What is a Machine Learning Engineer
Summary
TLDRThe video script introduces the role of a machine learning engineer, who bridges the gap between data science and practical application by creating software that utilizes machine learning to add value to businesses. Alexander Kondoforov, a Data Science Competence Lead at LTCH Soft, explains that machine learning engineers focus on building features for products or automating workflows, such as decision support systems. They start by selecting and preparing data, choosing an appropriate algorithm, and then training the model with a training dataset. After training, the model is deployed as a microservice and connected to data sources. Machine learning engineers are also responsible for monitoring the model's performance in real-world conditions and making necessary adjustments or retraining the model as needed. The video outlines the typical background and skill set required for a machine learning engineer, including knowledge in statistics, data analysis, applied mathematics, machine learning algorithms, programming languages like Python and R, and familiarity with frameworks and libraries such as scikit-learn and TensorFlow. The distinction between machine learning engineers, data scientists, and data engineers is also discussed, highlighting the unique contributions each role makes to a project involving machine learning.
Takeaways
- 🔍 The role of a machine learning engineer is to apply machine learning to bring value to a business or product, focusing on creating features or automating workflows.
- 🚀 Machine learning engineers are tasked with building decision support systems or fully automated decision automation systems, with a key focus on shipping a working piece of software that utilizes machine learning.
- 🧩 In practice, an ML engineer would start by choosing and preparing data, cleaning errors, filling in missing entries, and transforming records into a single format.
- 📈 They select algorithms based on the type of data, predictive accuracy required, and resource intensity, experimenting with several models to find the best fit for the task.
- 📝 Model training involves learning to make predictions by finding patterns in the training data set, and a testing set is used to evaluate the model's accuracy.
- 🚀 Once a model is trained and tested, the ML engineer is responsible for productionizing the model, which includes deploying it as a microservice and connecting it to necessary data sources.
- 🔧 ML engineers must monitor the model's performance in real-world conditions, setting up infrastructure to compare real-world data to the model's predictions, and decide if retraining is necessary.
- 🌐 Changes in world conditions may require new data, and ML engineers often automate the retraining process to adapt to these changes, making it a potentially daily task.
- 📚 A typical ML engineer background includes statistics, data analysis, applied mathematics, and knowledge of machine learning algorithms and architectures.
- 🛠️ They should be proficient in programming languages like Python and R, and be familiar with frameworks and libraries such as scikit-learn and TensorFlow.
- 🤖 High-performance languages and tools like Java, C++, Hadoop, Apache Spark, and NVIDIA CUDA are also part of the skill set for production engineering aspects.
- 👥 The distinction between data scientists, ML engineers, and data engineers lies in their focus areas: data scientists on analysis, ML engineers on building and maintaining ML models, and data engineers on data infrastructure and pipelines.
Q & A
What is the primary role of a machine learning engineer?
-The primary role of a machine learning engineer is to use machine learning to bring additional value to a business or product, often by building features for products or automating workflows, and focusing on shipping a working piece of software that uses machine learning.
How does a machine learning engineer approach predicting pickup times for a ride-hailing app?
-A machine learning engineer would build a model that learns the possible relations between data such as distance, speed, weather, and traffic congestion to predict pickup times more accurately than a simple rule-based system could.
What are the initial steps an ML engineer takes when preparing data for a model?
-The initial steps include choosing the right data, analyzing historical records, cleaning errors from the data, filling in missing entries, and transforming records into a single format.
Why might a machine learning model require retraining?
-A machine learning model might require retraining if world conditions change, causing the model to become less accurate because it was trained on outdated data.
What is the difference between a machine learning engineer and a data scientist?
-While both work with data and machine learning, a machine learning engineer is more focused on building and productionalizing machine learning models, whereas a data scientist might not necessarily work directly with productionalized ML models and could focus more on analytical tasks.
What is the role of a data engineer in the context of machine learning?
-A data engineer focuses on transferring data from one system to another, managing databases, and working with data transformation tools. They often cooperate with ML engineers in running data infrastructures that support machine learning.
What are some popular machine learning algorithms mentioned in the script?
-Some popular machine learning algorithms mentioned are decision trees, support vector machines, naive Bayes, and deep learning networks.
