What is a Machine Learning Engineer
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- čas přidán 5. 07. 2024
- There’s one specific role that builds a bridge between data science and its practical counterpart, machine learning. Meet the machine learning engineer.
In this video we explain:
1) 02:14 ML engineer’s responsibilities, including:
• Data preparation
• Selecting an algorithm
• Model training
• Model deployment and integration with data sources
• Model performance monitoring and evaluation
• Retraining
2) 06:25 background and skillset of the ML engineer
3) 08:16 and when you should hire an ML engineer
Read more in our blog:
1) What is Machine Learning Engineer: Responsibilities, Skills, and Value Brought
www.altexsoft.com/blog/machin...
2) Machine Learning Project Structure: Stages, Roles, and Tools
www.altexsoft.com/blog/datasc...
3) Whitepaper: Machine Learning: Bridging Between Business and Data Science
www.altexsoft.com/whitepapers...
Learn more about AltexSoft: www.altexsoft.com
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Music by Epidemicsound.com
#machinelearning #machinelearningengineer #mlengineer #ml - Věda a technologie
Thank you for this video. Recruiters need to watch this to really understand the role of a Data Scientist and that there are 101 ways to skin a cat. A Data scienctist job is to figure it out with the data. Hence, the word scientist. Scientist explore. They ask questions, then trying to find the answer and further explore to see what they can find. While a ML engineer's job is to design and make sure the result is functioning well. They also need to understand that what they put on a job ad or description is not the end all and be all in terms of tools as Data scientist. You can be in a roll where you are using a few algo and you can be in a roll where you are using a lot of different algos. Us data scientist and ML engineers still Google things. We use a lot of other people's codes and we use templates. Many data scientists are grossly underpaid. I see Data scientist jobs asking for 5 years of experience and only want to pay $120,000. They are even asking the DS and MLE to use other experiences and skills outside Data Science. KNOW YOUR WORTH, PEOPLE!!!
What a concise but clear and to the point explanation! It explains many details in a very simple and straightforward way.
I am truely satisfied by this video and the amount of information it has provided and questions it has answered.😁
This video is amazing! I really understood what is Machine Learning. Keep it going!!🖤
This is really good, makes things much simpler.
Thank you for the Video, Best explanation on internet.
This voice is fantastic! I could listen to it for hourssss
Thank you for explaining the differences between all three roles: makes much more sense now!
Happy to help ☺️
Very nice video, well produced.
Superb & clear Explanations
Really well made video. Thank you!
Thank you!
Nice and concise explanation
Very helpful, thank you!
Amazing video! The quality is superb, thank you very much!! Hope you have an amazin week.
Simple visualized and summarized
ultimate explanation , all video delivers deep insight about the subject ,please keep it up.❤👍
Thanks for the feedback ♥
Excellent 👌👌👌👌👌👌
amazing video, thank you so much!!! I want to start a business that has to do with AI. ML engineering makes so much more sense now. Thanks x100!!!
Best of luck
Thank you for your informative videos !!
omg
this video content is sooo good organized
but! you must find someone who can optimize videos here in youtube
you will open a door to a viewers
Very subtle detail showing a female as an MLE. nice work.
Thank you so much for this...
Hope it helped u a lot
Wow, what an incredible video! The production quality is top-notch, and I love how seamlessly the visuals and music blend together. The content is so informative and thought-provoking-I've learned a lot from watching this. It's evident that a lot of time and effort went into creating this video, and it truly paid off. Kudos to the talented team behind it! Keep up the fantastic work, and I can't wait to see what you come up with next. You have a new fan here!
Glad our content is useful for you. Thanks for such cool feedback ♥
@@AltexSoft ❤️❤️❤️
What would your advice be for a data analyst intern from non-CS background trying to break into data engineer/machine learning engineer career? I’m interested in ML engineer but it seems like DE work is closer to data analytics, and easier to break into compared to MLE.
Study a course in data science and ML, from institution like Scalar, Analytix labs,Excelr❤
i must have checked my notifications 100 times during this video😂😂
😂😂😂
This is really helpful thank you
Glad our video helped you)
Hi, thank you so much for this great video. Although the content of the video is amazing. I am really intrigued by the animation you have used. Can you please tell me the software used for this animation video?
Hi. For animation and video editing, we use Adobe packages: Premiere, Photoshop, Illustrator, and After Effects. Hope this'll help u
very informative video! can someone tell me how much time does it take an ML engineer from preparing the data to deploy the model ? cuz it looks like a long process
It depends on so many factors. Data preparation may take months, depending on your dataset, data quality, etc.
Video started out good, then you kinda started saying ML engineers are "unicorns" and mixing in too many skills. High-performance languages don't have very good ML frameworks, so they aren't usually used. And most frameworks in Python use Numpy underneath, which is written in C...
Also, you focused more on "big data platforms" rather than general MLOps, being able to write API, containerization, Linux , CI/CD, etc, which I think is much more important to an ML engineer than what you said.
Wow awesome video
Grateful for your positive feedback, thank you!
Please also make a video on AI Engineer
AI Engineer and ML Engineer are synonymous.
Is ML engineering is good career to choose in 2022? What is its future?
It's not likely that the profession will disappear anytime soon. Multiple research agencies predict the ML market growth of around 30% CAGR or more between 2022 and 2030. ML and DS-related jobs still offer relatively high salaries compared to other software engineering positions
First of all, all data is important and machine learning is not a person figuring out which data to use, it is the Cloud where all data is analyzed, humans cannot see the patterns the machine learning in the cloud can and so any time you have a human doing this you have errors creep into the data. ML uses the cloud for the analytics not a persons eyes looking at a computer screen, this is what Industry 4.0 is and why so many companies fail as they don't use ML properly.
So what does that means? So ML engineer isn’t that important and should just hire cloud engineer instead?
This is ridiculous
Thank you!