Top 6 ML Engineer Interview Questions (with Snapchat MLE)

Summary

In this insightful interview, machine learning engineer Raj from Snapchat discusses fundamental concepts such as training and testing data, hyperparameter tuning, and optimization algorithms like batch gradient descent. He addresses the challenges of non-convex loss functions, the importance of feature scaling, and the distinction between classification and regression. Raj also shares practical insights on model deployment, monitoring for concept drift, and strategies to handle exploding gradients, emphasizing the importance of domain-specific considerations in machine learning.

Takeaways

• 📘 Training data is the portion of data used by a machine learning algorithm to learn patterns, while testing data is unseen by the algorithm and used to evaluate its performance.
• 🔧 Hyperparameters, such as the number of layers or learning rate in a neural network, are tuned using a validation set, which is a part of the training data.
• 🔍 The final model evaluation is performed on the test data set, which should not influence the learning process or hyperparameter tuning of the model.
• 🛠 Gradient descent optimization techniques include batch gradient descent, mini-batch gradient descent, and stochastic gradient descent, each with different approaches to updating model parameters.
• 🧩 Batch gradient descent uses the entire training set for each update, mini-batch gradient descent divides the training set into smaller groups, and stochastic gradient descent involves random shuffling and smaller batches.
• 🔄 The choice between different gradient descent techniques often depends on memory requirements and the desire to introduce noise to prevent overfitting.
• 🏔 Optimization algorithms do not guarantee reaching a global minimum in non-convex loss functions, often settling in a local minimum or saddle point.
• 🔄 Feature scaling is important for algorithms that use gradient-based updating, as it helps to stabilize and speed up convergence by normalizing different scales of features.
• 🔮 Classification predicts categories, while regression predicts continuous values; the choice depends on the nature of the outcome variable and the problem context.
• 🔄 Model refresh in production is triggered by a degradation in performance, which can be monitored through various metrics and by comparing with the training set performance.
• 🔁 Concept drift is a common reason for performance degradation in production, where the relationship between input features and outcomes changes over time.
• 💥 Exploding gradients in neural networks can be mitigated by gradient clipping, batch normalization, or architectural changes like reducing layers or using skip connections.

Q & A

What is the purpose of training data in machine learning?

Training data is used by a machine learning algorithm to learn patterns. It helps in choosing the parameters of the model, such as those of a logistic regression algorithm, to minimize error on the training set.

Why is testing data important in machine learning?

Testing data is crucial as it is data that the algorithm has not seen before. It is used to evaluate the performance of the model without bias, ensuring that the model's performance is gauged on data other than what it was trained on.

What are hyperparameters in the context of machine learning?

Hyperparameters are parameters that are not learned from the data but are set prior to the training process. They include aspects like the number of layers in a neural network, the size of the network, or the learning rate. They are tuned using a validation set to maximize performance.

How does the validation set differ from the training set and test set?

The validation set is a portion of the training data used to tune hyperparameters. It is not used in the learning process of the algorithm but to adjust the model's hyperparameters. The training set is used to learn the model, and the test set is used to evaluate the final model's performance.

What is the difference between batch gradient descent, mini-batch gradient descent, and stochastic gradient descent?

Batch gradient descent uses the entire training set to compute the gradient and update parameters at once. Mini-batch gradient descent divides the training set into smaller batches and updates parameters using each mini-batch. Stochastic gradient descent shuffles the training set and updates parameters using small random batches, introducing more randomness.

Why might one choose to use mini-batch gradient descent over batch gradient descent?

Mini-batch gradient descent can be chosen over batch gradient descent due to memory requirements, as it allows for the processing of smaller subsets of data that can fit into RAM or a GPU. It also adds noise to the gradient computation, which can act as a regularizer and help prevent overfitting.

Are optimization algorithms guaranteed to find a global minimum for non-convex loss functions?

No, optimization algorithms are not guaranteed to find a global minimum for non-convex loss functions. They often converge to a local minimum or a saddle point, which may still be a good solution depending on the performance on validation and test sets.

Why is feature scaling important in machine learning?

Feature scaling is important because it helps in normalizing the range of independent variables or features of data. This ensures that the features contribute equally to the result and helps in faster convergence of gradient-based machine learning algorithms.

What is the difference between classification and regression in machine learning?

Classification predicts a discrete outcome, often a category such as yes or no, while regression predicts a continuous numerical value. The choice between them depends on the nature of the problem and the type of outcome variable being predicted.

