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How I Became a Machine Learning Practitioner

blog.gregbrockman.com

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Re: How I Became a Machine Learning Practitioner

#31

Post by IanCal »

streetcat1 wrote: post #12
dhairya wrote: post #11

Earlier quoted context omitted.

No worries, fair question. It worth noting that my job is not data analysis. So I do use data analysis to evaluate our metrics and model performance. Really none of it is really automatable. I'm working developing NLP features for our product (question answering, search, neural machine translation, dialog, etc). Our customer data is diverse, in different formats, and thier use cases are all distinct. So most of my wo…

Thanks. I do assume that the data format is different (alas I also assume that they are all some sort of a text file with known fields and types). But after you setup the dataset definition and defined the schema, the rest can be based on neural search? Moreover, isn't there a state of the art architecture for each of the task. E.g. Seq2Seq for machine translation. Can you just use that as a base line, and let the NA…

One of the difficulties is that the broader scope your optimiser has to push towards a solution, the better your measurements need to be. And having an accurate measure of which thing is "better" can be prohibitively expensive.

The costs of errors varies drastically in different domains and for different use cases, so something important is understanding how and why different models typically fail and making tradeoffs there.

Re: How I Became a Machine Learning Practitioner

#32

Post by threeseed »

jorblumesea wrote: post #13

Does anyone not want to become a ML engineer? Is this the future, and will we even have a choice or else be out of a job?

I started in ML about 7 years ago so well before the hype and back then very few people wanted to be ML engineers.

What's happening at least in Australia now is that contract rates (a good indicator of the supply/demand ratio) has halved for ML engineers. Which means (a) a lot of people want to be ML engineers and (b) there aren't that many jobs for them.

Re: How I Became a Machine Learning Practitioner

#33

Post by applecrazy »

gdb wrote: post #4
copperx wrote: post #3

Earlier quoted context omitted.

Studying Math at Harvard/MIT certainly puts you in a different category than the average software engineer. And if ML was still challenging to Greg, it is honestly a bit discouraging.

(I wrote the post.) If it's helpful, I dropped out of both schools — the vast majority of my knowledge is self taught!

Question: will the OpenAI fellows curriculum ever be released? I need a nice, structured intro to deep learning research and feel like the curriculum modules your company has developed would have the highest quality.

(For reference, I’m an undergrad looking to get into this field)

Re: How I Became a Machine Learning Practitioner

#34

Post by _ptgt »

streetcat1 wrote: post #12
dhairya wrote: post #11

Earlier quoted context omitted.

No worries, fair question. It worth noting that my job is not data analysis. So I do use data analysis to evaluate our metrics and model performance. Really none of it is really automatable. I'm working developing NLP features for our product (question answering, search, neural machine translation, dialog, etc). Our customer data is diverse, in different formats, and thier use cases are all distinct. So most of my wo…

Thanks. I do assume that the data format is different (alas I also assume that they are all some sort of a text file with known fields and types). But after you setup the dataset definition and defined the schema, the rest can be based on neural search? Moreover, isn't there a state of the art architecture for each of the task. E.g. Seq2Seq for machine translation. Can you just use that as a base line, and let the NA…

>But after you setup the dataset definition and defined the schema, the rest can be based on neural search?

Sure, but hyperparameter tuning and architecture selection takes such an insignificant amount of any competent ML practitioner's time so as to be pretty much irrelevant.

At least for me, my time is mostly spent: 1. Understanding (or designing) the process that generated the data. 2. Organizing the training schema. 3. Understanding the customer's business problem so that an appropriate ML system can be designed. 4. Doing an initial design of the ML system based on that understanding and then iteratively designing new components for said system based on customer feedback. 5. Developing or researching how to measure model performance. 6. Searching for alternative data sources. 7. Answering customer and stakeholder questions about the ML system 8. Implementing the ML system in code.

None of these can be automated with current technology, and there's a reason for that: if it was possible to automate a task then our team already would have.

Re: How I Became a Machine Learning Practitioner

#35

Post by streetcat1 »

dhairya wrote: post #27
streetcat1 wrote: post #12

Earlier quoted context omitted.

Thanks. I do assume that the data format is different (alas I also assume that they are all some sort of a text file with known fields and types). But after you setup the dataset definition and defined the schema, the rest can be based on neural search? Moreover, isn't there a state of the art architecture for each of the task. E.g. Seq2Seq for machine translation. Can you just use that as a base line, and let the NA…

Happy to talk more offline, my email is my profile. The short answer is no because there are more complexities involved - both related to our specific use cases but really natural language in general. If that were the case NLP would be solved and any company that could exist would already. From my experience, I'm not sure where the line is between choosing the right model vs having the right data solves most problems…

Thanks for the answer. I am happy to discuss offline. While I did my master on computational linguistics which is related to your field, I am currently creating a new auto ml platform so I would appreciate your feedback. My goal is to automate the straightforward parts.

