Skills needed to build a cloud based enterprise system that is secure, scalable and performant. Also, there’s a whole real-time aspect to interacting with this type of solution and inferencing has to be well designed.
great thinking! will add this to my list, coming up with a syllabus/defined set of skills for the AI Engineer is the most requested followup and something I purposely left open for others to weigh in haha
I needed this! I am a software engineer and started at an AI startup about a month ago. I was initially incredibly overwhelmed with all the "Deep Learning" and "ML" courses, and what not, out there. It wasn't until i started digging deeper into "what are people actually doing with this stuff?", that i started to realize there is a whole other side to this, and thus found a whole slew of tools, and practice projects to help me learn.
I like where you are going. But one AI engineer persona knows how to build applications and platforms with all the new AI stuff. Aka, they need to understand how to create architectures that are reusable and scalable.
Just as we've seen specialized roles emerge in the past two decades in the tech industry, we're bound to see a proliferation of specialized roles in the AI space.
This will be a combination of technical, ethical, regulatory, and product-focused roles, reflecting the multifaceted nature and impact of AI on society and industry.
Eng:
AI Engineer / ML Engineer --> While DS roles focus on building models, there's a growing realization that engineering robust AI systems is its own beast, leading to the rise of the AI or ML engineer role. These engineers focus on commercializing models, ensuring scalability, and integrating AI into broader software systems.
ML Research Engineer: This is a bridge between pure research scientists and production-focused software engineers. They transform experimental models from research papers into prototypes that can be tested in real-world scenarios.
MLOps Engineer: Analogous to DevOps in software engineering, MLOps focuses on the end-to-end ML lifecycle, ensuring smooth deployment, monitoring, and updating of ML models.
Infra:
New tools and platforms are constantly emerging to support the AI development lifecycle. Familiarity and expertise in using these tools, whether it's TensorFlow, PyTorch, MLflow, TFX, or newer platforms, are becoming vital.
100% ML Infra specialists are becoming a thing.
Governance:
As AI's impact on society becomes more pronounced, there's a growing need for specialists who understand the ethical implications of AI models and can design systems that are fair and transparent. These roles can be a blend of technical and philosophical expertise.
AI Product Managers:
Traditional product management is evolving to cater to AI-driven products. AI PMs need to understand the unique challenges of AI projects, from data acquisition to model interpretability, while aligning with business goals.
AI System Architects:
Just like system architects design complex software systems, AI architects will focus on designing intricate AI ecosystems, considering aspects like data pipelines, training infrastructures, inference optimization, and more.
Edge AI:
With AI moving to edge devices (like smartphones, IoT devices, etc.), there's a need for experts who can optimize models to run with limited resources without compromising performance.
AI Regulation and Policy Experts:
So many new classes and subsets of technical and non-technical roles are going to emerge from AI.
Skills needed to build a cloud based enterprise system that is secure, scalable and performant. Also, there’s a whole real-time aspect to interacting with this type of solution and inferencing has to be well designed.
great thinking! will add this to my list, coming up with a syllabus/defined set of skills for the AI Engineer is the most requested followup and something I purposely left open for others to weigh in haha
I needed this! I am a software engineer and started at an AI startup about a month ago. I was initially incredibly overwhelmed with all the "Deep Learning" and "ML" courses, and what not, out there. It wasn't until i started digging deeper into "what are people actually doing with this stuff?", that i started to realize there is a whole other side to this, and thus found a whole slew of tools, and practice projects to help me learn.
I like where you are going. But one AI engineer persona knows how to build applications and platforms with all the new AI stuff. Aka, they need to understand how to create architectures that are reusable and scalable.
Just as we've seen specialized roles emerge in the past two decades in the tech industry, we're bound to see a proliferation of specialized roles in the AI space.
This will be a combination of technical, ethical, regulatory, and product-focused roles, reflecting the multifaceted nature and impact of AI on society and industry.
Eng:
AI Engineer / ML Engineer --> While DS roles focus on building models, there's a growing realization that engineering robust AI systems is its own beast, leading to the rise of the AI or ML engineer role. These engineers focus on commercializing models, ensuring scalability, and integrating AI into broader software systems.
ML Research Engineer: This is a bridge between pure research scientists and production-focused software engineers. They transform experimental models from research papers into prototypes that can be tested in real-world scenarios.
MLOps Engineer: Analogous to DevOps in software engineering, MLOps focuses on the end-to-end ML lifecycle, ensuring smooth deployment, monitoring, and updating of ML models.
Infra:
New tools and platforms are constantly emerging to support the AI development lifecycle. Familiarity and expertise in using these tools, whether it's TensorFlow, PyTorch, MLflow, TFX, or newer platforms, are becoming vital.
100% ML Infra specialists are becoming a thing.
Governance:
As AI's impact on society becomes more pronounced, there's a growing need for specialists who understand the ethical implications of AI models and can design systems that are fair and transparent. These roles can be a blend of technical and philosophical expertise.
AI Product Managers:
Traditional product management is evolving to cater to AI-driven products. AI PMs need to understand the unique challenges of AI projects, from data acquisition to model interpretability, while aligning with business goals.
AI System Architects:
Just like system architects design complex software systems, AI architects will focus on designing intricate AI ecosystems, considering aspects like data pipelines, training infrastructures, inference optimization, and more.
Edge AI:
With AI moving to edge devices (like smartphones, IoT devices, etc.), there's a need for experts who can optimize models to run with limited resources without compromising performance.
AI Regulation and Policy Experts:
So many new classes and subsets of technical and non-technical roles are going to emerge from AI.
...and a whole lot else!