optimized images

docs/about-deepak-sood/meetups-talks-sessions.md

@@ -12,7 +12,7 @@ Introduction to Kong as an API gateway, key features, and benefits.
 
 Presentation - [Kong in Action: Simplifying API Management for modern Application](https://docs.google.com/presentation/d/1jOlgLiX3Bgu2daL7j8qPuuK4F8I8IOjplomguL0DQwU/edit?usp=sharing)
 
-![Simplifying API Management for modern Application - Kong](../media/Pasted%20image%2020241206150616.png)
+![Simplifying API Management for modern Application - Kong](../media/Pasted%20image%2020241206150616.jpg)
 
 Deepak Sood is a Senior AI, Data, and DevOps Architect with over 8 years of experience in designing, deploying, and optimizing scalable tech infrastructures. Specializing in DevOps, he has deep expertise in CI/CD pipelines, Kubernetes orchestration, and cloud platforms like AWS and Azure. His work includes automating deployments, implementing microservices, and ensuring infrastructure reliability through tools like Prometheus, Grafana, and Elasticsearch.
 
@@ -51,7 +51,7 @@ Deepak Sood is a Senior AI, Data, and DevOps Architect with over 8 years of expe
 - bit.ly/unsaid-session-14
 - [Session 2- Resource & Tasks - Cracking SDE - Big Tech](https://docs.google.com/document/d/1zL0WGVSkMjSDETfca43GTMCzJVmP40AlewAroIkpx7U/edit?usp=sharing)
 
-![Data Structures for Interview - UnsaidTalks](../media/WhatsApp%20Image%202024-11-30%20at%2009.18.59.jpeg)
+![Data Structures for Interview - UnsaidTalks](../media/WhatsApp%20Image%202024-11-30%20at%2009.18.59.jpg)
 
 ### Meetup Talk - Real-Time Data Warehousing Solution with AWS and Confluent Cloud - OpsTree Solutions (23 November 2024)
 
@@ -137,7 +137,7 @@ Proportion of students - 1st year + 2nd year (70%), 3rd + 4th year (30%)
 
 ![NIT Delhi - GenAI for Freshers](../media/Screenshot%202024-09-15%20at%2010.08.49%20PM.jpg)
 
-![NIT Delhi - GenAI for Freshers](../media/1726397196299.jpeg)
+![NIT Delhi - GenAI for Freshers](../media/1726397196299.jpg)
 
 [Deepak Sood on LinkedIn: #genai #techforfreshers #aiinnovation #careermoves #nitdelhi](https://www.linkedin.com/posts/deepaksood619_genai-aiinnovation-nitdelhi-activity-7241120393694994433-jsdz?utm_source=share&utm_medium=member_desktop)
 
@@ -177,7 +177,7 @@ Slides - [From Zero to Hero: Mastering GenAI in a Flash](https://docs.google.com
 
 "From Zero to Hero: Mastering GenAI in a Flash" is your one-stop session to dive deep into the world of Generative AI. We'll start by exploring **what GenAI is**, the **problems it solves**, and **where it all began**—taking you through its evolution. Then, we’ll glimpse into the **future of GenAI** and what’s on the horizon. In the technical deep dive, we’ll cover **prompt engineering**, the power of **LLMs (Large Language Models)**, the magic of **embeddings**, and how to supercharge your AI with **RAGs (Retrieval-Augmented Generation)**. It’s everything you need to go from zero to hero in GenAI!
 
-![From Zero to Hero: Mastering GenAI in a Flash](../media/Pasted%20image%2020240831144403.png)
+![From Zero to Hero: Mastering GenAI in a Flash](../media/Pasted%20image%2020240831144403.jpg)
 
 [Deepak Sood on LinkedIn: #genai #aiworkshop #futuretalent #innovation #hackathon #srmist…](https://www.linkedin.com/posts/deepaksood619_genai-aiworkshop-futuretalent-activity-7237536731544051712-QH0m)
 
@@ -241,7 +241,7 @@ Presentation - [Introduction to GenAI](https://docs.google.com/presentation/d/10
 
 Post - [Deepak Sood on LinkedIn: #genai #rag #artificialintelligence #datascience #aiinnovation…](https://www.linkedin.com/posts/deepaksood619_genai-rag-artificialintelligence-activity-7230291496627789825-T9hR)
 
-![Expert Speak - Empowering Gen AI with RAG](../media/WhatsApp%20Image%202024-08-10%20at%2016.09.32.jpeg)
+![Expert Speak - Empowering Gen AI with RAG](../media/WhatsApp%20Image%202024-08-10%20at%2016.09.32.jpg)
 
 ### Talk at Meetup Zero - GenAI Edition (3 August 2024)
 
@@ -256,9 +256,9 @@ Topic: Empowering GenAI with RAG (Retrieval-Augmented Generation)
 - How RAG is transforming industries by enhancing the capabilities of AI models.
 - Insights into implementing RAG to empower your AI solutions.
 