As you mentioned, some tasks in NLP like full conversation are not solved and will likely never be solved with deep learning by itself (at the level of the conversation). There should be some sort of symbolic AI or taxonomies/knowledge graph (like RDF) in combination with deep models.

Re: How I Became a Machine Learning Practitioner

#36

Post by bitL »

jorblumesea wrote: post #13

Does anyone not want to become a ML engineer? Is this the future, and will we even have a choice or else be out of a job?

ML engineer is a super boring job content-wise and has insane outside pressure. It's about building data pipelines, the ugly grunt work. ML/Data Scientist is the interesting job. Usually Data Scientists view ML Engineers as replaceable drones that don't understand anything interesting and do the boring part of the job for 2-3x less than they do. The only advantage of ML Engineers is that AutoML is unlikely going to replace some dirty work but might endanger outdated Data Scientists.

Re: How I Became a Machine Learning Practitioner

#37

Post by TrackerFF »

sevensor wrote: post #24
dhairya wrote: post #10

Earlier quoted context omitted.

So there is a difference between ML research and application. Being a practitioner doesn't require deep math knowledge that perhaps research would. Jeremy Howard's fastai course is a great example of how someone with a solid programming background can effectively transition into being a deep learning practitioner. Given that production ml and deep learning is still the wild west, as a practioner, you can contribute a…

I recently got the assignment to "do ML" on some data. I hadn't done anything in the area before, and a couple of things surprised me: 1. Most of your time is spent transforming data. Very little is spent building models. 2. Most of the eye-grabbing stuff that makes headlines is inapplicable. My application involves decisions that are expensive and can be safety critical. The models themselves have to be simple enoug…

The longer you work with ML, the more you discover that it's almost exclusively about handling data.

It's also one critique I have to the world of academia. When learning ML in academia, 9 of 10 times you work with clean and neat toy datasets.

Then you go out in the "real world" and instantly get hit with reality: You're gonna spend 80% of the time fixing data.

With that said, I think that 10 year from now, ML is going to be almost exclusively SaaS with very high levels of abstraction, with very little coding for the average user. Maybe some light scripting here and there, but I mostly just drag'n drop stuff.

Re: How I Became a Machine Learning Practitioner

#38

Post by nilkn »

bitL wrote: post #36
jorblumesea wrote: post #13

Does anyone not want to become a ML engineer? Is this the future, and will we even have a choice or else be out of a job?

ML engineer is a super boring job content-wise and has insane outside pressure. It's about building data pipelines, the ugly grunt work. ML/Data Scientist is the interesting job. Usually Data Scientists view ML Engineers as replaceable drones that don't understand anything interesting and do the boring part of the job for 2-3x less than they do. The only advantage of ML Engineers is that AutoML is unlikely going to r…

I might disagree on this. The software engineering behind production machine learning systems can be quite interesting and nontrivial. It really depends on the scope of the challenges being faced. If you have thousands of models that need to be served in production and continually retrained and monitored, that becomes a pretty sophisticated problem space to work in.

Re: How I Became a Machine Learning Practitioner

#39

Post by kadder »

jorblumesea wrote: post #13

Does anyone not want to become a ML engineer? Is this the future, and will we even have a choice or else be out of a job?

It all depends on definition of ML engineer. Low latency high tps model serving as others have described is an interesting problem in its own right, so is management of thousands of models, add repeatability and lineage tracking to the mix and you have significant engineering challenges - |via http://bit.ly/hnbuzz01 |

Re: How I Became a Machine Learning Practitioner

#40

Post by whymauri »

bitL wrote: post #36
jorblumesea wrote: post #13

Does anyone not want to become a ML engineer? Is this the future, and will we even have a choice or else be out of a job?

ML engineer is a super boring job content-wise and has insane outside pressure. It's about building data pipelines, the ugly grunt work. ML/Data Scientist is the interesting job. Usually Data Scientists view ML Engineers as replaceable drones that don't understand anything interesting and do the boring part of the job for 2-3x less than they do. The only advantage of ML Engineers is that AutoML is unlikely going to r…

Except for the part where data science is an incredibly broad term and the majority of the positions are seemingly what used to be called 'data analyst'.

It makes finding good positions really hard.

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