5 answers
6
Questions
Asked
625 views
What is a life of a machine learning engingeer is like?
I have an interest to work as a software enginner and focus on areas like artificial intellgence and machine learning. However, I am wodering what specific tasks would an engineer do.
Login to comment
5 answers
Updated
Chandler’s Answer
The life of a machine learning engineer is full of possibilities. There are many types of machine learning. Creating new ideas through neural networks includes a lot of planning and collaboration with teams. Deterministic programing includes a lot of individual time programming algorithms. Engineering is about making possible what seems impossible. Machine learning is a way to take out all the boring repetitive tasks so you can focus on what you want to create!
Reach out to local technology companies and see if they do machine learning.
Ask to job shadow someone that works in machine learning.
Chandler recommends the following next steps:
Updated
Dana’s Answer
I have two Machine Learning engineers in my team. We call them Applied Science Engineers in my company. Here are some things that they do:
- collaborate in order to find creative solutions
- they are passionate about learning and exploring new ways of doing things, new ML libraries
- read and write science academic papers
- write code
- find datasets in order to test their algorithms (or generate artificial datasets)
- collaborate in order to find creative solutions
- they are passionate about learning and exploring new ways of doing things, new ML libraries
- read and write science academic papers
- write code
- find datasets in order to test their algorithms (or generate artificial datasets)
Updated
Hrushikesh’s Answer
The field of machine learning has been rapidly growing in recent years, and with it, the demand for machine learning engineers. A machine learning engineer is a specialized software engineer who is responsible for designing and implementing machine learning models and systems. The job involves a unique set of skills and responsibilities, and the day-to-day life of a machine learning engineer can be challenging and rewarding.
A typical day for a machine learning engineer can vary greatly depending on the stage of the project they are working on. However, there are a few common activities that most machine learning engineers perform regularly. One of the primary tasks of a machine learning engineer is to collect, clean, and preprocess data. This involves understanding the data sources, selecting relevant data, cleaning it, and structuring it in a way that is suitable for machine learning algorithms.
Once the data is ready, the machine learning engineer needs to select the appropriate machine learning algorithms and techniques to solve the problem at hand. This involves researching and evaluating various machine learning models, assessing their strengths and weaknesses, and selecting the best one for the job. They may need to create custom models or modify existing ones to achieve the desired results.
After selecting the machine learning model, the engineer needs to train and validate it on the prepared data. This involves setting up the necessary infrastructure for training the model, selecting the appropriate parameters and hyperparameters, and running multiple iterations to optimize the model's performance. The machine learning engineer also needs to validate the model to ensure it is not overfitting or underfitting and is generalizing well.
Machine Learning Classes in Pune
Once the model is trained and validated, the engineer needs to deploy it into a production environment. This involves integrating the model into the software system, creating APIs for data ingestion and output, and ensuring the model is running efficiently and accurately in the production environment. They also need to monitor the performance of the model, make necessary updates and improvements, and troubleshoot any issues that arise.
Besides the technical tasks, a machine learning engineer also needs to communicate with stakeholders and team members regularly. This includes understanding the business needs of the project, explaining technical concepts and results to non-technical stakeholders, and collaborating with other engineers, data scientists, and product managers to achieve the project's objectives.
The life of a machine learning engineer is not just about writing code and building models. It involves a range of skills and tasks, including data analysis, problem-solving, project management, and communication. Here are some key skills and qualities that are essential for a successful career as a machine learning engineer:
Strong programming skills: A machine learning engineer needs to be proficient in at least one programming language, such as Python or Java, and have experience with data manipulation, algorithms, and data structures.
Mathematical and statistical skills: Machine learning involves a lot of mathematical and statistical concepts, including linear algebra, probability theory, and calculus. A machine learning engineer needs to have a strong understanding of these concepts to design and implement effective machine learning models.
Familiarity with machine learning frameworks: There are many machine learning frameworks available, such as TensorFlow, PyTorch, and scikit-learn. A machine learning engineer needs to be familiar with at least one of these frameworks and understand their features and limitations.
Data preparation and preprocessing skills: Machine learning algorithms require large amounts of clean, structured data to be effective. A machine learning engineer needs to have experience with data preparation and preprocessing techniques, such as data cleaning, feature engineering, and data normalization.
Problem-solving skills: A machine learning engineer needs to be able to analyze complex problems, break them down into smaller, manageable parts, and develop effective solutions.
Communication and collaboration skills: A machine learning engineer needs to be able to communicate technical concepts to non-technical stakeholders and collaborate effectively with team members.
A typical day for a machine learning engineer can vary greatly depending on the stage of the project they are working on. However, there are a few common activities that most machine learning engineers perform regularly. One of the primary tasks of a machine learning engineer is to collect, clean, and preprocess data. This involves understanding the data sources, selecting relevant data, cleaning it, and structuring it in a way that is suitable for machine learning algorithms.
Once the data is ready, the machine learning engineer needs to select the appropriate machine learning algorithms and techniques to solve the problem at hand. This involves researching and evaluating various machine learning models, assessing their strengths and weaknesses, and selecting the best one for the job. They may need to create custom models or modify existing ones to achieve the desired results.
After selecting the machine learning model, the engineer needs to train and validate it on the prepared data. This involves setting up the necessary infrastructure for training the model, selecting the appropriate parameters and hyperparameters, and running multiple iterations to optimize the model's performance. The machine learning engineer also needs to validate the model to ensure it is not overfitting or underfitting and is generalizing well.