-![Meet Up Zero - Empowering GenAI with RAG](../media/Pasted%20image%2020240810153313.png)
+![Meet Up Zero - Empowering GenAI with RAG](../media/Pasted%20image%2020240810153313.jpg)
 
-![Empowering GenAI with RAG](../media/Pasted%20image%2020240810155153.png)
+![Empowering GenAI with RAG](../media/Pasted%20image%2020240810155153.jpg)
 
 [TensorFlow User Group Ghaziabad (TFUG Ghaziabad) on LinkedIn: #tfug #tfugghaziabad #tensorflow #genai #newevent #event #developers #gde…](https://www.linkedin.com/posts/tensorflow-user-group-ghaziabad_tfug-tfugghaziabad-tensorflow-activity-7223731628953460737-v3xA?utm_source=share&utm_medium=member_desktop)
 

docs/about-deepak-sood/projects/54-airflow-kafka-migration.md

@@ -71,7 +71,7 @@ The migration from Confluent Kafka and Airflow SaaS to on-prem setups on Linode
 
 ### Confluent Cloud to Kafka on K8s Migration
 
-![Kafka on Kubernetes](../../media/Pasted%20image%2020240712020317.png)
+![Kafka on Kubernetes](../../media/Pasted%20image%2020240712020317.jpg)
 
 To ensure zero downtime during the migration of a Confluent Cloud Kafka cluster to a Kafka cluster deployed on Kubernetes using Strimzi and Kafka MirrorMaker, here are the steps:
 
@@ -86,7 +86,7 @@ To ensure zero downtime during the migration of a Confluent Cloud Kafka cluster
 
 ### Astronomer to Airflow on K8s Migration
 
-![Architecture Diagram](../../media/Pasted%20image%2020240712020653.png)
+![Architecture Diagram](../../media/Pasted%20image%2020240712020653.jpg)
 
 To ensure a smooth migration from Astronomer to Airflow on K8s, here are the steps:
 

docs/about-deepak-sood/projects/55-azure-social-media-analytics.md

@@ -8,7 +8,7 @@ To achieve this, building a comprehensive social media monitoring platform is cr
 
 ## Architecture Diagram
 
-![Architecture Diagram](../../media/Pasted%20image%2020240712013958.png)
+![Architecture Diagram](../../media/Pasted%20image%2020240712013958.jpg)
 
 - **Data Ingestion:** The solution facilitates the ingestion of social media data from diverse sources such as Twitter, news feeds, and other APIs, ensuring a continuous flow of relevant data for analysis.
 - **Data Processing:** Once ingested, the data undergoes a series of processing steps facilitated by Azure services such as Azure Storage, Azure Synapse Analytics, Language Service, Translator Service, and Azure Maps. These services work in tandem to cleanse, transform, and enrich the data, ensuring its quality and enhancing its value through language detection, translation, and geographical enrichment.

docs/about-deepak-sood/projects/70-interview-datainsight-alerting-engine.md

@@ -144,7 +144,7 @@ Instruction
 
 ## Architecture
 
-![mqtt-alerting-engine](../../media/MQTT%20Alerting%20Engine.drawio.png)
+![mqtt-alerting-engine](../../media/MQTT%20Alerting%20Engine.drawio.jpg)
 
 - Alert mapping table - Main copy in RDBMS
     - Pushed copy in redis
@@ -178,4 +178,4 @@ Instruction
 
 ### Alerting Exceptions Handling
 
-![alerting-exceptions-handling-flow](../../media/Communication%20exception%20flow.png)
+![alerting-exceptions-handling-flow](../../media/Communication%20exception%20flow.jpg)

docs/about-deepak-sood/projects/80-stashfin.md

@@ -4,7 +4,7 @@
 
 [NBFC / Banking Terms](economics/fintech-nbfc-banking-terms.md)
 
-![stashfin-product-architecture](../../media/Pasted%20image%2020231201172554.png)
+![stashfin-product-architecture](../../media/Pasted%20image%2020231201172554.jpg)
 
