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.
Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models. Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. It is the practice of collaboration between data science and IT teams designed to accelerate the entire machine lifecycle across model development, deployment, monitoring, and more.
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.
Databricks MLOps - Deploy Machine Learning Model On Azure In this little video series I'll get to the bottom of how you can control the Azure Databricks platform with your DevOps toolbox. In this part we're deploying the Machine Learning Model, which we created in part 2, first as Azure Container Instance and then to the Azure Kubernetes Service. For this purpose, we use the Azure Machine Learning Service to create containerised Web API. Everything will be - of course - completely automated by using our Azure DevOps pipeline. If you haven't yet seen the first video in this series, I strongly recommend that you do so: https://www.youtube.com/watch?v NLXis... Subscribe for more free data analytics videos: https://www.youtube.com/saschadittman...
The second half of the talk describes an MLOPS workflow for building a Shiny application, based on a model trained using the caret package. I used the Azure ML service and the azuremlsdk R package to coordinate the training process and provide a cluster of machines to train multiple models simultaneously and track accuracy to choose the best model to deploy. You can find the complete code behind the demonstration shown in this vignette, included with the azuremlsdk package.