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Daniel Andarge

@DanielAndarge

Full Stack Developer | Data Engineer
Addis Ababa, Ethiopia
Joined May 2022
455 Following
66 Followers
73 Posts
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Daniel Andarge @DanielAndarge
Checkout my latest project on Github: πŸ‘‡ Title: Integrated Credit Risk Modeling and Loan Optimization with Advanced Segmentation. The project Leverages alternative data source (data-driven lending approach) to enhance credit risk modeling scoring and loan optimization.
DanielAndarge's tweet photo. Checkout my latest project on Github: πŸ‘‡ Title: Integrated Credit Risk Modeling and Loan Optimization with Advanced Segmentation. The project Leverages alternative data source (data-driven lending approach) to enhance credit risk modeling scoring and loan optimization. https://t.co/bzPe0GNQnN
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DanielAndarge Β retweeted
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Victor Verified account @victor_explore
This GitHub contains 450 real-world Machine Learning case studies from 100+ top companies like Netflix, Airbnb, DoorDash, Uber etc. Link in comment See exactly how top companies implement ML systems for recommendations, fraud detection, search, personalization, and demand forecasting in production.
victor_explore's tweet photo. This GitHub contains 450 real-world Machine Learning case studies from 100+ top companies like Netflix, Airbnb, DoorDash, Uber etc. Link in comment See exactly how top companies implement ML systems for recommendations, fraud detection, search, personalization, and demand forecasting in production.
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DanielAndarge Β retweeted
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Dhanian πŸ—―οΈ Verified account @e_opore
How to Set a MEAN Stack Project Structure mean-app/ β”‚ β”œβ”€β”€ backend/ β”‚ β”œβ”€β”€ config/ β”‚ β”‚ β”œβ”€β”€ db.js β”‚ β”‚ └── env.js β”‚ β”œβ”€β”€ controllers/ β”‚ β”‚ └── userController.js β”‚ β”œβ”€β”€ models/ β”‚ β”‚ └── userModel.js β”‚ β”œβ”€β”€ routes/ β”‚ β”‚ └── userRoutes.js β”‚ β”œβ”€β”€ middleware/ β”‚ β”‚ β”œβ”€β”€ authMiddleware.js β”‚ β”‚ └── errorMiddleware.js β”‚ β”œβ”€β”€ utils/ β”‚ β”‚ └── generateToken.js β”‚ β”œβ”€β”€ server.js β”‚ └── package.json β”‚ β”œβ”€β”€ frontend/ β”‚ β”œβ”€β”€ e2e/ # End-to-end tests β”‚ β”œβ”€β”€ src/ β”‚ β”‚ β”œβ”€β”€ app/ β”‚ β”‚ β”‚ β”œβ”€β”€ components/ # Reusable Angular components β”‚ β”‚ β”‚ β”‚ └── navbar/ β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ navbar.component.ts β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ navbar.component.html β”‚ β”‚ β”‚ β”‚ └── navbar.component.scss β”‚ β”‚ β”‚ β”œβ”€β”€ models/ # TypeScript interfaces β”‚ β”‚ β”‚ β”‚ └── user.model.ts β”‚ β”‚ β”‚ β”œβ”€β”€ services/ # HTTP services β”‚ β”‚ β”‚ β”‚ └── user.service.ts β”‚ β”‚ β”‚ β”œβ”€β”€ pages/ # Page (route) modules β”‚ β”‚ β”‚ β”‚ └── home/ β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ home.component.ts β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ home.component.html β”‚ β”‚ β”‚ β”‚ └── home.component.scss β”‚ β”‚ β”‚ β”œβ”€β”€ app-routing.module.ts β”‚ β”‚ β”‚ └── app.module.ts β”‚ β”‚ β”œβ”€β”€ assets/ # Images, styles, fonts β”‚ β”‚ β”œβ”€β”€ environments/ # env configs for dev/prod β”‚ β”‚ β”‚ β”œβ”€β”€ environment.ts β”‚ β”‚ β”‚ └── https://t.co/XHJaLBzKFq.ts β”‚ β”‚ └── main.ts # Angular entry point β”‚ β”œβ”€β”€ angular.json β”‚ β”œβ”€β”€ package.json β”‚ └── tsconfig.json β”‚ β”œβ”€β”€ .env β”œβ”€β”€ .gitignore └── https://t.co/shpCukooKd 1. Backend Structure β†’ `backend/` β†’ Holds the server-side logic, APIs, database handling, and middleware. β†’ `config/` β†’ Keeps configuration files (e.g., `db.js`, `env.js`) centralized. β†’ `controllers/` β†’ Business logic responding to HTTP requests. β†’ `models/` β†’ Mongoose schemas defining MongoDB documents. β†’ `routes/` β†’ Defines API endpoints (e.g., for users, auth). β†’ `middleware/` β†’ Code that runs before controllers (authentication, error handling). β†’ `utils/` β†’ Reusable helper functions (e.g., token generation). β†’ `server.js` β†’ Application’s backend entry point. 2. Frontend Structure β†’ `frontend/` β†’ Contains all Angular application code. β†’ `e2e/` β†’ End-to-end tests using Protractor or Cypress. β†’ `src/app/components/` β†’ Angular reusable components (e.g., navigation bar). β†’ `src/app/models/` β†’ TypeScript interfaces or classes to define data shapes. β†’ `src/app/services/` β†’ Angular services to manage HTTP calls to backend. β†’ `src/app/pages/` β†’ Route-specific page-level modules and components. β†’ `src/app/app-routing.module.ts` β†’ Angular routing configuration. β†’ `src/app/app.module.ts` β†’ Main application Angular module. β†’ `src/assets/` β†’ Static assets (images, styles, fonts). β†’ `src/environments/` β†’ Environment configurations for development and production. β†’ `src/main.ts` β†’ Angular entry point. β†’ `angular.json`, `package.json`, `tsconfig.json` β†’ Build config, dependencies, TypeScript setup. 3. Root-Level Files β†’ `.env` β†’ Environment variables (e.g., database URLs, API keys, JWT secrets). β†’ `.gitignore` β†’ Files or folders to exclude from version control. β†’ `https://t.co/shpCukooKd` β†’ Documentation and project overview. Grasp your MEAN structure, then accelerate your learning: Grab your guide now and start building professional MEAN applications today! πŸ˜‰ https://t.co/9iLiXCrv1c
e_opore's tweet photo. How to Set a MEAN Stack Project Structure mean-app/ β”‚ β”œβ”€β”€ backend/ β”‚ β”œβ”€β”€ config/ β”‚ β”‚ β”œβ”€β”€ db.js β”‚ β”‚ └── env.js β”‚ β”œβ”€β”€ controllers/ β”‚ β”‚ └── userController.js β”‚ β”œβ”€β”€ models/ β”‚ β”‚ └── userModel.js β”‚ β”œβ”€β”€ routes/ β”‚ β”‚ └── userRoutes.js β”‚ β”œβ”€β”€ middleware/ β”‚ β”‚ β”œβ”€β”€ authMiddleware.js β”‚ β”‚ └── errorMiddleware.js β”‚ β”œβ”€β”€ utils/ β”‚ β”‚ └── generateToken.js β”‚ β”œβ”€β”€ server.js β”‚ └── package.json β”‚ β”œβ”€β”€ frontend/ β”‚ β”œβ”€β”€ e2e/ # End-to-end tests β”‚ β”œβ”€β”€ src/ β”‚ β”‚ β”œβ”€β”€ app/ β”‚ β”‚ β”‚ β”œβ”€β”€ components/ # Reusable Angular components β”‚ β”‚ β”‚ β”‚ └── navbar/ β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ navbar.component.ts β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ navbar.component.html β”‚ β”‚ β”‚ β”‚ └── navbar.component.scss β”‚ β”‚ β”‚ β”œβ”€β”€ models/ # TypeScript interfaces β”‚ β”‚ β”‚ β”‚ └── user.model.ts β”‚ β”‚ β”‚ β”œβ”€β”€ services/ # HTTP services β”‚ β”‚ β”‚ β”‚ └── user.service.ts β”‚ β”‚ β”‚ β”œβ”€β”€ pages/ # Page (route) modules β”‚ β”‚ β”‚ β”‚ └── home/ β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ home.component.ts β”‚ β”‚ β”‚ β”‚ β”œβ”€β”€ home.component.html β”‚ β”‚ β”‚ β”‚ └── home.component.scss β”‚ β”‚ β”‚ β”œβ”€β”€ app-routing.module.ts β”‚ β”‚ β”‚ └── app.module.ts β”‚ β”‚ β”œβ”€β”€ assets/ # Images, styles, fonts β”‚ β”‚ β”œβ”€β”€ environments/ # env configs for dev/prod β”‚ β”‚ β”‚ β”œβ”€β”€ environment.ts β”‚ β”‚ β”‚ └── https://t.co/XHJaLBzKFq.ts β”‚ β”‚ └── main.ts # Angular entry point β”‚ β”œβ”€β”€ angular.json β”‚ β”œβ”€β”€ package.json β”‚ └── tsconfig.json β”‚ β”œβ”€β”€ .env β”œβ”€β”€ .gitignore └── https://t.co/shpCukooKd 1. Backend Structure β†’ `backend/` β†’ Holds the server-side logic, APIs, database handling, and middleware. β†’ `config/` β†’ Keeps configuration files (e.g., `db.js`, `env.js`) centralized. β†’ `controllers/` β†’ Business logic responding to HTTP requests. β†’ `models/` β†’ Mongoose schemas defining MongoDB documents. β†’ `routes/` β†’ Defines API endpoints (e.g., for users, auth). β†’ `middleware/` β†’ Code that runs before controllers (authentication, error handling). β†’ `utils/` β†’ Reusable helper functions (e.g., token generation). β†’ `server.js` β†’ Application’s backend entry point. 2. Frontend Structure β†’ `frontend/` β†’ Contains all Angular application code. β†’ `e2e/` β†’ End-to-end tests using Protractor or Cypress. β†’ `src/app/components/` β†’ Angular reusable components (e.g., navigation bar). β†’ `src/app/models/` β†’ TypeScript interfaces or classes to define data shapes. β†’ `src/app/services/` β†’ Angular services to manage HTTP calls to backend. β†’ `src/app/pages/` β†’ Route-specific page-level modules and components. β†’ `src/app/app-routing.module.ts` β†’ Angular routing configuration. β†’ `src/app/app.module.ts` β†’ Main application Angular module. β†’ `src/assets/` β†’ Static assets (images, styles, fonts). β†’ `src/environments/` β†’ Environment configurations for development and production. β†’ `src/main.ts` β†’ Angular entry point. β†’ `angular.json`, `package.json`, `tsconfig.json` β†’ Build config, dependencies, TypeScript setup. 3. Root-Level Files β†’ `.env` β†’ Environment variables (e.g., database URLs, API keys, JWT secrets). β†’ `.gitignore` β†’ Files or folders to exclude from version control. β†’ `https://t.co/shpCukooKd` β†’ Documentation and project overview. Grasp your MEAN structure, then accelerate your learning: Grab your guide now and start building professional MEAN applications today! πŸ˜‰ https://t.co/9iLiXCrv1c
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DanielAndarge Β retweeted
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Shalini Goyal Verified account @goyalshaliniuk
Building a GenAI app? Don’t just plug in a model - design it to scale, adapt, and evolve. Here’s your blueprint for future-ready GenAI systems. πŸ‘‡ 1. Modular Architecture Separate UI, orchestration, models, and storage to swap parts independently. Use LangChain or LlamaIndex to build pipelines. 2. Context Engineering Layer system prompts, memory, and retrieved knowledge to optimize generation. Use chunking and summarization to stay efficient. 3. Retrieval-Augmented Generation (RAG) Connect vector DBs like Pinecone or Weaviate and use hybrid search (dense + keyword) for domain-specific relevance. 4. Low-Latency Design Cut load times and delay using model distillation, quantization, and async I/O. 5. Agent-Based Systems Use CrewAI, AutoGen, or LangGraph for task decomposition and tool execution via specialized sub-agents. 6. Tool & Plugin Integration Enable LLMs to run code, hit APIs, or use external tools through OpenAI function-calling or LangChain routing. 7. Streaming & Feedback Improve experience with real-time streaming via WebSockets and user feedback for continuous refinement. 8. Memory Management Support both session and long-term memory using Redis, Postgres, or vector DBs for persistence. 9. Smart Deployment Use K8s or serverless runtimes (like AWS Lambda) to deploy GenAI apps with dynamic scaling. 10. Observability Track usage, hallucinations, and prompts using tools like LangSmith or WhyLabs for LLM monitoring. [Explore More In The Post] Here’s the takeaway? Good GenAI apps aren’t just about prompts, they’re engineered for performance, adaptability, and scale.
