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Data Engineering

Scalable and efficient data pipelines are as important for the success of analytics, data science, and machine learning as reliable supply lines are for winning a war.

For deploying big-data analytics, data science, and machine learning (ML) applications in the real world, analytics-tuning and model-training is only around 25% of the work. Approximately 50% of the effort goes into making data ready for analytics and ML. The remaining 25% effort goes into making insights and model inferences…

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Data Engineering

Difference between SQL and NoSQL databases. Deep dive, decision tree, and cheatsheet to choose the best for your data type and use case from 12 database types.

How do you choose a database? Maybe, you assess whether the use case needs a Relational database. Depending on the answer, you pick your favorite SQL or NoSQL datastore, and make it work. It is a prudent tactic: a known devil is better than an unknown angel.

Picking the right…

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Programming Languages

What if programming languages were stocks? And you had to make a portfolio to fetch good returns in 2022?

You probably have seen various surveys and analyses listing the most popular programming languages. Those cover the universe of programming languages, like the S&P 500 does for the stock market. What would be the best portfolio for you to be successful and outperform the rest?

Of course, it depends on…

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Data Analytics, Notes from Industry

Systematic way of collecting data and extracting actionable insights using the Drivetrain Approach.

“Let’s collect all data we can, and we will fish for insights later.” Have you heard this before?

That approach seldom works. On rare occasions when it does work a little, the RoI is very low w.r.t. the cost of collecting, processing, and storing volumes of data. …

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Software Engineering

How to mitigate challenges in designing software at an early-stage startup.

The great thing about starting a new project is that you get a clean slate. No baggage of design choices that you hated to look at every day in your last project. But how many times have you seen a shiny new project not turning into the same intractable mess?

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Programming Tips

How to profile performance and balance it with ease of use

Applying a function to all rows in a Pandas DataFrame is one of the most common operations during data wrangling. Pandas DataFrame apply function is the most obvious choice for doing it. It takes a function as an argument and applies it along an axis of the DataFrame. …

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Conversational AI

A case for Bhārat Bhāṣā Stack

Bhārat Bhāṣā Stack will catalyze Voice Assistant and Conversational AI innovations for vernacular Indic languages as India Stack did for FinTech.

A decade ago, it was unimaginable.

That one would pay a street vendor in a nondescript small town in India by scanning on mobile a QR code hung on…

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Microservices

Distilled lessons from building microservices powering Slang Labs platform. Presented in a PyCon India 2019 tutorial.

A data model organizes data elements and formalizes their relationships with one another. In database design, data modeling is the process of analyzing application requirements and designing conceptual, logical, and physical data models for storage. However, data storage is only one, albeit an important, aspect of microservices.

There are three…

My trek mates and I climbing Mayali Pass in Uttarakhand Himalaya, India

Machine Learning for Developers

Map of the terrain and a compass for software developers to embark on ML expedition.

You are a Software Engineer. You notice Artificial Intelligence, Machine Learning, Deep Learning, Data Science buzzwords all around. You wonder what these phrases mean, whether all this is for real and useful or is yet another hype and passing fad.

You want to figure out how it is changing or…

Satish Chandra Gupta

Cofounder @SlangLabs. Ex Amazon, Microsoft Research. I learn, do, and write about Machine Learning in production.

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