#ML engineer

[aionlinemoney]

The Role of Machine Learning Engineer: Combining Technology and Artificial Intelligence

Artificial intelligence has transformed our daily lives in a greater way than we can’t imagine over the past year, Impacting how we work, communicate, and solve problems. Today, Artificial intelligence furiously drives the world in all sectors from daily life to the healthcare industry. In this blog we will learn how machine learning engineer build systems that learn from data and get better over time, playing a huge part in the development of artificial intelligence (AI). Artificial intelligence is an important field, making it more innovative in every industry. In the blog, we will look career in Machine learning in the field of engineering.

What is Machine Learning Engineering?

Machine Learning engineer is a specialist who designs and builds AI models to make complex challenges easy. The role in this field merges data science and software engineering making both fields important in this field. The main role of a Machine learning engineer is to build and design software that can automate AI models. The demand for this field has grown in recent years. As Artificial intelligence is a driving force in our daily needs, it become important to run the AI in a clear and automated way.

A machine learning engineer creates systems that help computers to learn and make decisions, similar to human tasks like recognizing voices, identifying images, or predicting results. Not similar to regular programming, which follows strict rules, machine learning focuses on teaching computers to find patterns in data and improve their predictions over time.

Responsibility of a Machine Learning Engineer:

Collecting and Preparing Data

Machine learning needs a lot of data to work well. These engineers spend a lot of time finding and organizing data. That means looking for useful data sources and fixing any missing information. Good data preparation is essential because it sets the foundation for building successful models.

Building and Training Models

The main task of Machine learning engineer is creating models that learn from data. Using tools like TensorFlow, PyTorch, and many more, they build proper algorithms for specific tasks. Training a model is challenging and requires careful adjustments and monitoring to ensure it’s accurate and useful.

Checking Model Performance

When a model is trained, then it is important to check how well it works. Machine learning engineers use scores like accuracy to see model performance. They usually test the model with separate data to see how it performs in real-world situations and make improvements as needed.

Arranging and Maintaining the Model

After testing, ML engineers put the model into action so it can work with real-time data. They monitor the model to make sure it stays accurate over time, as data can change and affect results. Regular updates help keep the model effective.

Working with Other Teams

ML engineers often work closely with data scientists, software engineers, and experts in the field. This teamwork ensures that the machine learning solution fits the business goals and integrates smoothly with other systems.

Important skill that should have to become Machine Learning Engineer:

Programming Languages

Python and R are popular options in machine learning, also other languages like Java or C++ can also help, especially for projects needing high performance.

Data Handling and Processing

Working with large datasets is necessary in Machine Learning. ML engineers should know how to use SQL and other database tools and be skilled in preparing and cleaning data before using it in models.

Machine Learning Structure

ML engineers need to know structure like TensorFlow, Keras, PyTorch, and sci-kit-learn. Each of these tools has unique strengths for building and training models, so choosing the right one depends on the project.

Mathematics and Statistics

A strong background in math, including calculus, linear algebra, probability, and statistics, helps ML engineers understand how algorithms work and make accurate predictions.

Why to become a Machine Learning engineer?

A career as a machine learning engineer is both challenging and creative, allowing you to work with the latest technology. This field is always changing, with new tools and ideas coming up every year. If you like to enjoy solving complex problems and want to make a real impact, ML engineering offers an exciting path.

Conclusion 

Machine learning engineer plays an important role in AI and data science, turning data into useful insights and creating systems that learn on their own. This career is great for people who love technology, enjoy learning, and want to make a difference in their lives. With many opportunities and uses, Artificial intelligence is a growing field that promises exciting innovations that will shape our future. Artificial Intelligence is changing the world and we should also keep updated our knowledge in this field, Read AI related latest blogs here.

[mlops-courses]

MLOps Courses: Prepare Now For The Future

Comprehensive Methodology: Courses at Aitech.Studio offer a comprehensive methodology merging MLOps components with AI optimizations for deploying high-performance and scalable solutions2.

Practical Examples: Enrollees learn through practical examples, covering aspects from model registries to data versioning, and utilizing AI tools like model compression and Intel®-optimized frameworks2.

Strategic Software and Hardware Optimizations: The courses emphasize how strategic software and hardware optimizations enhance the machine learning lifecycle, maximizing ROI and ensuring top-notch AI system quality2.

Intel® Developer Cloud Exploration: Learners can explore an intermediate-level exploration of MLOps using the latest Intel® hardware and software in the Intel® Developer Cloud, enhancing their understanding of hardware optimizations2.

Industry-Recognized Certificates: Completing courses at Aitech.Studio allows learners to earn industry-recognized certificates, validating their proficiency in deploying machine learning models seamlessly and empowering their career growth2.

Empowering Proficiency: The meticulously designed courses at Aitech.Studio empower learners through comprehensive modules, hands-on projects, and expert guidance, ensuring a deep understanding of MLOps principles and practices2.

