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 are at a stage where almost every other organisation is trying to incorporate AI/ML into their product. This new requirement of building ML systems adds/reforms some principles of the SDLC to give rise to a new engineering discipline called MLOps.
MLOps — A new term has popped up which is…
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 NLP. And NER(or NEL — Named Entity Linking) is used in several domains(finance, drugs, e-commerce, etc.) for information retrieval purposes.
In this tutorial post, I’ll show you how you can leverage NEL to develop a custom stock market news feed that lists down the buzzing stocks on the internet.
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. It is like working at the crossover of research and entrepreneurship.
Research to lay out the goals for your project and leverage entrepreneurship to create or build something that is valuable for someone out there. Aim for it to be truly remarkable. …
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 machine learning is programming data to learn the function that best describes the data.
The problem(or process) of finding the best parameters of a function using data is called model training in ML.
Therefore, in a nutshell, machine learning is programming to optimize for the best possible solution and we…
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 out that there are too many resources and many of them are expensive. Python programming was the only branch that had a number of really good courses but it ends right there for beginners.
A few important questions on foundational data science struck me:
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 is to screen the right set of courses that fit well into your roadmap and you then start building your foundation around those courses.
And this week, I wanted to share a few advanced-level specializations and courses that are on my list and that can help you with your search…
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 this week outside of my work.
I am learning a lot about engineering practices in ML as I am working towards finalizing the curriculum for one of my ML Engineering courses. …
“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 at scale and I have realized that oftentimes, I am either revisiting a book that I’ve read or I’m referring to a book that I just skimmed through but never got the chance to really read it.
This week, I want to share with you the books that I personally…
I hope you’re reading this email while keeping up in good health. This week I want you to ponder over how you frame your ML problems. I am going to introduce the reframing design pattern that breaks down the challenge of representing a problem.
Design patterns are a way of standardizing the experience and knowledge of experts to solve major challenges in any field. We have come too far in the field of AI and it’s high time we at least start discussing these design patterns.
The Reframing design pattern solves the challenge of posing an intuitive machine learning problem…
These questions are intriguing because all of these organizations (and the likes) have done an outstanding job of building state-of-the-art ML systems. …