According to deeplearning.ai, only 22% of companies using machine learning have successfully deployed a model. The need for ML Engineers is growing exponentially as the industry moves towards data-centric AI.
ML Engineering (MLOps) is at the intersection of Machine Learning, DevOps, and Data Engineering. It is a critical role that ensures AI products get deployed in production in a scalable and reliable way.
If you want to take your ML skills to the next level, WeCloudData has good news for you. Our ML Engineer Certificate program is created for professionals like you who want to sharpen their skills in deep learning, computer vision, NLP, big data, and MLOps.
Unsure which path to take?
Many AI projects fail due to the lack of MLOps expertise to help put ML in production. Companies realize that building successful AI products requires data engineers, scientists, ML engineers, and product managers to work in a team.
ML Engineers play an essential role in putting models in production and ensuring models can be continuously integrated and deployed (CI/CD) with high-quality data (data-centric AI).
If you currently work as a Data Scientist, Software Engineer, or DevOps professional, ML Engineer could be a great natural progression.
MLOps’ most important task is to make high quality data available through all stages of the ML project lifecycle.
-Andrew Ng on data-centric AI-
Common skill sets
- Coding (Python | Java)
- ML | Deep Learning
- Tensorflow | PyTorch | TFX
- ML at Scale (Spark ML)
- Containerization (Docker)
- Kubeflow | MLflow
- Cloud (AWS, GCP, Azure)
- Airflow | SageMaker
AVERAGE SALARY (CA)
AVERAGE SALARY (US)
ML in production is more than just the code
Source: Google NIPS Paper
What do ML Engineers do?
On a typical day, an ML Engineer may be involved in the following tasks:
- Use ML frameworks such as Tensorflow and PyTorch to build ML pipelines
- Work with distributed ML engines such as Spark ML to scale ML model training
- Automate ML and data pipelines using Apache Airflow
- Build reproducible models using Docker, DVC, and MLflow
- Deploy and orchestrate ML pipelines to Kubernetes
- Create the infrastructure for model-serving in production
- Build infrastructure to continuously monitor model performance and detect feature drift in production
Who can become an ML Engineer?
ML Engineering has a higher entry bar. An ideal candidate will have a solid software/programming background, decent knowledge in data science and machine learning, as well as experience with CI/CD and Data Engineering.
It may sound scary to beginners. The reality is that it’s very hard to find the ideal candidates. So as long as you are a relentless learner who can demonstrate great desire for learning, strong aptitude for problem solving, and the dedication to make it happen.
If you are a data scientist, then learning data engineering, DevOps, and the principles of software engineering is the key.
If you are coming from a DevOps background, learning AI frameworks, improving data wrangling skills, and knowing the difference between AI pipelines and traditional software CI/CD pipelines are important.
How much do ML Engineers earn?
The average base pay for ML Engineers is between $110k and $130k in the U.S. It is the highest in different data & AI roles.
This instructor-led part-time program gives you the opportunity to learn from the industry’s best and practice what you learn through hands-on projects. You will build awesome portfolios and receive job support for 6 months after graduation.
Short-term online live courses that focus on specific data skills. Learn in-demand data analytics, data science, data engineering, or AI skills with industry experts in the evening, or on weekends.
Special discount for bundles
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