Why is monitoring the performance of a machine learning model important?
-Monitoring the performance of a machine learning model is important to understand its accuracy and how it changes over time, which provides ML engineers with the necessary data to make decisions about whether the model performs well and if it needs retraining.
What programming languages and tools are commonly used by machine learning engineers?
-Machine learning engineers commonly use Python, which is the main programming language in data science. They may also use R for data exploration and visualization, and libraries such as scikit-learn for machine learning algorithms and TensorFlow for deep learning.
How does a machine learning engineer productionalize a model?
-A machine learning engineer productionalizes a model by deploying it as a microservice, wrapping up the model into a container, and deploying it on a server. They then connect the model to data sources and ensure it can consume the required data, calculate predictions, and send them back to the end user.
What are some factors that a machine learning model might need to consider when predicting taxi arrival times?
-Factors that a machine learning model might need to consider include distance from the customer to the driver, speed, weather conditions, and traffic congestion.
What is the significance of choosing the right algorithm for a machine learning task?
-The choice of algorithm is significant because it depends on the type of data, expected predictive accuracy, and the resource intensity of the model. The wrong choice could lead to inefficiency in processing power or inadequate predictive performance.
Outlines
🤖 Introduction to the Role of Machine Learning Engineers
This paragraph introduces the role of a machine learning engineer, emphasizing their importance in bridging the gap between data science and practical applications. The summary explains that machine learning engineers use ML to add value to businesses or products, often by building features or automating workflows. It illustrates the process of creating an algorithm to predict taxi arrival times, highlighting the complexities involved and how ML can handle numerous variables that a rule-based system might not. The responsibilities of choosing and preparing data, selecting algorithms, training models, and deploying them in a production environment are also covered.
📈 ML Engineer's Responsibilities: Model Training and Deployment
This section delves into the specific duties of a machine learning engineer, focusing on model performance monitoring and evaluation. It discusses the importance of comparing real-world data to a model's predictions to assess accuracy and the need for retraining models as world conditions change. The summary outlines the typical background and skill set required for an ML engineer, including knowledge of statistics, data analysis, machine learning algorithms, programming languages, and frameworks. It also touches on the distinction between data scientists and machine learning engineers, noting that the latter are more focused on the production aspect of machine learning models.
🔄 Overlap and Distinction Between ML Engineers, Data Scientists, and Data Engineers
The final paragraph explores the overlap and distinctions between the roles of machine learning engineers, data engineers, and data scientists. It emphasizes the collaborative nature of these roles, particularly when it comes to running data infrastructures that support machine learning. The summary explains that while data engineers focus on data transfer and management, ML engineers are responsible for setting up data infrastructure and databases to support machine learning models. It also clarifies that data scientists may focus on deep data research and analysis without necessarily implementing machine learning, making them suitable for roles that require extensive data exploration and statistical analysis.
Mindmap
Keywords
💡Data Scientist
💡Machine Learning Engineer
💡Feature
💡Algorithm
💡Model Training
💡Microservice
💡Data Attributes
💡Model Performance Monitoring
💡Data Preparation
💡Retraining
💡Data Engineering
Highlights
The job of a data scientist is to make sense of information and forecast the future.
Machine learning engineers bridge the gap between data science and practical application.
Alexander Kondoforov, a Data Science Competence Lead at LTCH Soft, explains the role of ML engineers.
ML engineers focus on building features for products or automating workflows using machine learning.
Machine learning models can predict taxi arrival times more accurately by considering various variables.
Rule-based systems struggle with the complexity of predicting arrival times due to numerous variables.
ML engineers are responsible for choosing, preparing, and cleaning data for model training.
Different algorithms are chosen based on data type, predictive accuracy, and resource intensity.
Experimentation with models and data subsets is crucial for finding the best fit for the task.
Training a model involves learning to make predictions by finding patterns in the training data set.
Testing a model with historical data is essential to evaluate its accuracy.
ML models are deployed as microservices, which are isolated containers with all necessary dependencies.
Connecting the model to data sources is vital for real-time data consumption and prediction.
Monitoring model performance in real-world conditions is a key concern for ML engineers.
ML engineers set up monitoring infrastructure to compare real-world data to model predictions.
