How to Become a Machine Learning Engineer in Seven Steps

Machine learning is the hottest job nowadays. But do you know that machine learning is part of Artificial Intelligence? If you love to work with new technologies like machine learning, Artificial Intelligence, and Data science. Then in this article we will guide you to become a successful machine learning engineer in seven steps.

You can be either a data engineer, data analytics, machine learning engineer, Data scientist, or AI engineer. Each of these profiles has different responsibilities. Machine learning engineers work with ML Algorithms. The salary of ML engineers is attractive, but it depends on various factors like experience, industry and complexity of the work. Machine learning engineers get a competitive package.

Roles and Responsibilities

A machine learning engineer can perform various tasks depending on the project, teams and organizations. Machine learning performs these tasks -

• Working with Data Engineers - They collaborate with Data engineers to get datasets. ML engineers can also perform this task.
• Training models - They train ML models using various available ML algorithms and given datasets.
• Selecting model - They choose the best performing model to deploy.
• Testing models - They test and retrain model if require to improve deployed model

If you are really interested to become a successful ML engineer then you can follow these seven points as guide to prepare yourself.

1. Education

Though it is not necessary to get any specific degree to become a ML engineer because most ML hiring companies seek only skills but not for specific degree or qualification. We will suggest you to earn some degree or qualification or certificate related to ML and AI because you will get basic understanding of ML algorithms and basic concepts. You can learn from tutorialspoint if you don't have much time to pursue any degree. We have explained all the basic concepts of ML and AI. You will get understanding of core concepts.

2. Programming languages- Python or R

You need to write code in programming languages. There are various languages used to implement ML algorithms but Python and R are most popular. We suggest you learn Python programming because it is easy to learn and has high level syntax. You should gain all the basics of Python programming languages before moving forward to Machine Learning. You can learn Python programming language available on the tutorialspoint.

3. Learn Machine learning

Once you complete Python programming language, You can start with Machine Learning basics and ML algorithms. You should learn all the type of Machine learning like supervised, unsupervised, and reinforcement. You should learn all types of ML classification like regression and classifications. You should also learn Machine learning algorithms based on classification and regression.

Other than machine learning, you should also learn about deep learning, natural language processing, neural networks, statistics, and linear algebra.

4. Python libraries

Once you complete machine learning you can learn Python libraries required for machine learning models. These libraries are very helpful from data preprocessing to model deployment. There are various Python libraries available but these are some popular -

• Pandas - It is required for data manipulations. It is good for terrible data.
• NumPy - It is required for numerical operations. It is good for array data.
• Matplotlib - It is required for data visualization.
• Seaborn - It is also required for data visualization. Seaborn based on Matplotlib.
• Scikit-learn - It is used for data analysis and data mining. It is based on other libraries like pandas and numPy.
• Tensorflow and PyTorch - These generally used for deep learning.

5. MLOps

Machine learning engineers train the models and also deploy the model in the production environment, so machine learning engineers should know the complete cycle of machine learning operation starting from gathering data to the deploy model and maintenance. Machine learning engineers should have understanding of cloud computing services for machine learning like amazon sagemaker, microsoft azure, machine learning and GCP Vertex AI. Machine learning should have knowledge of AWS, SQL, Mongodb. Machine Learning Operations (MLOps) is equivalent to DevOps. Machine learning engineer MLflow, MetaFlow, CubeFlow. Github and docker are required in the development phase. A ML engineer should have knowledge of Git.

6. AI and LLM

ML comes under Artificial Intelligence (AI). Deep learning comes under ML. Large language models (LLM) comes under deep learning. Nowadays LLM are very trending and has a lot of scope in the market. Machine learning engineers must have knowledge of LLM and AI. LLM are based on transformer architecture. Machine learning engineers should know transformer architecture, neural networks, RNN, LSTM. They should know how to find a large language model. They should also know vector and RAG (Retrieval Argument Generation). These are technologist most of the companies working. There are lot of startup working only on LLM technology. They give competitive salaries too for AI engineers.

You can learn these technologies on tutorialspoint too.

7. Apply & Learn

Once you complete all the core concepts of machine learning and MLOps. You can start applying for the jobs in this role. You can choose any online job platform or apply directly on company website. You can also reach the team leader or manager to ask if they have any job for you in this role in the company.

Machine learning engineer always learn and grow. There always are always new technologies coming up and machine learning engineers shoulkd never stay with old technologies. As nowadays large language models are trendings but next few year there can be new technologist in trending. A machine learning engineer should level up his knowledge and understanding about machine learning because it is like a sea. However core concepts are always remains.

Conclusion

Machine learning jobs are hottest nowadays. One only requires skills to work with machine learning algorithms. No other degree or certificates are needed if you are good with machine learning concepts and have experience. In this article we have discussed seven points to become a successful machine learning engineer. You can follow these points as a guide and assure you will be a good machine learning engineer. You can follow tutorials and courses available at tutorialspoint for this.

FAQs (Frequently Ask Questions)

Is degree mandatory for machine learning engineer?

If you have experience and skills required for machine learning jobs, then no degree is required in most companies. However we will suggest you to earn some degree related to machine learning or engineering because some of the companies ask for degree too.

What is the salary range of machine learning engineer?

The salary of a machine learning engineer depends on various factors like experience, location, industry and complexity of the work. They earn good and competitive compensation packages.

How long it takes to become a machine learning engineer?

If you are a biginner and don't have any software development skills then it can take 6 to 18 months. But it is total depend on your learning skills and capacity to get knowledge.