How can you tell when it's time to refresh a machine learning model in production?

A model may need to be refreshed when its performance degrades, which can be detected by monitoring metrics like precision, recall, loss, or accuracy. If the performance in production does not match the training performance, it might be time to update the model.

What is concept drift, and how can it affect a machine learning model's performance?

Concept drift refers to changes in the relationship between input features and the outcome variable over time. This shift in the underlying data distribution can cause a model's performance to degrade as the assumptions it was trained on no longer hold true.

How can exploding gradients be managed during the training of neural networks?

Exploding gradients can be managed by gradient clipping, which limits the value of gradients to a certain threshold, or by using batch normalization to stabilize the gradients. Additionally, adjusting the network architecture, such as reducing the number of layers or using skip connections, can help mitigate this issue.

Outlines

00:00

📚 Introduction to Machine Learning Fundamentals

The video script begins with an introduction to the concepts of training and testing data in machine learning. Raj, a machine learning engineer at Snapchat, explains that training data is used by algorithms to learn patterns and minimize error, while testing data assesses the algorithm's performance on unseen data. Hyperparameters like the number of layers or learning rate in neural networks are tuned using a validation set, separate from the training data. The importance of using different data sets to avoid biased performance evaluation is highlighted.

05:02

🔍 Deeper Dive into Training Data and Model Evaluation

This paragraph delves into the specifics of model training and evaluation. It discusses the use of batch gradient descent, mini-batch gradient descent, and stochastic gradient descent as optimization techniques. The differences between these methods in terms of how they handle the training set for parameter updates are explained. The paragraph also touches on the choice of optimization algorithm based on memory requirements and the potential for overfitting when the training set's order influences the model.

10:05

🔧 Handling Exploding Gradients and Model Deployment

The script addresses the challenge of exploding gradients in neural networks during backpropagation and offers solutions such as gradient clipping, batch normalization, and architectural choices like reducing the number of layers or using skip connections in architectures like the Transformer. It also covers the considerations for model deployment, including monitoring performance metrics and refreshing models when there's a significant deviation from the training performance.

15:06

🛠️ Model Performance and Concept Drift

The final paragraph discusses reasons for discrepancies in model performance between development and production environments, such as concept drift where the underlying data distribution changes. It emphasizes the importance of monitoring data and prediction distributions, as well as confidence scores, to detect when a model's performance degrades. The conversation wraps up with a reflection on the interview, suggesting the inclusion of a case study for a more applied perspective on the discussed topics.

Keywords

💡Training Data

Training data refers to the dataset used by a machine learning algorithm to learn patterns and make predictions. It is the foundation upon which the algorithm builds its understanding. In the script, Raj explains that the parameters of algorithms like logistic regression are chosen to minimize error on the training set, emphasizing its importance in the learning process.

💡Testing Data

Testing data is a separate dataset that the algorithm does not see during training. It is used to evaluate the performance of the trained model to ensure that it can generalize well to new, unseen data. The script mentions the importance of not gauging the model's performance on the same data it was trained on, highlighting the need for an unbiased evaluation.

💡Hyperparameters

Hyperparameters are configuration settings of a machine learning algorithm that are set prior to the start of the training process. They include parameters like the number of layers in a neural network or the learning rate. The script discusses how a validation set, a subset of the training data, is used to tune these hyperparameters to maximize performance.

💡Validation Set

A validation set is a portion of the training data used for tuning hyperparameters. It helps in selecting the best model configuration without affecting the learning process of the algorithm itself. The script explains the role of the validation set in the model development process, ensuring that the final model is not influenced by the data used for hyperparameter tuning.

💡Gradient Descent

Gradient descent is an optimization technique used to minimize a loss function by iteratively moving in the direction of the steepest descent as defined by the negative of the gradient. The script describes different variations of gradient descent, such as batch, mini-batch, and stochastic gradient descent, and their applications in machine learning.

💡Batch Gradient Descent

Batch gradient descent is a variant of the gradient descent algorithm where the entire training set is used to compute the gradient and update the parameters in one go. The script mentions this method in the context of optimization algorithms, explaining its memory requirements and computational intensity.

💡Mini-batch Gradient Descent

Mini-batch gradient descent divides the training set into smaller subsets, or mini-batches, and computes the gradient for each batch to update the model parameters. The script explains that this method adds noise to the gradient computation, which can help prevent overfitting and is more memory efficient than batch gradient descent.

