Devops Engineer to Machine Learning

Machine Learning Forum
1

Hello everyone!

Seeking career advice here. :star2: Considering a shift from a senior DevOps role to machine learning.

Here’s my background:

I bring 10 years in tech, with 5 focused on DevOps—strong in systems engineering, automation, and some Python and Golang. I’ve crafted a solid learning plan and started studying.

Questions:

  1. Overall, should I steer clear of ML based on discouragements I’ve heard?
  2. If you’ve made this move, was it worthwhile?
  3. Can I self-learn to mid-level proficiency, or will I likely start at a junior level?
  4. Is ML/Ops a smart interim step before a full transition to ML Engineer?
  5. Are there ample ML engineer opportunities in the EU compared to DevOps roles? :thinking:
2

It is a pretty fascinating topic; without a degree in the relevant field, it might be challenging because of how much it depends on statistics, arithmetic, and other things, but it is not insurmountable, in my opinion.

3

If you have credentials for DevOps, how did you receive the opportunity for ML engineer?
Self-research?

4

Your DevOps background gives you a solid foundation to transition into machine learning (ML). The skills you’ve developed, such as systems engineering and automation, are highly relevant to ML roles.

Navigating the Transition:

  • Overcoming Discouragement: Don’t let challenges or negative feedback discourage you. Many successful ML engineers have transitioned from different fields.
  • Evaluating the Move: Transitioning to ML can be rewarding, offering new challenges, growth opportunities, and higher earning potential.
  • Structured Learning: While self-learning is an option, a structured approach through online courses, bootcamps, or part-time degree programs can accelerate your progress.
  • Starting Point: You might begin at a junior or mid-level role despite your experience. Demonstrating your skills and enthusiasm for ML will help you advance quickly.
  • ML/Ops as a Stepping Stone: Leveraging your DevOps expertise in ML/Ops roles can be a strategic interim step toward becoming a full-fledged ML engineer.
  • EU Job Market: The demand for ML engineers is rising in the EU, and your strong DevOps background can make you a competitive candidate despite the competition.

Key Skills to Develop:

  • Python and Statistical Programming: Master Python and consider learning additional languages like R or Julia for statistical programming.
  • Machine Learning Algorithms: Gain a deep understanding of algorithms like linear regression, decision trees, and neural networks.
  • Data Science: Enhance your skills in data cleaning, preprocessing, and analysis.
  • Cloud Platforms: Develop expertise in cloud platforms like AWS, GCP, or Azure, as these are essential for deploying ML models.