goyalshaliniuk's tweet photo. Building a GenAI app? Don’t just plug in a model - design it to scale, adapt, and evolve. Here’s your blueprint for future-ready GenAI systems. πŸ‘‡ 1. Modular Architecture Separate UI, orchestration, models, and storage to swap parts independently. Use LangChain or LlamaIndex to build pipelines. 2. Context Engineering Layer system prompts, memory, and retrieved knowledge to optimize generation. Use chunking and summarization to stay efficient. 3. Retrieval-Augmented Generation (RAG) Connect vector DBs like Pinecone or Weaviate and use hybrid search (dense + keyword) for domain-specific relevance. 4. Low-Latency Design Cut load times and delay using model distillation, quantization, and async I/O. 5. Agent-Based Systems Use CrewAI, AutoGen, or LangGraph for task decomposition and tool execution via specialized sub-agents. 6. Tool & Plugin Integration Enable LLMs to run code, hit APIs, or use external tools through OpenAI function-calling or LangChain routing. 7. Streaming & Feedback Improve experience with real-time streaming via WebSockets and user feedback for continuous refinement. 8. Memory Management Support both session and long-term memory using Redis, Postgres, or vector DBs for persistence. 9. Smart Deployment Use K8s or serverless runtimes (like AWS Lambda) to deploy GenAI apps with dynamic scaling. 10. Observability Track usage, hallucinations, and prompts using tools like LangSmith or WhyLabs for LLM monitoring. [Explore More In The Post] Here’s the takeaway? Good GenAI apps aren’t just about prompts, they’re engineered for performance, adaptability, and scale.
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DanielAndarge Β retweeted
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Shalini Goyal Verified account @goyalshaliniuk
Confused between ML, NLP, Generative, and other AI models? Here’s a quick breakdown of the 6 most important types of AI models you must understand in 2025 πŸ‘‡ 1. Machine Learning Models They learn from labeled and unlabeled data to classify, predict, and detect patterns. Think decision trees, SVMs, and XGBoost. 2. Deep Learning Models Neural networks built for unstructured data like images, audio, and text. Includes CNNs, RNNs, Transformers, and GANs. 3. NLP Models Focused on understanding and generating human language - used in chatbots, summarizers, and assistants like GPT and BERT. 4. Generative Models These models create, from text to images to music. Powered by models like GPT-4, DALLΒ·E, and StyleGAN. 5. Hybrid Models Combine the best of rule-based and neural AI. Perfect for use cases needing both reasoning and context awareness (e.g., RAG pipelines). 6. Computer Vision Models Built for images and videos. Used in object detection, facial recognition, and medical scans - powered by models like YOLO and ResNet. Each AI model has its strengths and knowing which one fits your use case is half the battle. Save this guide as your cheat sheet!
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DanielAndarge Β retweeted
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Bytebytego Verified account @bytebytego
IBM MQ -> RabbitMQ -> Kafka ->Pulsar, How do message queue architectures evolve? πŸ”Ή IBM MQ IBM MQ was launched in 1993. It was originally called MQSeries and was renamed WebSphere MQ in 2002. It was renamed to IBM MQ in 2014. IBM MQ is a very successful product widely used in the financial sector. Its revenue still reached 1 billion dollars in 2020. πŸ”Ή RabbitMQ RabbitMQ architecture differs from IBM MQ and is more similar to Kafka concepts. The producer publishes a message to an exchange with a specified exchange type. It can be direct, topic, or fanout. The exchange then routes the message into the queues based on different message attributes and the exchange type. The consumers pick up the message accordingly. πŸ”Ή Kafka In early 2011, LinkedIn open sourced Kafka, which is a distributed event streaming platform. It was named after Franz Kafka. As the name suggested, Kafka is optimized for writing. It offers a high-throughput, low-latency platform for handling real-time data feeds. It provides a unified event log to enable event streaming and is widely used in internet companies. Kafka defines producer, broker, topic, partition, and consumer. Its simplicity and fault tolerance allow it to replace previous products like AMQP-based message queues. πŸ”Ή Pulsar Pulsar, developed originally by Yahoo, is an all-in-one messaging and streaming platform. Compared with Kafka, Pulsar incorporates many useful features from other products and supports a wide range of capabilities. Also, Pulsar architecture is more cloud-native, providing better support for cluster scaling and partition migration, etc. There are two layers in Pulsar architecture: the serving layer and the persistent layer. Pulsar natively supports tiered storage, where we can leverage cheaper object storage like AWS S3 to persist messages for a longer term. Over to you: which message queues have you used? – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://t.co/eVEdOFSYPY
