How to Make Money in Machine Learning, without doing the Math/Theory[Finance Fridays]
This is relevant to everyone
There have been a lot of insane developments in Machine Learning over the last 2 weeks,
Ever since Google showed off PaLM, the game of one-upmanship normally played by tech giants really kicked into the next level. Just 3 days ago, Meta AI fully open-sourced their Large Language Model. The training, data, sources, everything. This is a huge deal for Machine Learning since conventional wisdom was that companies were going to sell their LLMs in embedded products. Meta just flipped the script completely, since people will be able to use their product for free. So what does this have to do with you?
There is a lot of money to be made in this space. And you will be making it. Don’t worry, I won’t tell you all to get into AI Research, Data Analysis, or Data Engineering. Not everyone has the affinity for these. Take me for example. I am exceptional with AI research or Machine Learning Engineering. I do well with Data Analysis. However, there is no amount of money you can pay me to get into Data Engineering. That is how much I hate the field.
Instead, I will focus on a more holistic picture of how you can make money from the investments going into Machine Learning. Why would you do this? There is a lot of money in ML, but not everyone has the love for theoretical math I do. This way you can get involved in the field without going having to slog through what you hate. Keep in mind, that these skills are still important, and you will have to master them. They are just different to traditional Deep Learning.
Let’s get this party started-
Different Ways to Make Money through Machine Learning-
If you read my article, The Best Machine Learning Company of 2021 (you should, it is great), you will know that there is a lot more to Machine Learning than Math and Models (which is what I specialize in). We have to-
Develop the hardware (there is custom hardware for ML)
Build the data sources. For many companies, we also have a lot of storage.
Create the pipelines that will allow us to actually get the data to and from our models (for inference).
Create the platforms that use the ML inference in useful ways.
None of these require ML skills. These are all pure software engineering. This is what we will be focusing on. There are some important skills that you need to learn to be successful here.
Writing Good Tests
You will have to do a lot of testing and checking. Data Quality checks, monitoring different aspects of your platform, and making sure systems are live are some of the important areas. As with all Software Engineering, testing will be extremely important in your career.
Aside from making sure everything works, you will also have to learn to make sure everything is working well. This is because models need not just data, but good data. The old Data Science cliche is Garbage In-Garbage Out. This would mean you have to write tests that account for phenomena like Data Drift. While a Data Analyst will tell you the ways to compute it, you will still need enough ability to be able to interact with the data and attention to detail in spotting unexpected changes.
Data Compression and Handling
With companies spending a lot on storage, you will become very valuable if you can keep up with best practices in Data Compression and Handling it in secure ways. Don’t underestimate this skill. You can generate a lot of money by coming up with efficient or safe ways of storing, querying, and transferring data.
Large Scale System Design
ML makes money at scale. Thus, you want to get very good at designing systems that scale. This will require a broad base of knowledge so that you can make the correct decisions, and a lot of experience. That is why I focus on more than Leetcode style questions. This newsletter is meant to make you better Computer Science, and level up your career. Not just pass the Leetcode Style round of an interview.
Keeping up with ML resources
I can already feel all of you getting angry at me. I promised you no ML, and I have this. However, this is different. Promise.
When I read papers, look at talks, or attend conferences I focus on the idea and its details. You don’t have to care about that. However, with all the money being poured into Machine Learning and Data Analysis, there are a lot of tools being built to handle specific cases/pain points. Technologies like Airflow, Kubernetes, and Snowflake constantly updating. This is true for all of tech, but because of all the money and hype being put into Machine Learning, it will be very true for it in the coming years. Every week, atleast 4 different companies reach out to me, and tell me how they can improve my life in some specific way.
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