 ## Processes
 

docs/about-deepak-sood/projects/86-stashfin-customer-support.md

@@ -47,7 +47,7 @@
 
 ### Creating and maintaining product roadmaps
 
-![product-roadmap-example](../../media/Pasted%20image%2020231201183958.png)
+![product-roadmap-example](../../media/Pasted%20image%2020231201183958.jpg)
 
 ## Links
 

docs/about-deepak-sood/projects/87-stashfin-team-management-culture.md

@@ -19,20 +19,20 @@
 - Atleast 2 approvals, one from senior dev and one from junior dev is mandatory for merging the code
 - Using Git and a proper PR process. Every feature or bug fix is a separate branch and submitted as a PR
 
-![stashfin-git-review-process](../../media/Pasted%20image%2020231201181214.png)
+![stashfin-git-review-process](../../media/Pasted%20image%2020231201181214.jpg)
 
 ### Scrum / Kanban / Project Management
 
 - Implemented Agile project management methodology across teams
 
-![example-scrum-board](../../media/Pasted%20image%2020231201181414.png)
+![example-scrum-board](../../media/Pasted%20image%2020231201181414.jpg)
 
 ## Documentation
 
 - Used a combination of google docs with team folders, etc
 - Introduced confluence for documentation
 
-![example-confluence-documentation](../../media/Pasted%20image%2020231201181347.png)
+![example-confluence-documentation](../../media/Pasted%20image%2020231201181347.jpg)
 
 ### Process process for documentation
 
@@ -42,7 +42,7 @@
 - HLD provides an overview of the system architecture, major components, and their interactions. It helps in aligning the team and stakeholders on the overall structure of the application.
 - We should update the HLD whenever there are significant changes to the system architecture. It serves as a reference for new team members and ensures everyone has a shared understanding of the system.
 
-![high-level-diagram-example](../../media/Pasted%20image%2020231201183011.png)
+![high-level-diagram-example](../../media/Pasted%20image%2020231201183011.jpg)
 
 #### LLD (Low Level Diagrams) and ER (Entity Relationship Diagrams)
 
@@ -53,6 +53,6 @@
 - We include ERDs as part of our documentation to maintain a clear understanding of the database schema. This is especially helpful during database migrations or when onboarding new team members.
 - During code reviews or discussions about database changes, referring to the ERD ensures that everyone is on the same page regarding the data model.
 
-![low-level-diagram-example](../../media/Pasted%20image%2020231201183115.png)
+![low-level-diagram-example](../../media/Pasted%20image%2020231201183115.jpg)
 
-![entity-relationship-diagram](../../media/Pasted%20image%2020231201183143.png)
+![entity-relationship-diagram](../../media/Pasted%20image%2020231201183143.jpg)

docs/about-deepak-sood/projects/88-stashfin-security-iam-apis.md

@@ -23,23 +23,23 @@ Followed - AAA - Authentication, Authorization and Audit with best practices
 
 ### Immutable Logs for Audit
 
-![stashfin-immutable-audit-logs](../../media/Pasted%20image%2020231201175020.png)
+![stashfin-immutable-audit-logs](../../media/Pasted%20image%2020231201175020.jpg)
 
 ### Authorization
 
-![stashfin-authorization](../../media/Pasted%20image%2020231201175035.png)
+![stashfin-authorization](../../media/Pasted%20image%2020231201175035.jpg)
 
 ### Postman implementation and documentation of all APIs
 
 [Stashfin Partners API](https://documenter.getpostman.com/view/16927648/TzzGGtg9)
 
-![stashfin-screenshot](../../media/Pasted%20image%2020231201175731.png)
+![stashfin-screenshot](../../media/Pasted%20image%2020231201175731.jpg)
 
-![stashfin-screenshot](../../media/Pasted%20image%2020231201175751.png)
+![stashfin-screenshot](../../media/Pasted%20image%2020231201175751.jpg)
 
 #### API Testing
 
-![stashfin-screenshot](../../media/Pasted%20image%2020231201175759.png)
+![stashfin-screenshot](../../media/Pasted%20image%2020231201175759.jpg)
 
 ## WebView Implementations
 
@@ -52,8 +52,8 @@ Followed - AAA - Authentication, Authorization and Audit with best practices
     - brand ambassador program
     - stashearn
 