bytebytego's tweet photo. IBM MQ -> RabbitMQ -> Kafka ->Pulsar, How do message queue architectures evolve? πŸ”Ή IBM MQ IBM MQ was launched in 1993. It was originally called MQSeries and was renamed WebSphere MQ in 2002. It was renamed to IBM MQ in 2014. IBM MQ is a very successful product widely used in the financial sector. Its revenue still reached 1 billion dollars in 2020. πŸ”Ή RabbitMQ RabbitMQ architecture differs from IBM MQ and is more similar to Kafka concepts. The producer publishes a message to an exchange with a specified exchange type. It can be direct, topic, or fanout. The exchange then routes the message into the queues based on different message attributes and the exchange type. The consumers pick up the message accordingly. πŸ”Ή Kafka In early 2011, LinkedIn open sourced Kafka, which is a distributed event streaming platform. It was named after Franz Kafka. As the name suggested, Kafka is optimized for writing. It offers a high-throughput, low-latency platform for handling real-time data feeds. It provides a unified event log to enable event streaming and is widely used in internet companies. Kafka defines producer, broker, topic, partition, and consumer. Its simplicity and fault tolerance allow it to replace previous products like AMQP-based message queues. πŸ”Ή Pulsar Pulsar, developed originally by Yahoo, is an all-in-one messaging and streaming platform. Compared with Kafka, Pulsar incorporates many useful features from other products and supports a wide range of capabilities. Also, Pulsar architecture is more cloud-native, providing better support for cluster scaling and partition migration, etc. There are two layers in Pulsar architecture: the serving layer and the persistent layer. Pulsar natively supports tiered storage, where we can leverage cheaper object storage like AWS S3 to persist messages for a longer term. Over to you: which message queues have you used? – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://t.co/eVEdOFSYPY
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DanielAndarge Β retweeted
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Mastercard Foundation Verified account @MastercardFdn
The Mastercard Foundation Scholars Program is a global initiative to develop the next generation of transformative leaders. Please note: Our partners fully manage the recruitment and selection process. Learn more here: https://t.co/4QEm2LXDhThe Mastercard Foundation Scholars Program is a global initiative to develop the next generation of transformative leaders. Please note: Our partners fully manage the recruitment and selection process. Learn more here: https://t.co/4QEm2LXDhThe Mastercard Foundation Scholars Program is a global initiative to develop the next generation of transformative leaders. Please note: Our partners fully manage the recruitment and selection process. Learn more here: https://t.co/4QEm2LXDhThe Mastercard Foundation Scholars Program is a global initiative to develop the next generation of transformative leaders. Please note: Our partners fully manage the recruitment and selection process. Learn more here: https://t.co/4QEm2LXDhF
MastercardFdn's tweet photo. The Mastercard Foundation Scholars Program is a global initiative to develop the next generation of transformative leaders. Please note: Our partners fully manage the recruitment and selection process. Learn more here: https://t.co/4QEm2LXDhF
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DanielAndarge Β retweeted
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Solomon Kassa Verified account @SolomonMKassa
Official Notice: Google Play Developer Registration Now Available for Ethiopian Developers! I am thrilled to announce that Ethiopia is now officially supported for Google Play Developer Console registration! This means our talented developers can directly publish apps on Google Play, bringing their innovative ideas to the world. This positive milestone was achieved following a discussions I had on behalf Ethiopian Ministry of Innovation and Technology with the Google team in late October, where we formally requested support for Ethiopia during their visit to Addis Ababa. While the process was in the making, I would like to extend my appreciation to the Google team for making it happen so swiftly and to everyone at Google Africa office who played a part in making this happen. The inclusion was made official in October, but we wanted to provide an official statement after receiving a formal confirmation from Google. This marks a significant step for the Ethiopian tech community, empowering developers to share their talent on a global platform. To our talented developers: the stage is now yours! Let's seize this opportunity to showcase Ethiopian tech talent globally and push our digital transformation forward. Congratulations to the Ethiopian developer community, and thank you Google! πŸ’šπŸ’›β€οΈ#Ethiopia #GooglePlay #DigitalTransformation #TechInnovation
