In one of our previous articles it was highlighted how DevOps manages the End-to-End application cycle, leveraging agility and automation. CI/CD pipelines, collaboration and transparency, monitoring and automation are part of the list on how DevOps leverages and facilitates agility. What if then we bring those to support ML? That is how MLOps comes to the table and starts making sense! Lego Alike data assembly - Generated with Gemini A big tech corporation, or a startup, nowadays will see how it is becoming a requirement to incorporate AI and Machine learning (ML) in their operations. ML components are key parts of the ecosystem, supporting the solutions provided to clients. As a result, DevOps and MLOps have become part of the "secret sauce" for success. What is MLOps Just to bring the definition of what you probably know (or put together based on the above) MLOps focuses on the life-cycle management of machine learning models. It combines machine learning with traditional ...
Have you ever been in a conversation that you feel at best is just a waste of time; at worst, that the other person does not care? Have you been the messenger in some of them, the recipient in another? We’ve all been there; at times with family, with friends, at work. When at work, has this ever happened to you in a group meeting, at a one-on-one; when giving or receiving feedback? Photo by Christina Morillo via Pexels I knew the type of manager I wanted to be from the start of my mentorship. As I continued to navigate the waters, it became increasingly clear that the path I had chosen was appropriate, and therefore, the type of future colleagues I wanted to be surrounded by, and the type of mentors I wanted to continue seeking guidance from. For me, the path has always been of being a leader over a manager. I believe I can attribute that, first and foremost, to my family’s values and principles, and the way my parents raised us. Secondly, my path in the ...