Retraining models with fresh data is often a daily task due to changing world conditions.
The typical background for an ML engineer includes statistics, data analysis, and applied mathematics.
Knowledge of machine learning algorithms, architectures, and programming languages like Python is essential.
ML engineers need to be familiar with frameworks like scikit-learn and TensorFlow for model training.
High-performance languages and distributed computing frameworks are also part of the skill set.
The role of a data scientist may not involve direct work with productionalized ML models, focusing more on analysis.
Data engineers focus on data infrastructure, pipelines, and are closer to software engineering.
ML engineers, data scientists, and data engineers often cooperate, though their roles can overlap.
Transcripts
[Music]
the job of a data scientist is to make
sense of information what does data tell
are there any patterns
and more importantly can it help us
forecast the future but what if we need
to predict the future every day every
hour or minutes and do that for
thousands of people simultaneously say
predict the taxi arrival time there's
one specific role that builds a bridge
between data science and its practical
counterpart machine learning
meet the machine learning engineer
well the role of machine learning
engineer is to use machine learning to
[Music]
somehow
bring additional value to the business
or the product
this is alexander kondoforov a data
science competence lead at ltch soft in
most cases
it means that we are building
some features for products or automate
some workflows so
for example like building
decision
support systems or fully automated
decision automation systems the key word
here is product a machine learning
engineer always focuses on shipping a
working piece of software
and this product uses machine learning
sounds simple but how does that
translate into practice
let's imagine we have a product team
that builds a ride-hailing app what we
want is an algorithm that will
accurately predict pickup time for the
customer we can calculate pickup time
based on distance and average time
without machine learning using a simple
rule-based system but there are plenty
of variables that may skew the results
rainfalls or blizzards traffic
congestion and road incidents all affect
the arrival time with a rule-based
system a software engineer would have to
consider all possible factors and write
code for them there are so many of those
and there's no way to write rules for
everything then how can an ml engineer
help
they can build a model that learns all
the possible relations between data by
itself and then gives us a more accurate
prediction if we support it with the
necessary data that said let's talk
about ml engineers responsibilities
so an ml engineer will start with
choosing and preparing data let's assume
that there are several variables we need
to calculate pickup time the distance
from the customer to the driver speed
weather and traffic congestion to name a
few all of these can become features
data attributes a model uses to give us
prediction results to get this data an
ml engineer will have to analyze
historical records on previous pickups
that contain those variables choosing
the right data and consolidating it is
the first step in preparation then the
ml engineer would clean the errors from
the data fill in the missing entries and
transform records into a single format
once the data is ready an ml engineer
needs to choose an algorithm that would
fit the task
the choice depends on the type of data
expected predictive accuracy and how
resource intensive the model is
you may need deep neural networks to
process images and videos with 98
accuracy but training them would require
renting clusters of gpus and running
those models in production may require
specific ai optimized processing units
but sometimes good old decision trees
would be enough
the ml engineer would experiment with
several models and a subset of data to
find the one that fits the task to start
with model training
during the training process a model will
learn to make predictions by finding
patterns in the training data set you
also need a testing set of historical
data to evaluate whether the model gives
accurate forecasts if it passes the test
congratulations we have a model that can
make predictions but the model isn't a
part of our product and our customers
can't use it yet so now an ml engineer
comes to productionalizing the model and
its deployment
here is our taxi application or in this
case two client applications used by
drivers and customers and our server
where all the back end logic sits now we
need to deploy the model machine
learning models are usually deployed as
a microservice an isolated container
where the code has all the dependencies
and can perform as a standalone unit so
an ml engineer wraps up the model into a
container and deploys it on the server
then he or she needs to connect the
model to data sources
the applications will handle some part
of the data like driver and customer
geolocation current speed of the car and
so on we'll also need extra data like
traffic incidents jams or whether that
comes from a separate database from this
point the model can consume the required
data calculate a prediction and send it
back to the customer but here is another
problem
remember we tested the model on
historical data but how well does it
work in real life conditions
you need to track its performance and
this is one of the main concerns of a
machine learning engineer
model performance monitoring and
evaluation
let's say the model predicted a taxi
would arrive in 14 minutes while it
actually took 20 minutes
to capture this an ml engineer would set
up monitoring infrastructure to compare
real world data to the model's
