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What is the Top Job in AI? Data Scientist or Machine Learning Engineer?
The world of artificial intelligence (AI) is growing rapidly, and with it the demand for skilled data scientists and machine learning engineers. So which job is more in-demand? Which career path offers the biggest salary potential? And what skills do you need to pursue either one of these top jobs in AI? Here’s a closer look at what you need to know about becoming a data scientist or machine learning engineer.
Let’s start by defining what a Data Scientist and a Machine Learning Engineer do.
According to Wikipedia, A data scientist is someone who creates programming code and combines it with statistical knowledge to create insights from data.
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that ‘learn’, that is, methods that leverage data to improve performance on some set of tasks.
Do you want to work in AI? If so, which is the top job: data scientist or machine learning engineer? Many people might say that data scientist is the top job because they are responsible for analyzing data and extracting insights. However, machine learning engineers are responsible for designing and implementing algorithms that allow machines to learn from data, so they may be more responsible for the final outcome of an AI project. Which is the top job in AI? That depends on your priorities.
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For me, The top job in all of AI is the machine learning engineer and NOT the data scientist.
It takes very unique skills and interests to be a Data Scientist which not everybody has. Obviously you need to enjoy Math and Statistics, because these are the foundations of any good data analysis. You need to have those technical skills, but also excellent social skills because as a Data Scientist you will have to communicate your results to stakeholders.
As a Data Scientist, you will often find yourself doing research and investigating why X happened, or how to achieve Y. That is why you should be a person that prefers to do investigative work over implementing a solution to certain problems.
Data Science can be boring
The fun part of Data Science (for me) is building Machine Learning models to predict something. Those algorithms are extremely fascinating and take a very different approach to solving problems than traditional programming.
But building those models is only 10% of the work a Data Scientist is doing. The main part is wrangling and normalizing the data that has to be fed into those models. Wrangling, normalizing, transforming and aggregating data means that it is likely that you write a lot of SQL queries or something similar and execute query after query. Since most of the time the amount of data is pretty big, the queries will take a long time to run.
Many young Data Scientists cannot wait to get into their first job creating super efficient Machine Learning Models, maybe even doing Deep Learning. But then realizing that the work a Data Scientist is doing can vary a lot. Some Data Scientist may actually just do Deep Learning and heavy research, but many many others will just do SQL, Excel and very basic statistical models like linear regression. Most Data Scientists do not build their own Machine Learning Models from Scratch, but rather use some pre-built models like scikit-learn.
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Even though the pay is often good, the entry barriers are enormous, and the job market currently is oversaturated because a lot of people want to get into Data Science.
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If you see yourself enjoying investigating causes/making predictions over implementing solutions, and you have or are looking to have a higher level education — then go for it. Data Science is definitely not for everyone, but might just be the right thing for you.
Data Science, as often known and mentioned, is a broader term for multiple processes and Machine Learning is one of the major parts of it. Machine Learning demands strong programming skills and understanding of algorithms, whereas, Data Science on the other hand requires strong analytical, statistical skills, combined by domain science and decision making.
The major difference between Data Science and Machine Learning lies in the set of tasks performed as a part of each process. Data Science contains a long list of tasks and tasks like predictions from the past data is a subset of this list of tasks and machine learning on the other hand absolutely deals with predictions only. One way to see the difference is that the end output of the Machine Learning algorithm is by and for a computer, whereas the output from a Data Science stack is meant to be understood by humans. Keeping in mind the differences between the underlying methodologies in the two fields of study, let’s try to understand the difference between the roles and responsibilities of someone who is designated as a Data Scientist vs a Machine Learning Engineer.
A Data Scientist cleans data, does data mining, feature engineering, and the like, building models. Their models may or may not use ML and when it does use ML it is generic ML from a library like XGBoost. Data Scientists do not specialize in advanced ML.
A Machine Learning Engineer takes a model then chooses a more advanced ML for the job. They often end up creating their own ML, their own deep neural networks typically, to get more accuracy out of the model created by the data scientist. When the model is ready for prime time the MLEng will deploy the model into the cloud and work with often the frontend web dev team (or whoever) to help them interface with the model.
One of the benefits of an MLE is they are never on call. (ymmv if you’re at a shitty company, or a small company that is under resourced.) Instead DevOps, MLOps, or in rare situations Data Engineers / Infrastructure Software Engineers, will be on call and responsible for the server that is hosting the model. If something goes down, or something is on fire, they’re on call to fix it. If the model the MLE deployed is broken and causing errors, instead of contacting the MLE in the middle of the night they’ll just roll back to an older version.
DevOps monitors servers for issues and takes care of server issues.
There’s a lot of buzz around AI and machine learning right now, and for good reason – the potential applications are endless. But with all the uncertainty around what these technologies will eventually look like, it can be tough to decide which career path to pursue in this field.
There are many differences between a Data Scientist and Machine Learning Engineer, but the main ones are:
- Data scientists define the metrics. MLEs try to move them.
- Data scientists understand the problem. MLEs find the solution.