💡Stochastic Gradient Descent

Stochastic gradient descent involves shuffling the training set and computing the gradient using small random batches of data to update the model parameters. The script discusses this method as a way to introduce randomness in the training process, which can help avoid overfitting and is computationally efficient.

💡Feature Scaling

Feature scaling is a preprocessing step where features of the data are scaled to a common range or unit, often improving the performance of gradient-based machine learning algorithms. The script highlights the importance of feature scaling in stabilizing gradient descent and speeding up the convergence of the algorithm.

💡Classification

Classification is a type of supervised learning where the algorithm predicts a category or class for the input data. The script differentiates classification from regression by explaining that classification predicts discrete labels, such as 'yes' or 'no', while regression predicts continuous values.

💡Regression

Regression is another type of supervised learning where the algorithm predicts a continuous numerical value for the input data. The script uses the example of predicting a person's height to illustrate how regression is used to forecast continuous outcomes.

💡Concept Drift

Concept drift refers to the change in the statistical properties of the input and output variables over time, which can lead to a degradation in the performance of a machine learning model. The script discusses concept drift as a potential reason for model performance differences between development and production environments.

💡Exploding Gradients

Exploding gradients occur when the gradients in a neural network become excessively large during backpropagation, causing instability in the training process. The script suggests methods to handle this issue, such as gradient clipping, batch normalization, and architectural changes to mitigate the problem.

💡Model Refresh

Model refresh refers to the process of updating or retraining a machine learning model to maintain its performance as new data becomes available or when the underlying patterns in the data change. The script touches on the importance of monitoring model performance and refreshing the model when its performance degrades.

Highlights

Training data is used by a machine learning algorithm to learn patterns, while testing data evaluates the algorithm's performance without prior exposure.

Hyperparameters like the number of layers or learning rate in a neural network are tuned using a validation set derived from the training data.

Batch gradient descent, mini-batch gradient descent, and stochastic gradient descent differ in how the training set is divided for computing gradients and updating parameters.

Memory requirements often drive the choice between batch, mini-batch, and stochastic gradient descent due to the size of datasets.

Optimization algorithms do not guarantee reaching a global minimum in non-convex loss functions, often converging to local minima or saddle points.

Feature scaling is crucial for algorithms that use gradient-based updating to ensure stability and faster convergence.

The choice between classification and regression depends on the type of outcome predicted, with classification predicting categories and regression predicting continuous values.

A problem can be formulated as either classification or regression, depending on how the outcome variable is treated.

Model refresh in production is triggered by a degradation in performance, benchmarked against the initial training set performance.

Monitoring data distributions and prediction confidence scores can indicate when a model in production needs refreshing.

Concept drift, where the relationship between input features and outcomes changes, is a common reason for model performance decline in production.

Exploding gradients in neural networks can be mitigated by gradient clipping, batch normalization, or architectural changes.

Different models may have varying sensitivity to distribution drift, affecting their robustness in production environments.

Practical machine learning solutions often require domain-specific insights and cannot rely solely on one-size-fits-all approaches.

Incorporating concrete examples or case studies can enhance understanding of machine learning theories and their applications.

The interview covered a broad range of topics in machine learning, providing a comprehensive overview of key concepts and practices.

The discussion on formulation of problems and the differences between classification and regression provided valuable insights into machine learning approaches.

The interview emphasized the importance of considering the specific domain and problem when applying machine learning techniques.

Transcripts

I'd love if you could tell me about the

uh the terms training data and testing

data in the context of machine

[Music]

