MLOps — The dust has not settled yet (ML4Devs Newsletter, Issue 7)
Satish Chandra Gupta
Data/AI Consultant ? I help startups & SMBs escape the AI PoC trap and turn prototypes into products ? Data, ML, DL, LLM, RAG, AI Agents ? Ex- Amazon, Microsoft Research
You must have noticed the buzz about MLOps.
MLOps is the lifecycle, process, and tools for deploying machine learning models in production.
There has been an explosion of MLOps vendors and tools. Many of those are named as xyzFlow or xyzML:
These were just a few examples. Here is the relevant part of?Matt Turck’s 2021 ML/Data tool landscape.
As if all that was not crazy enough...
You may think that these are small vendors or startups, what about the Big Three?
There is a remarkable similarity in the traditional software DevOps offerings from AWS, Azure, and Google Cloud. We can use their MLOps offerings as a template and make some sense out of it all.
So I looked and?Amazon SageMaker?and?Google VertexAI, and there are some similarities in the tooling. But the MLOps worldview of the Big Three has not yet converged. It is apparent from their MLOps Lifecycle and ML Maturity Levels descriptions.
The dust has not settled yet...
This is how the desktop software development world was back in the 1980s and the cloud development world in the 2000s. There were several competing methodologies, processes, and tools, and slowly coherence emerged. The same will happen for ML in 2-3 years. And then, everything about MLOps will be so obvious. Quite like how a caterpillar turns into a butterfly.
ML4Devs is a biweekly newsletter for software developers. The aim is to curate resources for practitioners to design, develop, deploy, and maintain ML applications at scale to drive measurable positive business impact. Each issue discusses a topic from a developer’s viewpoint.
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NVIDIA, Global Head of AI Platform Partnerships | Enterprise AI - Strategic Alliances
2 年Great read, and an important subject bringing innovative approach, solutions and tools to scale AI and ML application development and its lifecycle.
Principal Business Technology Leader - Ecosystem Engineering Lab, Client Engineering. IBM | Software Engineering Leader | AI and ML Enthusiast
2 年Agree. There's a lot of research and fast paced innovation seen in related areas ( model analysis, explainability, monitoring etc ) and associated tooling. This space is surely growing and getting more competitive with small miche players and large hperscalars with their related services. Considering that the rate of AI adoption and production deployment is fast increasing, there will be more demand for MLOps tooling and we should see some consolidation and converging happening
Chief Data Officer, ex:IBM, Oracle |IIT- KGP|ML|AI, Gen-AI & Analytics|Data Engg| Author
2 年Thanks for sharing this - this is the “Cambrian” phase of evolution: explosive arrival of many tools will continue to happen, most will die or be cannibalised or merge and morph). Most of these tools today are struggling to make money as mass scale MLOPs adoption of these models are yet to happen.