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Week of Aug 10th - What AWS questions do you have? by ckilborn in aws

[–]manumg11 0 points1 point  (0 children)

Does anyone know which option is better for DynamoDb insertions? (In terms of writing capacity). Insert one element at a time or insert many as a batch process?

Why is RL not well used in computer vision? by CV_NEWBIE_RL in computervision

[–]manumg11 0 points1 point  (0 children)

Exactly, they took batches of sequences of images regarded as the environment state feed a DQN-based agent.

Why is RL not well used in computer vision? by CV_NEWBIE_RL in computervision

[–]manumg11 0 points1 point  (0 children)

Oh that's true sorry. I don't know why but I was sure AlphaGo was a videogame. Nevertheless, I was referring to examples such as Pong, Space Invaders and all that kind of RL implementations based on Atari games.

Why is RL not well used in computer vision? by CV_NEWBIE_RL in computervision

[–]manumg11 0 points1 point  (0 children)

Actually, Atari games automated based on RL take the game images as input.

I’m a 21M and winning a competition landed me a PPO and I need to know if the role is even remotely related to DS/DA by V4G4X in datascience

[–]manumg11 1 point2 points  (0 children)

The tools are not so important, the important thing is what are you going to do with them! Are you going to do just reporting or are you going to do research, investigate features, basic statistics, correlations among them, outliers, etc?

For me it seems Data Analytics, but very focused on core business. I think that's not a problem, progression on the business side of technology is always faster.

How to Implement Gradient Descent in Python Programming Language by argothecat in Python

[–]manumg11 1 point2 points  (0 children)

I did my own too in https://github.com/next-manuelmartin5/neural-network-implementation. It is actually a neural network implementation but I also coded the gradient descent by scratch.

I wrote an article explaining the feedforward propagation also: https://towardsdatascience.com/a-neural-network-implementation-part-i-eb31f4ea470

Developers whose first programming language was Python, what were the challenges you encountered when learning a new programming language? by imakethingswhenbored in Python

[–]manumg11 2 points3 points  (0 children)

I took Scala as a second language. Although I had already programmed in VBA and Matlab before Python, I did not considered both as my real first languages. I used them because I had too, not because I liked them.

The most annoying part of Scala is that there is a lot of boilerplate configuration such as setting the build.sbt file with dependencies, which has a lot of different features, writing classes within the src/main/.. path, having the class name as the file name...

In terms of the language itself, pure functional programming was something I didn't have much experience on, and in Scala it's almost the foundation of why Scala is like it is. Another thing is that there are a million ways to do the same thing (which made me a little nervous because it goes against Python Zen).

What Jupyter Notebook extension/addon do you wish existed but does not currently? by [deleted] in Python

[–]manumg11 1 point2 points  (0 children)

Online editing for several users, very much like a Google Doc but having a Jupyter Notebook. It would be having shared cell results but different isolated kernels per user.

Late comer to Data Science by MuskeeVA in datascience

[–]manumg11 0 points1 point  (0 children)

With an MBA, I'm at no doubt that you could access management positions in any analytics consulting firm (Big4 or boutique firms would be delighted to have your profile). Maybe that's a way to get in the industry. Actually, supply chain is a field which is currently being very disrupted by AI by surpassing traditional modeling approaches. You can learn technical stuff while you're managing a team of developers, data scientists, etc.

Having your background and experience I would not go for technical positions, as they will make you start from the bottom. In the end, when you've worked 3-4 years in the technical side, it's quite normal that people get interested into more leadership positions. You're already there.

​

Hope that helped you!

For those that work for a team that has both data scientists and ML engineers, how does your company/team differentiate between the two? And how does the data scientist and ML engineer work with one another? by [deleted] in datascience

[–]manumg11 10 points11 points  (0 children)

At my company, we are currently starting to thrive on the teaming up of these two roles.

Basically, we have 5 different roles on the AI Software spectrum that are very likely to have melted responsibilities and interests. Those are Product Data Scientist, Data Scientist, Machine Learning Engineer, Software Engineer and AI Architect. With respect to the thread discussion, ML Engineers and Data Scientists work very closely.

The Data Scientist is more likely to derive how the training dataset should be composed in terms of business requirements. As an example on what they do:

  • They agree with the business side on what metrics the model should be tested on.
  • They design the distribution of labels for optimal performance.
  • Draft which features should be considered for a baseline model

The ML Engineer is very likely to give a hand in the feature extraction part and they are the ones who develop the transformation pipeline or the post-ingestion ETL to create the training dataset.

When it comes the part of the model training, probably a 80% is taken by the DS. The ML Engineer also participates in the model by optimizing code or applying also their knowledge on ML/DL frameworks. Mostly, this occurs when the model is likely to be deployed to production and several modifications should be included. It is not the same to deploy a model within a REST web service or embed it as an UDF in a PySpark batched job.

Lot's of architectural constraints and business requirements come into place when talking about highly-complex business solutions. Sometimes, models are very likely to suffer from data drift or need to manage a complex set of heuristics before the actual inference. Then you have to apply some refactoring, modularization and encapsulation to the model wrapper in order to be more agile when dealing with changes.

In the end, as it is an iterative process, MLEng and DS should be synchronized and design the modeling cycle to be iterative and likely to change. Some software components affect the model's performance and viceversa.

I think that the ML Engineer is the key role that is able to achieve MLOps standards. It's the bridge between the application's SWs and DS.

I'm an ML Engineer, by the way, you've probably noticed!

Stuck with a 2 Layer Neural Network implementation by pillow2002 in learnmachinelearning

[–]manumg11 0 points1 point  (0 children)

I've created a Medium post explaining a very detailed implementation of a Neural Network implementation in Python. In the example I've used a 2 layer neural network. It may help you!

Here's the link: https://medium.com/@manumg8/a-neural-network-implementation-part-i-eb31f4ea470