What is a Machine Learning Engineer

Summary

The 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

00:00

🤖 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.

05:02

📈 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.

10:04

🔄 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.

Keywords

💡Data Scientist

A data scientist is a professional who analyzes and interprets complex digital data, such as information from business transactions, scientific research, or social media. In the video, data scientists are depicted as individuals who make sense of information to find patterns and forecast the future, but they may not necessarily work directly with machine learning models in a production environment.

💡Machine Learning Engineer

A machine learning engineer is a specialist who applies machine learning techniques to build systems that can learn from data and make predictions or decisions. The video emphasizes the role of a machine learning engineer in creating value for a business by automating workflows and building decision support systems, focusing on shipping a working piece of software that utilizes machine learning.

💡Feature

In the context of machine learning, a feature is an individual measurable property or characteristic that is used as input for the model. The video mentions that machine learning engineers select variables such as distance, speed, weather, and traffic congestion to serve as features for predicting pickup times in a ride-hailing app.

💡Algorithm

An algorithm is a set of rules or procedures for solving problems, especially in mathematics and computer science. The video discusses how machine learning engineers choose an algorithm that fits the task at hand, such as decision trees or deep neural networks, based on the type of data and the expected predictive accuracy.

💡Model Training

Model training is the process by which a machine learning model is taught to make predictions or decisions based on patterns in data. The video explains that during training, a model learns by analyzing a dataset and that a testing set is used to evaluate the model's accuracy before it is deployed.

💡Microservice

A microservice is a modular software design approach where a large application is built as a suite of small services that run independently and are interconnected through APIs. The video describes how machine learning models are typically deployed as microservices, allowing them to function as standalone units with all necessary dependencies.

💡Data Attributes

Data attributes, also referred to as features in the context of machine learning, are the variables that a model uses to generate prediction results. The video script uses the example of a ride-hailing app where attributes like distance, speed, and weather are considered to predict the arrival time of a taxi.

💡Model Performance Monitoring

Model performance monitoring involves tracking and evaluating how well a deployed machine learning model performs in real-life conditions. The video highlights the importance of this process for machine learning engineers, as it helps them understand the accuracy of the model's predictions and identify when the model may need retraining.

💡Data Preparation

Data preparation is the process of selecting, cleaning, and transforming data into a format suitable for analysis or modeling. In the video, it is mentioned as the first step in the machine learning workflow where historical records are analyzed, errors are cleaned, and data is consolidated into a single format for the model to use.

💡Retraining

Retraining is the process of training a machine learning model again, typically with new or updated data. The video script discusses how changes in world conditions may require models to be retrained to maintain accuracy, which can become a daily task for a machine learning engineer, especially when the process is automated.

💡Data Engineering

Data engineering involves building and maintaining the infrastructure that supports the storage, processing, and retrieval of data. The video script points out that data engineers work closely with machine learning engineers to create data pipelines and manage databases, which are crucial for the functioning of machine learning models.

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