learning okay thank you so much for

being here with us today Raj can you

quickly introduce yourself our

viewers yeah absolutely thanks so much

for having me uh my name is Raj and I'm

currently a machine learning engineer at

Snapchat uh where I work on a lot of

stuff related to generative AI

initiatives at the company that's really

cool really popular nowadays and I feel

like there's so many products cool

products being built with generative AI

so really interested to hear the

insights that you have in our interview

today so to get started I'd love if you

could tell me about the uh the terms

training data and testing data in the

context of machine

learning yeah absolutely so training

data generally will refer to the portion

of the data that a machine learning

algorithm uses to learn patterns so for

example the parameters of a logistic

regression album algorithm rather can be

chosen such that the error is minimized

on the training set the testing set is a

data that is not seen by the actual

algorithm and is used purely to gauge

the algorithm's

performance yeah that makes sense right

because you don't want to um gauge the

model's performance on the same data

that it was trained on so what I'm

curious about then is uh you mentioned

that you want to minimize the error on

the training data set what if though

your algorithm involves like parameters

that you need to tune so for example

like the number of layers or like the

size of your neural network or learning

rate um if you need to tune those

parameters uh do you tune it on the

training data like what do you do

instead yeah so generally those are

called hyper parameters and so what

people will typically do is take out a

portion of the training data and call it

a validation set and then they will tune

those hyper parameters to maximize the

performance on the validation set okay

perfect so now you have training data

set and a validation data set and uh so

then again which data set do you

actually evaluate the final model on

typically you will evaluate it on your

test data set at the final step um

usually you should only be using your

validation set to tune these

hyperparameters but you should kind of

have this hold outet that uh never has

any information that is used to inform

uh the learning of the actual algorithm

as well as the hyper parameters yeah so

then moving on So speaking of training a

model uh there's many different

optimization algorithms for doing so

right so could you tell me about the

differences between some of them

specifically between batch gradient

descent mini batch gradient descent and

stochastic gradient descent sure yeah so

firstly uh gradient descent is an

optimization technique like you

mentioned that is used to find the

minimum of a loss function uh so

specifically the GR can be calculated by

taking the derivative of the loss with

respect to the parameters of a

particular algorithm and since the

gradient descent actually represents the

direction of the steepest descent it can

be used to take gradual steps towards

the minimum of that loss function um

kind of back to your question of those

differences uh those terms refer to uh

different but related ways of dividing

up the trading Set uh comp the actual

gradient and then performing the actual

parameter updates so batch gradient

descent is when you use the entire

training set on one go and you compute

the gradient and then you do a single

step of gradient design mini batch

gradient descent is when you divide up

the training set into what are called

mini batches and you typically choose a

batch size um and then you will

separately compute the gradients on

those midi batches and then you will

take a step in that direction for each

one of those mini batches stochastic

gradient desent uh is related to both

batch and mini batch gradient Des set

but it mostly refers to shuffling up the

training set randomly and then similarly

you would divide that up into smaller

batches and then compute the gradients

on those batches and then perform the

respective parameter

updates okay yeah that generally makes

sense so I'm curious then when might you

choose to use for example like full

batch gradient descent versus mini batch

versus St stochastic like why are there

different ones and why do people choose

a specific one to

use yeah so uh people typically choose

to split up the data into batches

because of memory requirements so if you

have a data set that has millions of

data points for example uh unfortunately

you cannot usually fit that into memory

when actually doing gradient Des set and

so in practice uh people will divide

these up into many badges so it can fit

into your RAM of a GPU let's say and

then you know periodically you can

compute these updates and then gradually

kind of lower the loss function um mini

batch gradient descent is also kind of

used as a regular a regularizer rather

um to prevent overfitting on the

training set because it adds a little

bit of noise to the actual uh gradient

that Computing on these mini batches in

response to the stochastic part of it um

well let's say that you have a

particular training set that has

patterns that are underlying in the

order of the training set you don't want

to overfit your training data uh or

rather the training of your model to any

order that could be representative

within your training set and so people

will use stochastic grad grading descent

to make sure uh you know that that

shuffling kind of takes out that

variable of the order within the trading

data set okay perfect yeah that makes a

lot of sense because with um deep

learning a particular there's often very

strict like memory requirements and

these models are typically quite large

uh so it makes sense that we would have

variations at account for that um so I'm

also curious then you mentioned that we

use these optimization algorithms to try

to decrease the loss um so I would

assume you want the loss to reach some

kind of minimum but a lot of loss

functions that are encountered nowadays

are actually

non-convex so can you tell me are um any

of these optimization algorithms that we

just talked about guaranteed to reach a

global

Minima in the case of a non-convex

function they are not guaranteed to

reach a global minimum in fact uh

usually they don't reach a global

