DEV Community: Sanjana Sharma

Image Captioning with PyTorch: A Practical Introduction Introduction

Sanjana Sharma — Tue, 28 Apr 2026 10:08:58 +0000

If you’ve worked with computer vision, you know classification is just the beginning. With PyTorch, you can go beyond that and build systems that actually describe images.

Here’s a helpful reference to get started:
https://artificialintelligence.oodles.io/dev-blogs/introduction-to-image-captioning-using-pytorch

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗜𝗺𝗮𝗴𝗲 𝗖𝗮𝗽𝘁𝗶𝗼𝗻𝗶𝗻𝗴?

Image captioning is the process of generating textual descriptions for images using deep learning.

𝗖𝗼𝗿𝗲 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀

CNN (Encoder)
Extracts features from the image
RNN / Transformer (Decoder)
Generates captions
Dataset
Common datasets include MSCOCO

𝗕𝗮𝘀𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄
1.Load and preprocess images
2.Extract features using CNN
3.Train a sequence model
4.Generate captions
5.Real-World Use Case

In one of our implementations at Oodles, we built a PyTorch-based image captioning system to automate content tagging and improve searchability.

𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗺𝗼𝗿𝗲:
https://www.oodles.com/

𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀

PyTorch is flexible and powerful
Image captioning combines CV + NLP
Real value comes from automation

𝗖𝗧𝗔

If you're working on AI projects, exploring PyTorch for real-world use cases like image captioning is definitely worth it.

AI Agents Explained: From Chatbots to Autonomous Systems

Sanjana Sharma — Mon, 27 Apr 2026 07:52:50 +0000

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻

Traditional AI systems generate responses. AI agents go a step further—they execute tasks.

If you're new to AI agents, this guide explains the concept in depth: https://artificialintelligence.oodles.io/services/agentic-ai-services/ai-agent/

𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺

Most AI implementations fail because:

They are reactive
They lack execution capability
They depend heavily on user input

𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁

AI agents operate using a loop:

Input → Reason → Action → Feedback → Repeat

𝗖𝗼𝗿𝗲 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀

LLM / Brain

Handles reasoning and planning

Tools / APIs

Allow interaction with external systems

Memory

Stores past interactions and context

Orchestration

Manages multi-step workflows

𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲

In one of our projects at Oodles, we built an AI agent that automated workflow orchestration across systems, reducing manual dependency significantly.

𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗺𝗼𝗿𝗲:
https://www.oodles.com/

𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀
AI agents = autonomous systems
Execution > generation
Integration is critical
Real value comes from workflows

Machine Learning Developers: From Models to Production Systems

Sanjana Sharma — Fri, 24 Apr 2026 09:23:09 +0000

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻

Building ML models is one thing—deploying and scaling them is another. That’s where machine learning developers play a crucial role.

If you want a broader overview, this resource explains it well:
https://artificialintelligence.oodles.io/services/machine-learning-development-services/machine-learning-developers/

𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺

Many ML projects fail because:

Models don’t scale
Data pipelines are weak
Deployment is ignored

𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗠𝗟 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀

Step 1: Data Engineering

Cleaning and preparing datasets for training.

Step 2: Model Development

Training models using frameworks like TensorFlow or PyTorch.

Step 3: Deployment

Using APIs, Docker, or cloud services to deploy models.

Step 4: Monitoring

Tracking performance and retraining models when needed.

𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲

In one of our projects at Oodles, we implemented a predictive analytics system that automated decision-making workflows and improved efficiency.

Explore more:
https://www.oodles.com/

𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀

ML is more than just modeling
Deployment is critical
Continuous improvement is required

𝗖𝗧𝗔

If you're exploring real-world ML implementations, understanding the role of developers is essential.

Data Scraping: A Practical Guide to Automating Data Collection

Sanjana Sharma — Thu, 23 Apr 2026 08:10:02 +0000

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻

Manual data collection is slow, repetitive, and doesn’t scale. That’s why data scraping has become essential for modern applications.

𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗻𝗲𝘄 𝘁𝗼 𝘁𝗵𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁, 𝘁𝗵𝗶𝘀 𝗴𝘂𝗶𝗱𝗲 𝗴𝗶𝘃𝗲𝘀 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗼𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
👉 https://artificialintelligence.oodles.io/services/machine-learning-development-services/data-scraping/

𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺
1.Manual work is inefficient
2.Data is inconsistent
3.Insights are delayed

𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻

Step 1: Identify Data Sources

Define what data you need and where it exists.

Step 2: Use Scraping Tools

Tools like BeautifulSoup, Scrapy, or Selenium help extract data efficiently.

Step 3: Structure the Data

Convert raw HTML into usable formats like JSON or CSV.

Step 4: Automate

Schedule scraping workflows for continuous data updates.

𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲

In one of our implementations at Oodles, we built an automated scraping pipeline for competitor analysis. This significantly reduced manual effort and improved efficiency.

𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗼𝘂𝗿 𝘄𝗼𝗿𝗸:
👉 https://www.oodles.com/

𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀

Automation is essential
Data quality matters
Integration unlocks real value

𝗖𝗧𝗔

If you're exploring real-world implementations of data scraping, understanding structured approaches can make a big difference.