Machine Learning Classes in Pune
Once the model is trained and validated, the engineer needs to deploy it into a production environment. This involves integrating the model into the software system, creating APIs for data ingestion and output, and ensuring the model is running efficiently and accurately in the production environment. They also need to monitor the performance of the model, make necessary updates and improvements, and troubleshoot any issues that arise.
Besides the technical tasks, a machine learning engineer also needs to communicate with stakeholders and team members regularly. This includes understanding the business needs of the project, explaining technical concepts and results to non-technical stakeholders, and collaborating with other engineers, data scientists, and product managers to achieve the project's objectives.
The life of a machine learning engineer is not just about writing code and building models. It involves a range of skills and tasks, including data analysis, problem-solving, project management, and communication. Here are some key skills and qualities that are essential for a successful career as a machine learning engineer:
Strong programming skills: A machine learning engineer needs to be proficient in at least one programming language, such as Python or Java, and have experience with data manipulation, algorithms, and data structures.
Mathematical and statistical skills: Machine learning involves a lot of mathematical and statistical concepts, including linear algebra, probability theory, and calculus. A machine learning engineer needs to have a strong understanding of these concepts to design and implement effective machine learning models.
Familiarity with machine learning frameworks: There are many machine learning frameworks available, such as TensorFlow, PyTorch, and scikit-learn. A machine learning engineer needs to be familiar with at least one of these frameworks and understand their features and limitations.
Data preparation and preprocessing skills: Machine learning algorithms require large amounts of clean, structured data to be effective. A machine learning engineer needs to have experience with data preparation and preprocessing techniques, such as data cleaning, feature engineering, and data normalization.
Problem-solving skills: A machine learning engineer needs to be able to analyze complex problems, break them down into smaller, manageable parts, and develop effective solutions.
Communication and collaboration skills: A machine learning engineer needs to be able to communicate technical concepts to non-technical stakeholders and collaborate effectively with team members.
Updated
Aaryan’s Answer
Hi Jiarong,
A ML Engineer can mean 2 separate things from an industry viewpoint and the role of a ML engineer usually differs across companies. Let me talk about these 2 things briefly first -
1. You have a business problem at hand that can leverage ML. You have to figure out a working solution to this problem in form of a ML model by running various experiments. You interact more with features, frameworks such as PyTorch (and many others), and more research-based-implementation concepts such as neural networks. You can be working on different types of problems - predictive modeling, time-series forecasting, NLP, Computer Vision, etc. You may even find yourself doing some "Data Science" work, specifically running SQL queries, interacting with the databases and data engineering aspects, etc.
2. You have a ML model at hand that works well and will solve the given business problem. But how do you make this model easily usable and adoptable by the company's use-cases? You will have to figure out the correct infrastructure and the corresponding engineering effort to serve this model to consumer-facing use-cases, and ensure it's scalability and availability. You will be mainly focusing on finding ways to make the model development processes quicker, setting up inference services, and contributing to maybe a broader data or ML platform.
Now, as a general trend, a small(er) company would want you to wear several hats and probably do (almost) all of the tasks mentioned above. A large(er) company, instead, may have these 2 roles separated out to a great extent, and would be looking for a specialized engineer to only do a specific set of tasks. It really varies a lot for each company, so you would want to go through job descriptions in more detail to understand what that particular job is expecting from a candidate.
That said, it would also be helpful for you to figure out which aforementioned path you want to take, and look for such opportunities.
A ML Engineer can mean 2 separate things from an industry viewpoint and the role of a ML engineer usually differs across companies. Let me talk about these 2 things briefly first -
1. You have a business problem at hand that can leverage ML. You have to figure out a working solution to this problem in form of a ML model by running various experiments. You interact more with features, frameworks such as PyTorch (and many others), and more research-based-implementation concepts such as neural networks. You can be working on different types of problems - predictive modeling, time-series forecasting, NLP, Computer Vision, etc. You may even find yourself doing some "Data Science" work, specifically running SQL queries, interacting with the databases and data engineering aspects, etc.
2. You have a ML model at hand that works well and will solve the given business problem. But how do you make this model easily usable and adoptable by the company's use-cases? You will have to figure out the correct infrastructure and the corresponding engineering effort to serve this model to consumer-facing use-cases, and ensure it's scalability and availability. You will be mainly focusing on finding ways to make the model development processes quicker, setting up inference services, and contributing to maybe a broader data or ML platform.
Now, as a general trend, a small(er) company would want you to wear several hats and probably do (almost) all of the tasks mentioned above. A large(er) company, instead, may have these 2 roles separated out to a great extent, and would be looking for a specialized engineer to only do a specific set of tasks. It really varies a lot for each company, so you would want to go through job descriptions in more detail to understand what that particular job is expecting from a candidate.
That said, it would also be helpful for you to figure out which aforementioned path you want to take, and look for such opportunities.
Updated
Laura’s Answer
Hi Jiarong,
adding to what was already said: as a ML engineer, you also have to do stakeholder management to a certain degree. In this case, stakeholders are managers and other decision makers (product manager etc.) inside the company. Sometimes it is forgotten that you have to ensure that what you are coding also fulfils the stakeholders expectations; and sometimes you might disagree with them. A book I can recommend is "Designing Machine Learning Systems" by Chip Huyen. In the beginning, she describes the different roles and responsibilities.
adding to what was already said: as a ML engineer, you also have to do stakeholder management to a certain degree. In this case, stakeholders are managers and other decision makers (product manager etc.) inside the company. Sometimes it is forgotten that you have to ensure that what you are coding also fulfils the stakeholders expectations; and sometimes you might disagree with them. A book I can recommend is "Designing Machine Learning Systems" by Chip Huyen. In the beginning, she describes the different roles and responsibilities.