Sources: Quora Reddit analyticxlabs
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Machine Learning Engineer Interview Questions and Answers
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List of Freely available programming books - What is the single most influential book every Programmers should read
- Bjarne Stroustrup - The C++ Programming Language
- Brian W. Kernighan, Rob Pike - The Practice of Programming
- Donald Knuth - The Art of Computer Programming
- Ellen Ullman - Close to the Machine
- Ellis Horowitz - Fundamentals of Computer Algorithms
- Eric Raymond - The Art of Unix Programming
- Gerald M. Weinberg - The Psychology of Computer Programming
- James Gosling - The Java Programming Language
- Joel Spolsky - The Best Software Writing I
- Keith Curtis - After the Software Wars
- Richard M. Stallman - Free Software, Free Society
- Richard P. Gabriel - Patterns of Software
- Richard P. Gabriel - Innovation Happens Elsewhere
- Code Complete (2nd edition) by Steve McConnell
- The Pragmatic Programmer
- Structure and Interpretation of Computer Programs
- The C Programming Language by Kernighan and Ritchie
- Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein
- Design Patterns by the Gang of Four
- Refactoring: Improving the Design of Existing Code
- The Mythical Man Month
- The Art of Computer Programming by Donald Knuth
- Compilers: Principles, Techniques and Tools by Alfred V. Aho, Ravi Sethi and Jeffrey D. Ullman
- Gödel, Escher, Bach by Douglas Hofstadter
- Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
- Effective C++
- More Effective C++
- CODE by Charles Petzold
- Programming Pearls by Jon Bentley
- Working Effectively with Legacy Code by Michael C. Feathers
- Peopleware by Demarco and Lister
- Coders at Work by Peter Seibel
- Surely You're Joking, Mr. Feynman!
- Effective Java 2nd edition
- Patterns of Enterprise Application Architecture by Martin Fowler
- The Little Schemer
- The Seasoned Schemer
- Why's (Poignant) Guide to Ruby
- The Inmates Are Running The Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity
- The Art of Unix Programming
- Test-Driven Development: By Example by Kent Beck
- Practices of an Agile Developer
- Don't Make Me Think
- Agile Software Development, Principles, Patterns, and Practices by Robert C. Martin
- Domain Driven Designs by Eric Evans
- The Design of Everyday Things by Donald Norman
- Modern C++ Design by Andrei Alexandrescu
- Best Software Writing I by Joel Spolsky
- The Practice of Programming by Kernighan and Pike
- Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt
- Software Estimation: Demystifying the Black Art by Steve McConnel
- The Passionate Programmer (My Job Went To India) by Chad Fowler
- Hackers: Heroes of the Computer Revolution
- Algorithms + Data Structures = Programs
- Writing Solid Code
- Getting Real by 37 Signals
- Foundations of Programming by Karl Seguin
- Computer Graphics: Principles and Practice in C (2nd Edition)
- Thinking in Java by Bruce Eckel
- The Elements of Computing Systems
- Refactoring to Patterns by Joshua Kerievsky
- Modern Operating Systems by Andrew S. Tanenbaum
- The Annotated Turing
- Things That Make Us Smart by Donald Norman
- The Timeless Way of Building by Christopher Alexander
- The Deadline: A Novel About Project Management by Tom DeMarco
- The C++ Programming Language (3rd edition) by Stroustrup
- Patterns of Enterprise Application Architecture
- Computer Systems - A Programmer's Perspective
- Agile Principles, Patterns, and Practices in C# by Robert C. Martin
- Growing Object-Oriented Software, Guided by Tests
- Framework Design Guidelines by Brad Abrams
- Object Thinking by Dr. David West
- Advanced Programming in the UNIX Environment by W. Richard Stevens
- Hackers and Painters: Big Ideas from the Computer Age
- The Soul of a New Machine by Tracy Kidder
- CLR via C# by Jeffrey Richter
- The Timeless Way of Building by Christopher Alexander
- Design Patterns in C# by Steve Metsker
- Alice in Wonderland by Lewis Carol
- Zen and the Art of Motorcycle Maintenance by Robert M. Pirsig
- About Face - The Essentials of Interaction Design
- Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky
- The Tao of Programming
- Computational Beauty of Nature
- Writing Solid Code by Steve Maguire
- Philip and Alex's Guide to Web Publishing
- Object-Oriented Analysis and Design with Applications by Grady Booch
- Effective Java by Joshua Bloch
- Computability by N. J. Cutland
- Masterminds of Programming
- The Tao Te Ching
- The Productive Programmer
- The Art of Deception by Kevin Mitnick
- The Career Programmer: Guerilla Tactics for an Imperfect World by Christopher Duncan
- Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp
- Masters of Doom
- Pragmatic Unit Testing in C# with NUnit by Andy Hunt and Dave Thomas with Matt Hargett
- How To Solve It by George Polya
- The Alchemist by Paulo Coelho
- Smalltalk-80: The Language and its Implementation
- Writing Secure Code (2nd Edition) by Michael Howard
- Introduction to Functional Programming by Philip Wadler and Richard Bird
- No Bugs! by David Thielen
- Rework by Jason Freid and DHH
- JUnit in Action
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