SolomonMKassa's tweet photo. Official Notice: Google Play Developer Registration Now Available for Ethiopian Developers! I am thrilled to announce that Ethiopia is now officially supported for Google Play Developer Console registration! This means our talented developers can directly publish apps on Google Play, bringing their innovative ideas to the world. This positive milestone was achieved following a discussions I had on behalf Ethiopian Ministry of Innovation and Technology with the Google team in late October, where we formally requested support for Ethiopia during their visit to Addis Ababa. While the process was in the making, I would like to extend my appreciation to the Google team for making it happen so swiftly and to everyone at Google Africa office who played a part in making this happen. The inclusion was made official in October, but we wanted to provide an official statement after receiving a formal confirmation from Google. This marks a significant step for the Ethiopian tech community, empowering developers to share their talent on a global platform. To our talented developers: the stage is now yours! Let's seize this opportunity to showcase Ethiopian tech talent globally and push our digital transformation forward. Congratulations to the Ethiopian developer community, and thank you Google! πŸ’šπŸ’›β€οΈ#Ethiopia #GooglePlay #DigitalTransformation #TechInnovation
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DanielAndarge Β retweeted
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javinpaul @javinpaul
10 Best System design concepts to learn in 2024 1. Caching 2. DB Sharding 3. load-balancing 4. replication 5. fault-tolerance 6. high-availability 7. API Gateway 8. scalability 9. Performance 10. Indexing learn more on DesignGuru - https://t.co/VuZLWnBFWY
javinpaul's tweet photo. 10 Best System design concepts to learn in 2024 1. Caching 2. DB Sharding 3. load-balancing 4. replication 5. fault-tolerance 6. high-availability 7. API Gateway 8. scalability 9. Performance 10. Indexing learn more on DesignGuru - https://t.co/VuZLWnBFWY https://t.co/60gBpQ7n1b
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DanielAndarge Β retweeted
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Bytebytego Verified account @bytebytego
Best ways to test system functionality. Next week's topic will be listed at the end. Testing system functionality is a crucial step in software development and engineering processes. It ensures that a system or software application performs as expected, meets user requirements, and operates reliably. Here we delve into the best ways: 1. Unit Testing: Ensures individual code components work correctly in isolation. 2. Integration Testing: Verifies that different system parts function seamlessly together. 3. System Testing: Assesses the entire system's compliance with user requirements and performance. 4. Load Testing: Tests a system's ability to handle high workloads and identifies performance issues. 5. Error Testing: Evaluates how the software handles invalid inputs and error conditions. 6. Test Automation: Automates test case execution for efficiency, repeatability, and error reduction. Over to you: How do you approach testing system functionality in your software development or engineering projects? Over to you: what's your company's release process look like? -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://t.co/FIzCeaVVan
bytebytego's tweet photo. Best ways to test system functionality. Next week's topic will be listed at the end. Testing system functionality is a crucial step in software development and engineering processes. It ensures that a system or software application performs as expected, meets user requirements, and operates reliably. Here we delve into the best ways: 1. Unit Testing: Ensures individual code components work correctly in isolation. 2. Integration Testing: Verifies that different system parts function seamlessly together. 3. System Testing: Assesses the entire system's compliance with user requirements and performance. 4. Load Testing: Tests a system's ability to handle high workloads and identifies performance issues. 5. Error Testing: Evaluates how the software handles invalid inputs and error conditions. 6. Test Automation: Automates test case execution for efficiency, repeatability, and error reduction. Over to you: How do you approach testing system functionality in your software development or engineering projects? Over to you: what's your company's release process look like? -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://t.co/FIzCeaVVan
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Daniel Andarge @DanielAndarge
#DigitalEthiopia
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Checkout my latest project on Github: πŸ‘‡ Title: Integrated Credit Risk Modeling and Loan Optimization with Advanced Segmentation. The project Leverages alternative data source (data-driven lending approach) to enhance credit risk modeling scoring and loan optimization.
DanielAndarge's tweet photo. Checkout my latest project on Github: πŸ‘‡ Title: Integrated Credit Risk Modeling and Loan Optimization with Advanced Segmentation. The project Leverages alternative data source (data-driven lending approach) to enhance credit risk modeling scoring and loan optimization. https://t.co/bzPe0GNQnN
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Daniel Andarge @DanielAndarge
#fintech #CreditRisk #CreditScore #LoanOptimization #CustomerSegmentation #DigitalLending #Ethiopia #AlternativeData #DigitalBanking
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Daniel Andarge @DanielAndarge
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Daniel Andarge @DanielAndarge
@zerodtes @Hamptonism Okay then , but why reptiles only there are aslo other endangered species
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