-![stasfin-screenshot](../../media/Pasted%20image%2020231201180310.png)
+![stasfin-screenshot](../../media/Pasted%20image%2020231201180310.jpg)
 
-![stashfin-screenshot](../../media/Pasted%20image%2020231201180349.png)
+![stashfin-screenshot](../../media/Pasted%20image%2020231201180349.jpg)
 
-![stashfin-screenshot](../../media/Pasted%20image%2020231201180442.png)
+![stashfin-screenshot](../../media/Pasted%20image%2020231201180442.jpg)

docs/about-deepak-sood/projects/89-stashfin-devops-overhaul.md

@@ -7,23 +7,23 @@
 
 ## Screenshots
 
-![stashfin-jenkins-screenshot](../../media/Pasted%20image%2020231201181252.png)
+![stashfin-jenkins-screenshot](../../media/Pasted%20image%2020231201181252.jpg)
 
-![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173646.png)
+![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173646.jpg)
 
-![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173733.png)
+![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173733.jpg)
 
-![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173742.png)
+![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173742.jpg)
 
-![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173759.png)
+![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173759.jpg)
 
-![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173816.png)
+![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173816.jpg)
 
-![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173823.png)
+![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173823.jpg)
 
-![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173831.png)
+![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173831.jpg)
 
-![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173837.png)
+![stashfin-devops-screenshot](../../media/Pasted%20image%2020231201173837.jpg)
 
 ## Keeping all repositories consistent and follow same standards across
 
@@ -33,10 +33,10 @@
 - Keeping code complexity low for slowly reducing the function and file sizes
 - [Code smells](https://deepaksood619.github.io/computer-science/software-engineering/code-smell)
 
-![autoformatter-implementation](../../media/Pasted%20image%2020231201182421.png)
+![autoformatter-implementation](../../media/Pasted%20image%2020231201182421.jpg)
 
-![autoformatter-implementation](../../media/Pasted%20image%2020231201182427.png)
+![autoformatter-implementation](../../media/Pasted%20image%2020231201182427.jpg)
 
 ### SonarQube / snyk - Continuous Code Quality Inspector
 
-![sonarqube-implementation](../../media/Pasted%20image%2020231201182608.png)
+![sonarqube-implementation](../../media/Pasted%20image%2020231201182608.jpg)

docs/ai/computer-vision-cv/convolutional-neural-network-cnn.md

@@ -16,13 +16,13 @@ There is only some pre-processing needed when using CNNs. They develop and adapt
 
 The basic unit of a CNN framework is a neuron. The concept of neurons is based on human neurons, where synapses occur due to [neuron activation](https://viso.ai/deep-learning/neuron-activation/). These are statistical functions that calculate the weighted average of inputs and apply an activation function to the result generated. Layers are a cluster of neurons, with each layer having a particular function.
 
-![Concept of a neural network](../../media/Pasted%20image%2020240917123040.png)
+![Concept of a neural network](../../media/Pasted%20image%2020240917123040.jpg)
 
 ## CNN Layers
 
 A CNN system may have somewhere between 3 to 150 or even more layers: The “deep” of Deep neural networks refers to the number of layers. One layer’s output acts as another layer’s input. Deep multi-layer neural networks include [Resnet50 (50 layers) or ResNet101 (101 layers)](https://viso.ai/deep-learning/resnet-residual-neural-network/).
 
-![Concept of a Convolutional Neural Network (CNN)](../../media/Pasted%20image%2020240917123109.png)
+![Concept of a Convolutional Neural Network (CNN)](../../media/Pasted%20image%2020240917123109.jpg)
 
 ![CNN Architecture](../../media/Screenshot%202024-09-18%20at%2011.12.53%20PM.jpg)
 

docs/ai/computer-vision-cv/pre-trained-models.md

@@ -10,7 +10,7 @@ The architecture of pre-trained models varies, but they share common traits. The
 
 ## Top Pre-Trained Models for Image Classification
 
-![Overview of architectures until 2018](../../media/Pasted%20image%2020240916193555.png)
+![Overview of architectures until 2018](../../media/Pasted%20image%2020240916193555.jpg)
 
 Several pre-trained models have become standards in image classification due to their performance and reliability. Here are the key models:
 
@@ -124,7 +124,7 @@ YOLO is popular because of its single-stage architecture, real-time performance,
 
 ## Differences
 
-![Differences between different pre-trained models](../../media/Pasted%20image%2020240916184828.png)
+![Differences between different pre-trained models](../../media/Pasted%20image%2020240916184828.jpg)
 
 | Model name                   | Number of parameters (Millions) | ImageNet Top 1 Accuracy | Year |
 | ---------------------------- | ------------------------------- | ----------------------- | ---- |

docs/ai/data-science/data-governance.md

@@ -66,9 +66,9 @@ To protect sensitive data, we should grant minimal permissions to the users. Oft
 
 When we develop data products like reports or data feeds, we need to design a process to maintain data quality. Data developers should be granted with necessary permissions during development. After the data is online, they should be revoked from the data access.
 
