Machine Learning has become an exciting route to go down by many teams and companies. However, it's not always realistic that everyone is expected to catch up with all of the latest ML trends.
Usually Machine learning teams are made up of different people. On the technical side you can have a mixture of the different data scientists and engineers, like a Machine Learning Data Scientists, as well as Machine Learning and Data Engineers. The data scientists' main responsibility would be building out or improving the models, and the engineers will help with everything else around deployment and that the models are getting the data they need.
From the non-technical side it's likely you'll have a project manager and possibly also several other business stakeholders. This course is aimed for these people, who need to understand what's going on at a higher level, without necessarily having to dive into the technical components. Those that need to know enough to help with product vision, and be able to have and understand discussions about current statuses, blockers, as well as estimations.
In this course we'll look at some of the different components involved in an ML project so that you can feel like you can have fruitful conversations when working on an ML project without needing to get bogged up on all the technical details.
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Hey there! My name is Max.
And I’m a data loving, Dungeons & Dragons playing, Python programming dude.
I’ve got a Bachelors in Physics and a Masters in Astrophysics.
For the past 5 years, I’ve been working in the field of data - starting as a Data Scientist then becoming and working as a Data Engineer.
I stumbled into the world of programming and data completely by accident — but the first time I coded a simple blackjack program in Python is the first time it really “clicked” for me.
They say you know you truly love an activity if you reach a state of “flow”, and that is what programming does for me. Everything fades away and all that exists is me, some good tunes, those little lines of white text, and the agitating company of a bug or two or five.
If I’m honest, these 3 decisions changed the path of my career (and probably my life) entirely:
1. Deciding to finally learn Python at university, after 2 failed attempts in high school
2. Tentatively venturing into the data world, which started with simply googling “what does a data scientist do”
3. Committing to a data engineer pivot by learning about big data tools and infrastructure design to build scalable systems and pipelines
I could talk endlessly about data infrastructure, big data pipelines and my relentless and eternally raging fire for Python.
Going into data gave me endless passion for my work & completely changed the trajectory of my life.
I can’t wait to help you find and do the same.
