence? Before, we look at that, we need to understand on what is Intelligence. ...