Thanks to the rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) across industries, AI and ML have found a place in the common vocabulary. Almost every sector of the industry (healthcare, e-commerce, IoT, banking & finance, etc.) are leveraging AI and ML to streamline business operations and create innovative products/services. So, when everyone in the industry is using AI and ML, what can you do differently to up your game? The answer is MLOps or Machine Learning Operationalization. In simple terms, MLOps is the Machine learning equivalent of DevOps.
Inside IT organizations, getting machine learning technologies from pilot into production is one of the hot topics of 2019. At this point, many organizations have run successful pilots. Yet many more have still haven't achieved the value promised by machine learning because it isn't integrated into organizational processes. A Gartner study showed that only 47% of machine learning models are making it into production. Organizations are employing a few different methods to get their machine learning investments to production.
"MLOps (a compound of Machine Learning and "information technology OPerationS") is [a] new discipline/focus/practice for collaboration and communication between data scientists and information technology (IT) professionals while automating and productizing machine learning algorithms." The understanding of the machine learning lifecycle is constantly evolving. When I first saw graphics illustrating this "cycle" years ago, the emphasis was on the usual suspects (data prep and cleaning, EDA, modeling etc…). Less notice was given to the more elusive and less tangible final state -- often termed "deployment", "delivery" or in some cases just "prediction". At the time, I don't think a lot of rising data scientists really considered the sheer scope of that last term (I sure as hell didn't).
MLOps is a relatively new concept in the AI (Artificial Intelligence) world and stands for "machine learning operations." Its about how to best manage data scientists and operations people to allow for the effective development, deployment and monitoring of models. "MLOps is the natural progression of DevOps in the context of AI," said Samir Tout, who is a Professor of Cybersecurity at the Eastern Michigan University's School of Information Security & Applied Computing (SISAC). "While it leverages DevOps' focus on security, compliance, and management of IT resources, MLOps' real emphasis is on the consistent and smooth development of models and their scalability." The origins of MLOps goes back to 2015 from a paper entitled "Hidden Technical Debt in Machine Learning Systems."
It's exciting to see the Pytorch Community continue to grow and regularly release updated versions of PyTorch! Recent releases improve performance, ONNX export, TorchScript, C frontend, JIT, and distributed training. Several new experimental features, such as quantization, have also been introduced. At the PyTorch Developer Conference earlier this fall, we presented how our open source contributions to PyTorch make it better for everyone in the community. We also talked about how Microsoft uses PyTorch to develop machine learning models for services like Bing.