Top 6 ML Engineer Interview Questions (with Snapchat MLE)
Summary
TLDRIn 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
📚 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.
🔍 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.
🔧 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.
🛠️ 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.
Mindmap
Keywords
💡Training Data
💡Testing Data
💡Hyperparameters
💡Validation Set
💡Gradient Descent
💡Batch Gradient Descent
💡Mini-batch Gradient Descent
💡Stochastic Gradient Descent
💡Feature Scaling
💡Classification
💡Regression
💡Concept Drift
💡Exploding Gradients
💡Model Refresh
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]
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