-![managing sensitive data](../../media/Pasted%20image%2020240228190110.png)
+![managing sensitive data](../../media/Pasted%20image%2020240228190110.jpg)
 
-![Data Governance](../../media/Pasted%20image%2020240213122425.png)
+![Data Governance](../../media/Pasted%20image%2020240213122425.jpg)
 
 ## Links
 

docs/ai/data-visualization/tableau/architecture-components.md

@@ -9,11 +9,11 @@
 
 ## Tableau Server Architecture
 
-![tableau-architecture-diagram](../../../media/Pasted%20image%2020230714180101.png)
+![tableau-architecture-diagram](../../../media/Pasted%20image%2020230714180101.jpg)
 
 ### Tableau Server Deployment Reference Architecture
 
-![tableau-server-deployment-reference-architecture](../../../media/Pasted%20image%2020230714180337.png)
+![tableau-server-deployment-reference-architecture](../../../media/Pasted%20image%2020230714180337.jpg)
 
 ## Components of Tableau Server
 

docs/ai/data-visualization/tableau/data-model.md

@@ -13,15 +13,15 @@ The data model has two layers:
 
 Noodles = Relationships
 
-![relationship-logical-layer](../../../media/Pasted%20image%2020230320181009.png)
+![relationship-logical-layer](../../../media/Pasted%20image%2020230320181009.jpg)
 
 The top-level view of a data source with multiple, related tables. This is the logical layer. Logical tables can be combined using relationships (noodles). They don't use join types. They act like containers for physical tables.
 
 #### PHYSICAL LAYER
 
 Venn diagram = Joins
 
-![relationship-physical-layer](../../../media/Pasted%20image%2020230320181018.png)
+![relationship-physical-layer](../../../media/Pasted%20image%2020230320181018.jpg)
 
 Double-click a logical table to open it and see its physical tables. Physical tables can be combined using joins or unions. In this example, the Book logical table is made of three, joined physical tables (Book, Award, Info).
 

docs/ai/deep-learning/components.md

@@ -6,7 +6,7 @@
 - The **hidden layers** now perform the calculations on the received data. The biggest challenge here in neural networks creation is to decide the number of neurons and optimal number of hidden layers.
 - Finally, the **output layer** takes in the inputs that are passed in from the layers before it and performs the calculations via its neurons to compute the output.
 
-![neural network in deep learning](../../media/Pasted%20image%2020240917230635.png)
+![neural network in deep learning](../../media/Pasted%20image%2020240917230635.jpg)
 
 Deep learning requires a large amount of data for best results, while processing the data, neural networks can classify data with labels received from the dataset involving highly complex mathematical calculations.
 

docs/ai/libraries/mlops-model-deployment.md

@@ -18,7 +18,7 @@ The model is deployed to an online prediction service cluster (generally contain
 
 DataOps is an approach to data analytics and data-driven decision making that follows the agile development methodology of continuous improvement. The goal of DataOps is to reduce the cost of data management, improve data quality, and deliver insights to analysts and business users faster by creating datapipelines.
 
-![DataOps](../../media/Pasted%20image%2020240906232222.png)
+![DataOps](../../media/Pasted%20image%2020240906232222.jpg)
 
 [DataOps vs. DevOps: What's the Difference?](https://blog.hubspot.com/website/dataops-vs-devops)
 
@@ -77,7 +77,7 @@ To note a few features:
 
 ### ONNX (Open Neural Network Exchange)
 
-![ONNX](../../media/Pasted%20image%2020240719194528.png)
+![ONNX](../../media/Pasted%20image%2020240719194528.jpg)
 
 [ONNX](http://onnx.ai/) (Open Neural Network Exchange), an open-source format for representing deep learning models, was developed by Microsoft and is now managed by the Linux Foundation. It addresses the challenge of model packaging by providing a standardized format that enables easy transfer of machine learning models between different deep learning frameworks.