Focus on Scalability and Precision: The courses focus on crafting scalable, seamless ML systems, ensuring precision and resilience in predictive modeling, essential for success in the dynamic field of MLOps.

https://aitech.studio/aih/aic/mlops-courses/

[codersdev]

Artificial Intelligence Engineering: Building Intelligent Systems | Coders

Looking to build intelligent systems using Artificial Intelligence? Our AI engineering course provides you with the essential concepts and techniques to build intelligent systems that solve complex problems. From understanding the fundamentals of machine learning and neural networks to natural language processing and computer vision, our course covers everything you need to know. Join Coders today and learn how to build intelligent systems that can revolutionize your industry. Read More-: artificial intelligence engineering

[nixcraft]

I asked Google "who ruined Google" and they replied honestly using their AI, which is now forced on all of us. It's too funny not to share!

[absolutebl]

Hi hello,

Recently I've been wanting to indulge in some of my guilty pleasures, and therefore, I come to you, my liege.

Do you have any bl recommendations with a particularly jealous ml?

Jealousy!!!!

Oooo. Well it's in all of them but boy do the Thai pulps love it. But I'm only pulling from stuff I rated above 7/10 otherwise we be here all night. Putting aside all rumbles of toxicity, let's do this thing!

BLs with a particularly jealous ml (seme)

Jun and Jun

Dark Blue Kiss

Meet You At the Blossom

Star in My Mind

My Engineer

Nitiman

En of Love: Love Mechanics

Love Mechanics

He's Just Crazy Possessive

There's a lot of "mine" glaring

Cosmetic Playlover

Lovely Writer

Minato's Laundromat

My Personal Weatherman

Old Fashion Cupcake

Oxygen

2gether

Addicted

Cutie Pie

Hidden Agenda

HIStory Obsessed

Future

BLs where jealousy acts as a major plot point/narrative driver:

Happy Ending Romance (just not that kind of jealousy)

La Pluie

Love is Science? (BL sides)

Love Tractor

Happy Merry Ending

HIStory 3: Trapped

Love is Better the Second Time Around

Mr Cinderella

TharnType

The Tasty Florida

Why R U? (Thai)

At 25:00, in Alaska

Big Dragon

Love Sick

Individual Circumstances

Make it Right

The Love Triangle Approach

Light On Me

The Rebound

Behind Cut

Oh! Boarding House

Tinted With You

I don't know what's actually going on but jealousy seems to be part of it

My Stand-In

Dangerous Romance

Naughty Babe

Plus and Minus

Vice Versa

What Zabb Man!

Enchanté

All the 2 Moonss and Gen Ys

[jestershark]

HELP WANTED: HAS AI MADE YOUR SERVICES WORSE

Hey folks Im writing an article about the current AI (ML) boom and I'm looking for screencaps from the last few months that you feel like show A) a pronounced decrease in the quality of the service and B) you believe that pronounced decrease to be because of AI

I'm talking terrible google results, grammarly giving bizarre recommendations, anything like that.

if you want your blog sourced i can do that too!

[uptoolateart]

Voyage! Tales of the USS Miraculous

Episode 4 Part 4 is now live - and I did some cover art yesterday (chapter preview below the image)

As Marinette led Ga’Bi and N’Tal back to the observation lounge, no one spoke a word. Perhaps the Ambassador was just tired. From what Marinette had read, melds often drained the melder. Though as he strode at her side, his long sleeves dripping from his arms, he looked neutral as ever. Like nothing had happened.

Unlike Adrien.

No matter how hard she tried, she couldn’t get the images out of her head – like he’d melded with her. The way he’d gripped the edge of that sofa. Those animalistic sounds of distress. His head jerking to get free of the Ambassador’s hands.

Her eyes stung just thinking about it. Everything about that meld had felt off. Now, the question was – what the hell had happened in it? What had the Ambassador seen in that psychic space?

Read at Ao3

[givemeabreakimbig28]

bitter sweet is really gonna be reuniting the whole My Engineer cast im so excited

[stealthnoodle]

I see there's a new post on AO3 on AI and data scraping, the contents of which I would describe as a real mixed bag, and the sheer number of comments on it is activating my self-preservation instincts too much for me to subject myself to reading through them. Instead I'm thinking about how much daylight there is between does or doesn't constitute a TOS violation and what does or doesn't violate community norms, and how AO3 finally rolled out that blocking and muting feature recently, and how I think it would be good, actually, if most people's immediate reaction to seeing a work that announces itself as being the product of generative AI was to mute the user who posted it.

That's my reaction, anyway!

[megumi-fm]

hi i have been inactive for a while due to the chk chk boom. hope you understand.

[carinelian]

seo is drinking vinegar for the early parts of the story because this man will NOT give a fuck once the plot kicks in

it's the payoff of that slowburn build

i happen to be onto that particular flavor of "mc falls in love first and smashes his face to the ground BUT he picks himself back up

then the ML falls second and smashes his face to the ground BUT the MC helps him up

cue ML seeing MC in a new light

cue MC doing some john wick shit

it looks like some classic university drama now and i dont know how we're going to escalate to the action bits but i have a vision and idgaf how to get there

(coming soon on tapas/some writing platform idk but SOMEWHERE on 2025)

[jynjackets]

I finally watched The Creator and holy shit why didn’t any of you tell me it was going to be that beautiful

[crotovane]

currently feeling that "binge watching day in the life of a japanese worker videos" depression tonight

[ellocentlabsin]

Artificial Intelligence Technology

Software development is undergoing a revolution thanks to generative AI's previously unheard-of efficiency. This game-changing technology automates complicated operations ranging from code creation to optimization, hence shortening the development lifecycle. Generative AI enables developers to build, modify, and optimize code quickly and intelligently using machine learning and pattern recognition. This not only speeds up the programming process but also improves overall software quality. Embracing generative AI ushers in a new era of creativity, shorter development cycles, and software that grows with incredible speed and precision. The tremendous interplay between human brilliance and generative AI skills defines the future of software development.