minimum in the case of neural networks

they usually have a lot of different

Minima uh and so usually it'll converge

to some sort of local minimum or

possibly a saddle point okay yeah and if

the Alm reaches a local minimum instead

are there issues with that like is that

generally still a good model what do you

think uh so you know it depends it could

be a good algorithm um you might want to

try different uh parameter

initialization techniques to see if

you're able to get to different Minima

within the actual model that you're

training however that is dependent on

factors such as how it perform forms on

your validation set and ultimately on

your test set um it kind of just depends

how exactly you'd like to go about it

however a way of uh potentially getting

to a new minimum is using a different

parameter initialization technique now

that you've started training you need to

actually prepare your data for training

um so when you're preparing the data

something that people often do is they

do feature scaling or they do

normalization uh can you tell me a

little bit more about the importance of

these particular pre-processing steps in

machine learning yeah so feature scaling

is really important for training machine

learning algorithms that uh do gradient

based updating like we were just

discussing and the reasoning behind that

is because uh often features have

different orders of magnitude and so the

derivatives of the loss with respect to

those input parameters will be on

different scales as well and so when you

have them on different scales gradient

descent on on normalized features tend

to be unstable and converge slower and

so feature scaling can be a way of

getting an algorithm to converge faster

so now that you've like prepared your

data let's say that you're actually

trying to figure out what type of

learning problem that you're uh tackling

with your machine learning approach um

so some common types of learning

problems include classification and

regression can you tell me a little bit

about the differences between those yeah

so classification and uh classification

aggression rather refer to the type of

outcome predicted by a supervised

machine learning algorithm uh and so in

the case of classification that will

usually predict some sort of category so

in the simplest case a yes or a no while

as regression will be predicting some

sort of numerical or continuous value

for example a person's

height okay uh can you foresee instances

where uh a problem could be both CL

ification or regression and if so why

might you choose one or the

other sure so let's say there was a case

where the outcome was a numerical

variable and so of course you could use

regression to formulate that problem

however you could also bin the different

values into different categories right

so for example in the case of height

maybe you can bend them based on ranges

so you could have one that says low one

that says medium one that says high and

then you can turn that into a

classification problem uh and I think

the general reasoning is making uh it

easier for the algorithm to distinguish

and learn based on the actual patterns

underlying in the data uh sometimes uh

for example in the case of the highight

uh the scale is kind of all over the

place um you know there's kind of a

bigger

that you have to be able to predict um

and so getting the underlying pattern of

whether it's in the medium range or the

higher range might be something that's

easier for the algorithm to learn and it

can also be something that is perhaps

more useful for the algorithm to learn

um so I think it just depends on the use

case that you have and and what makes

most sense for you know your particular

problem right yeah and a lot of these

intuitive insights uh that you have

about the data can be really important

when it comes to like feature

engineering or data pre-processing so

now let's assume your model's fully

trained you've deployed it into

production congratulations um okay and

it's been in production for a little bit

of a while now and you've been

monitoring it um and just like you've

been measuring various metrics how might

you be able to tell um when it's time to

actually refresh the model that's in

production yeah so typically a model

will need to be refreshed when there is

a degrading in performance of the

algorithm so generally you will

Benchmark the performance on some sort

of training set and perhaps at some

point you see that the performance of

your data and production is not matching

up to uh the performance on the training

Set uh and so some of the ways that you

can tell is basically just using some of

the metrics that you chose for your

initial problem uh for example if a

Precision metric or a recall metric uh

or perhaps the loss or the accuracy all

of that assumes that you do have for

example the ground truth label for the

data incoming and production um however

you know that is possible for certain

cases and that can be used as a way to

Benchmark and see if it's actually

differing from your training performance

it isn't always that straightforward

because you don't always have that

source of grand truth and production so

alternative strategies can include

monitoring data distributions of the

input features for your model as well as

prediction distributions and also

confidence scores from the algorithm

itself so it really depends kind of on

your use case and there's no one best

way to do it however there are

definitely different ways of you know

helping solve that R okay yeah um I like

how you mentioned that it's really on a

case-by Case uh basis you got to really

uh look into like the domain specifics

of your problem and think about it that

way so can you give me um some reasons

or um some in insights for why model

performance might actually differ uh in

production versus uh in

development yeah so there's a lot of

possible reasons for why this could

actually happen in production however I

can give one example which is something

called concept drift which is where the

relationship between the input features

and the actual outcome variable changes

another way of thinking about it is that

typically a supervised machine learning

model is represented by the probability

distribution of probability of Y given X

so concept drift is when this underlying