predictions to understand its accuracy
and how it changes over time
monitoring systems provide ml engineers
with necessary data to make a decision
whether the model performs well and if
it needs retraining so what is that
as world conditions are changing the
model can require new data
say a large part of a major city highway
was closed for reconstruction which made
drivers reach their destinations later
the model started predicting a pickup
time less accurately because it was
trained on outdated data and if the ml
engineer has monitoring systems set
right they will show this drift such
changes are a prerequisite for training
a new model with fresh data since the
world conditions may change daily
retraining often becomes a daily task
for a machine learning engineer so it
makes sense to automate this process
as you can see the ml engineer is
generally responsible for
well the whole machine learning part of
the product starting from data analysis
to the moment the model is trained and
launched in production
so what would the typical background and
skill set of an ml engineer look like
first it's statistics data analysis and
applied mathematics as ml engineers
curate features and prepare data the
fundamentals are critical
as you probably guessed these
specialists must also know existing
machine learning algorithms and common
architectures decision trees support
vector machines naive bayes deep
learning networks are a few popular
algorithms used in ml applications
to train those models engineers have to
be familiar with common tools python is
the main programming language used in
data science ml engineers may also be
proficient in r to explore and visualize
data similar to software engineering ml
has a number of frameworks and libraries
that specialists use to streamline their
work one of the main ones is
scikit-learn which is a python based
library featuring a variety of machine
learning algorithms as deep learning
becomes a universal answer to any ml
problem it has its own library
tensorflow
but what about skills required for
production engineering normally ml
engineers are required to know
high-performance languages like java and
c-plus plus to run models on the server
if they work with big data architectures
ml engineers must be familiar with
distributed computing frameworks like
hadoop and data processing tools like
apache spark and if the product actively
uses deep learning the engineer may need
to know how to configure parallel gpu
computing platforms such as nvidia cuda
so where do machine learning engineers
come from obviously you'd expect them to
have a computer science education some
engineers transition from software
development while others start with data
science and analytics and then acquire
engineering skills
but this set of skills sounds like a
data scientist right then what's the
difference between them and when
specifically should you hire an ml
engineer
data scientists
and machine learning engineers are in
quite common and in fact in many
companies uh these titles are usage
like equally uh
and it it's actually up to the company
uh whether to
name their specialists to be
data scientists or machine learning
engineers for example
data scientists might not actually use
machine learning
to do their everyday job so for example
they can be doing some analytics data
analytics or eb testing or
apply algorithms and statistics to data
in other words data scientists don't
necessarily work directly with
productionalized machine learning models
sometimes they only focus on analytical
tasks for instance our ride hailing
company may employ data scientists
besides hiring ml engineers to explore
new markets and to find the viability of
expanding there
at the same time
machine learning engineers tend to be
more engineer savvy in most cases
probably they build some kind of
machine learning-based features for
products
like in i don't know google or netflix
like recommendations or search also
machine learning engineers might be
it might be easier for them to actually
productionalize their models the results
of their work integrated with
other
parts of the system so the production
part is what can draw the line between
data scientists and machine learning
engineers the latter definitely train
launch and maintain ml models data
scientists may not do that what about
data engineers the responsibilities of a
machine learning engineer will also
overlap with that of a data engineer
a specific tech professional that
focuses on transferring data from one
system to another managing databases and
working with data transformation tools
so data engineers are more
closer to software engineers so they
obviously work with data they build data
pipelines
some streaming
processing caching whatever it's not
required from them to actually know
machinery ml engineers and data
engineers often cooperate in running
data infrastructures that support
machine learning back to our example
an ml engineer is likely to define
specifications for a database to keep
information on traffic incidents jams or
weather in turn data engineers can use
these specifications to upload data to a
database and connect it with the model
and there you have it
if you're aiming at running machine
learning models in production you're
looking for an ml engineer to set up
data infrastructure and databases you'd
look for a data engineer
and if you need deep data research and
analysis without necessarily running
machine learning you should consider
data scientists
of course it's hard to draw clear lines
to separate these three roles but this
distinction should make things a bit
easier for you
to learn more watch our videos on data
science teams and data engineers thank
you for watching and stay tuned
you
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