ML Systems Engineering and Operations with their Stakeholders — Image generated by Author

Until recently, all of us were learning about software development lifecycle(SDLC) and how it goes from requirement elicitation → designing → development → testing → deployment → all the way down to maintenance. We were(and are) studying the waterfall model, iterative model, and agile models of software development.

Now, we…

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Continuing the deep dive into the sea of NLP, this post is all about training tokenizers from scratch by leveraging Hugging Face’s tokenizers package.

Tokenization is often regarded as a subfield of NLP but it has its own story of evolution and how it has reached its current stage where…

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NLP may have been a little late to the AI epiphany but it is doing wonders with organizations like Google, OpenAI releasing state-of-the-art(SOTA) language models like BERT and GPT-2/3 respectively.

GitHub Copilot and OpenAI codex are among a few very popular applications that are in the news. As someone who…

One of the very interesting and widely used applications of NLP is Named Entity Recognition(NER).

Getting insights from raw and unstructured data is of vital importance. Uploading a document and getting the important bits of information from it is called information retrieval.

Information retrieval has been a major task/challenge in…

That’s me on my desk just tapping some keys!

Before you start working for an organisation, you are basically working in solitude. And the challenge with working solo is that there are no defined and externally imposed goals. You don’t have a laid-out path.

This is a scenario I find myself in every other week or every other month…


Machines or your computers only understand numbers and these numbers need to be represented and processed in a way that enables these machines to solve problems by learning from data instead of predefined instruction as in the case of programming.

All types of programming use mathematics at some level and…


At the start of this year, I published a mind map on the Data Science learning roadmap (shown below). The roadmap was widely accepted, that article got translated into different languages, and a large number of folks thanked me for publishing it.

Everything was good until a few aspirants pointed…

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Today, you don’t need to go to a university or a college to pursue a career in machine learning or any data-driven domain but you need a plan and a roadmap to guide yourself.

Once you have charted your own learning roadmap with a goal in mind, the next step…

I am going to keep this one short(but valuable🤞) without any detailed theme since I got my first jab of the vaccine today(Thursday, May 13) and my body isn’t allowing me to sit for long hours on my desk.

So, here are a few things that have kept me occupied…

Learning from books, taking down notes, and trying code snippets in colab/jupyter notebooks.

“Good friends, good books, and a sleepy conscience: this is the ideal life.”
Mark Twain

I hope you’re reading this blog in your pyjamas looking forward to a rejuvenating and healthy weekend. So, I have been working on multiple projects from creating MLE/MLOps courses to developing end-to-end ML systems…

Harshit Tyagi

Web & Data Science Instructional Designer | YouTuber | Writer

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