distribution actually changes and so all

the assumptions when you trained your

model don't actually apply anymore so

that can often be a common reason as to

why uh performance isn't matching what

you would expect okay perfect yeah

because um if you trained on one set of

data and the new set set of data is

pretty different uh it's very possible

for your model to just like not uh not

be trained well on that new distribution

during training itself like there could

be a lot of irregularities that happen

so sometimes uh something known as an

exploding gradient which is when the

values of your gradient become really

really large um and cause uh can cause

training its abilities um can you tell

me how you might handle that yeah so

like you mentioned the exploding

gradients is really because of uh back

propag

in a neural network and specifically

when there are success success of layers

in a network uh for which the gradients

need to be computed and typically those

are calculated with the chain Rule and

so that involves multiplications of many

different gradients um and so one way of

handling it is straight up just clipping

the gradients at a certain threshold

kind of like a brute force and just

saying that hey if it exceeds this value

it's too much and we don't want to

result in unstable training so that's

one way of doing it you could also use

what's become a lot more common in the

past few years which is batch

normalization which is basically using a

type of normalization after a particular

layer or activation and then uh taking

the mean and standard deviation B based

on the batch of examples and this can

help scale the gradients to more

reasonable stable values that's a second

way of doing it uh what people also do

is they also change their architecture

or choose their architecture to help

mitigate these exploding gradients um so

you could directly just reduce the

number of hidden layers which will uh

therefore reduce the amount of

multiplications that need to happen for

the chain rule you could also choose

architectures for example the

Transformer with skip connections and

Skip connections are basically Pathways

from certain layers to layers further

down in the network rather than the

layer that directly follows it and so

that kind of gives uh the network a

pathway for the gradient to follow

without having to pass through several

consecutive layers and this can

definitely help mitigate the exploding

gradient problem okay perfect yeah I

like that you suggested a couple of

different approaches B based both on the

model architectures themselves and the

data set um Okay cool so I think this is

a really great place to pause thank you

so much for answering all these

questions today um I'm really curious to

hear your insights about this though

like if you were the interviewer uh how

did you feel about this interview what

do you think well and is there anything

that you think you would have done

differently I think that uh the

interview touched on some really

important topics in machine learning um

I think that these are all relevant

topics for example the exploting

gradient is extremely common in training

neural networks and neural networks have

obviously become super common in AI

they're very widely used um so I really

liked that I like that we also touched

on some of the basic fundamentals as far

as how you formulate a problem I really

liked how we talked about the

differences between classification and

regression and also the fact that uh

those can actually be formulated

differently you don't always have to

follow a certain format and it really

just differs on your use case

um I think that it would have been nice

to uh have some sort of like case study

maybe like very Mini case study you know

not something that takes the entire

interview uh but maybe where we asked a

hypothetical scenario and said okay well

what might you do in this case and I

think that that was present to some

extent uh but perhaps we could have

applied it to like a specific domain or

a particular company yeah I agree often

times I think having that kind of

concrete example or a case study really

helps us understand like why the the

theory applies or why these techniques

were invented in the first place you

know uh but I do think that actually you

had gave quite a few like good like

small concrete examples for example like

the height problem we talked about that

in the case of like classification

versus regression um yeah we talked a

little bit about some examples of like

concept drift as well uh so I thought

that that was actually very helpful and

I really like that a lot of your answers

Incorporated you talking about how um

there's not necessarily A one- siiz fits

all machine learning like solution a lot

of the times you have to pay attention

to your particular domain or the

particular problem that you're trying to

learn um so a lot of it really does

depend upon like you just like looking

at your data and thinking about like

what does this model do what do you want

this model to ideally do uh for the user

um so that was uh I thought that that

was really well done so as for what we

might have been able to elaborate more

on uh so I believe for the question

where we talked a little bit about um a

potential reason for why model

performance uh might differ in

production what you might see that uh

regression um maybe we could have also

talked about like uh how some models are

more sensitive than others to uh

distribution drift and sometimes we also

call that o o generalization out of

distribution generalization even if a

model has only seen like quote unquote

in distribution data which is like the

data that you've seen in development um

some models may have like wider decision

boundaries than others which tends to

make them a little bit less sensitive to

distribution DFT and a drift and a

little bit more robust in general so

that's uh something that would have been

interesting to touch upon but in general

like you covered so many topics uh so

like thoroughly uh so I think we all

learned a lot from you today so thank

you for being here yeah yeah thanks so

much for having me yeah and thanks

everybody for watching uh if you have

any machine learning interviews coming

up good luck thank you for watching bye

everyone

[Music]