Shruti Turner

Tickets are the building blocks that make up a team’s work, without clearly defined blocks it’s difficult to work efficiently and effectively as a team, catching gaps and avoiding duplication of work.

A high level introduction to how Reinforcement Learning works. No equations and not super technical, but hopefully enough to whet your appetite.

We are laying the groundwork for more complex statistics which are based on these concepts, so it's worth getting comfortable with these terms and equations.

This is just an introduction to some basic probability terminology, sometimes a lot of these terms might be used interchangeably but they have distinct meanings and it's worth knowing them well and understanding them as you progress!

Often people might jump straight to wanting to use ML, but there is a place for them and the key is to use the right tool for the job!

Find a way to get experience, then focus on the "dream job". It's tough out there at the moment and a lot of companies are looking for self-sufficient individuals not juniors.

I want to take this opportunity to look back and reflect on my journey over the last 365 days. I feel like I've changed *a lot* - both personally and technically.

This year, Microsoft have put together 4 AI/ML related challenge pathways to complete: Machine Learning, Machine Learning Operations (MLOps), Cognitive Services and AI Builder.

In this article, I'll be giving a rundown of my thoughts and reflections having completed the GenAI and LLM Coursera course.

Errors due to dependencies being unexpected can lead to a lot of lost time as they can be tough to find, especially when you think you have them!

I'm in a great position with some foundational MLOps knowledge and experience, starting a new chapter which is my next great opportunity to learn new skills in a different aspect of Machine Learning.

In this post I'll be covering and overview of some of the principals and also some things to think about when thinking about Responsible AI.

You don't have to put your interests on hold to develop your fundamental skills, and nor should you put your fundamental skill development on hold for your interests.

Working with other people can be a good way to learn new skills and develop current ones. It's a great way to foster team collaboration and tackle conundrums.

Reviewing a pull request (PR) is such a fundamental part of being a developer, and yet it's rare than anyone actually sits down and explains to you how to review a PR effectively.

What does it mean to "investigate" an issue?! Sometimes it can feel overwhelming to have such a large scope.

That being said, your career is yours. It's up to you to make sure that you are keeping yourself employable with the range of skills you need.

3 simple steps, that's the rational side of it for sure, but hopefully it helps to have those steps in your head to get through this slightly intimidating time.

There is no one size fits all approach to Cloud, and thankfully the creators of Cloud platforms understand that.

These address the foundational theory of why we use cloud and why its the go-to choice, superseding on-premises set ups, and independent of which provider you favour.

Being ready for it doesn't mean you have to know everything, being ready is about having a good level of foundation knowledge and knowing which questions to ask and when.

There are so many programming languages out there, it can be difficult to know where to start in terms of which language to learn. Each language will have its strengths and weaknesses in terms of capability.

What I have gained though, is more than technical knowledge, but also confidence. Confidence in my abilities, how I can contribute to the team and also my understanding of how my team thinks/works (and by extension this industry.)

Instead of seeing your learning success as how many topics/skills you can "know" in a certain space of time, what happens if you see your learning success as a journey of gaining knowledge bit by bit.

What is the threshold we will accept for bias models? How do we balance overall model performance compared to performance of individual groups?

Often documentation is a thing that we consume to help us, there's a love-hate relationship with them. But good documentation isn't just something we should be consuming, we should be writing it too!

Last week I hit my six month milestone as a professional Machine Learning Engineer. Even 2 years ago, I would never have imagined that this would be the career I'm in. I sure didn't plan to be here!

Standards exist for a reason. They're there to help you out, as much as you think future you will understand and remember everything present you thinks...you're wrong. That's not to mention all the people you're collaborating with as well.

Sometimes all the code and logic and syntax circling around your brain can just feel too much. We just want this to be a problem we can talk through in my own natural language, but no one is around to listen.

Well, the point is testing is important and I'm going to try and give an introductory overview of the what and why, and touch a little on the how, i.e. implementation, but leave the details and "how to" for another post!

Knowing the theory but not knowing how to actually do it won't help you much (she says from experience). Go through those steps and processes.

There are a lot of "guides" to passing the exam online, I found a lot were outdated and recommended tools which were paid for etc. but I didn't really get a feel for what it would be like to take the exam and so I really wasn't sure how to prepare.

Git is a handy tool for keeping track of and sharing your code. I'll be doing a bit of a "How to" guide on getting stared with git and cloud repositories.

First off, what even is GIT? Well, no I'm not insulting anyone with British slang. Actually, this is the tool used to track code over time, helpful for collaborating with others.

It's not to say that one role is better than the other, nor that either is more skilled that the other. It's a matter of personal interest and company structure.

I'm planning to use this blog to write about my journey as a Machine Learning Engineer, both to share my experiences and knowledge. I hope to have a mixture of blog posts, technical and otherwise which will hopefully be of interest to others.