[freevoidman]

on my hands and knees PRAYING that we'll get more Missing Link news within the next month if they're willing to show a new teaser image and update the official account they've GOTTA be willing to at least announce a global CBT (hopefully on iphone like please pretty please don't let it be android exclusive like Ever Crisis was)

[git-commit-die]

ChatGPT, LLMs, Plagiarism, & You

This is the first in a series of posts about ChatGPT, LLMs, and plagiarism that I will be making. This is a side blog, so please ask questions in reblogs and my ask box.

Why do I know what I'm talking about?

I am a machine engineer who specializes natural language processing (NLP). I write code that uses LLMs every day at work and am intimately familiar with OpenAI. I have read dozens of scientific papers on the subject and understand how they work in extreme detail. I have 6 years of experience in the industry, plus a graduate degree in the subject. I got into NLP because I knew it was going to pop off, and now here we are.

Yeah, but why should I trust you?

I've been a Tumblr user for 8 years. I've posted my own art and fanart on the site. I've published writing, both original and fanfiction, on Tumblr and AO3. I've been a Reddit user for over a decade. I'm a citizen of the internet as much as I am an engineer.

What is an LLM?

LLM stands for Large Language Model. The most famous example of an LLM is ChatGPT, which was created by OpenAI.

What is a model?

A model is an algorithm or piece of math that lets you predict or make mimic how something behaves. For example:

The National Weather Service runs weather models that predict how much it's going to rain based on data they collect about the atmosphere

Netflix has recommendations models that predicts whether you'd like a movie or not based on your demographics, what you've watched in the past, and what other people have liked

The Federal Reserve has economic models that predict how inflation will change if they increase or lower interest rates

Instagram has spam models that look at DMs and automatically decide whether they're spam or not

Models are useful because they can often make decisions or describe situations better than a human could. The weather and economic models are good examples of this. The science of rain is so complicated that it's practically impossible for a human to make sense of all the numbers involved, but models are able to do so.

Models are also useful because they can make thousands or millions of decisions much faster than a human could. The recommendations and spam models are good examples of this. Imagine how expensive it would be to run Instagram if a human had to review every single DM and decide whether it was spam.

What is a language model?

A language model is a model that can look at a piece of text and tell you how likely it is. For example, a language model can tell you that the phrase "the sky is blue" is more likely to have been written than "the sky is peanuts."

Why is this useful? You can use language models to generate text by picking letters and words that it gives a high score. Say you have the phrase "I ate a" and you're picking what comes next. You can run through every option, see how likely the language model thinks it is, and pick the best one. For example:

I ate a sandwich: score = .7

I ate a $(iwnJ98: score = .1

I ate a me: score = .2

So we pick "sandwich" and now have the phrase "I ate a sandwich." We can keep doing this process over and over to get more and more text. "I ate a sandwich for lunch today. It was delicious."

What makes a large language model large?

Large language models are large in a few different ways:

Under the hood, they are made of a bunch of numbers called "weights" that describe a monstrously complicated mathematical equation. Large language models have a ton of the weights--as many as tens of billions of them.

Large language models are trained on large amounts of text. This text comes mostly from the internet but also includes books that are out of copyright. This is the source of controversy about them and plagiarism, and I will cover it in greater detail in a future post.

Large language models are a large undertaking: they're expensive and difficult to create and run. This is why you basically only see them coming out of large or well-funded companies like OpenAI, Google, and Facebook. They require an incredible amount of technical expertise and computational resources (computers) to create.

Why are LLMs powerful?

"Generating likely text" is neat and all, but why do we care? Consider this:

An LLM can tell you that:

the text "Hello" is more likely to have been written than "$(iwnJ98"

the text "I ran to the store" is more likely to have been written than "I runned to the store"

the text "the sky is blue" is more likely to have been written than "the sky is green"

Each of them gets us something:

LLMs understand spelling

LLMs understand grammar

LLMs know things about the world

So we now have an infinitely patient robot that we can interact with using natural language and get it to do stuff for us.

Detecting spam: "Is this spam, yes or no? Check out rxpharmcy.ca now for cheap drugs now."

Personal language tutoring: "What is wrong with this sentence? Me gusto gatos."

Copy editing: "I'm not a native English speaker. Can you help me rewrite this email to make sure it sounds professional? 'Hi Akash, I hope...'"

Help learning new subjects: "Why is the sky blue? I'm only in middle school, so please don't make the explanation too complicated."

And countless other things.