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Hot Topics of AI : Voice, Image, Social Media and Text Analytics

We are living in the age of evolving Artificial Intelligence. Here everything is data, so there is an enormous opportunity of getting intelligent from each aspect of life/business.

Having worked on spreadsheets and relational databases for a decade, I can confidently say that we have to go beyond it if we care to be more intelligent than ever. This need is being manifested in every business, and its super set human activities.

Image, audio, social media and documents have been and will be main area of AI applications. So, we have to get ready to dig out those unstructured data so as to get meaningful insights.In this pursuit, I developed APIs for each hot area of AI.

First, let us start from voice based application. We have to care about voice based intelligence so as to do things more easily than ever. Here I would like to show an API where you can change the color of plot, title of the plot, size of points and even you can add a (local polynomial) regression line just by your voice

We are in the process of building smart image recognition system with/without google vision API. Here I would like to share an API meant for editing and viewing any image

Document reading has been a tedious task for most of us, specially compliance officers, risk managers and any sort of document readers. So, I developed an API to summarize pdf/document with help of NLP/LSA .

Sentiment tracking has been an essential work of any marketing team /strategy team. You must be aware of how people perceive your product/service/event, public sentiment score, its change with time and who talks to whom on social media. Here is the social media API for that purpose.

Real time monitoring/Sensor data visualization has been very important component of smart healthcare, smart building management and smart home. Here is the API

Please feel free to give your kind suggestions.

 

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Silent Drains: How Poor Data Observability Costs Enterprises Millions

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

  1. Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
  2. Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
  3. Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
  4. Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
  5. Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data operations across diverse regions, Faced with a vast and complex system, manual oversight became unsustainable. Rakuten provided data observability solutions