This post has been republished via RSS; it originally appeared at: Microsoft Azure Blog.
Everyone’s talking about machine learning (ML). Business decision makers are finding ways to deploy machine learning in their organizations. Data scientists are keeping up with all the advancements, tools, and frameworks available. Media outlets are reporting on awe-inspiring breakthroughs in the artificial intelligence revolution.
We believe the way forward lies in democratizing artificial intelligence and machine learning by proxy. This means making machine learning services available to singular data scientists and developers, small to medium sized businesses, and global organizations–all with the ability to scale their models up and out.
This means offering automated and prebuilt algorithms, as well as the ability to create highly customized models. It also means ensuring they are compatible with open source frameworks.
The challenges of machine learning
As you likely already know, machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. But the promises of machine learning come with challenges. Here are just a few:
- There is a lot of manual math, data analysis, programming, training, and experimentation.
- There are multiple ways to solve every problem.
- Challenges arise in monitoring and evaluating the precision, accuracy, and efficacy of a given model.
- Data scientists struggle to find the right development tools, debugging tools, and educational resources.
Azure Machine Learning service
The Azure Machine Learning service provides a cloud-based service you can use to develop, train, test, deploy, manage, and track machine learning models. With Automated Machine Learning and other advancements available, training and deploying machine learning models is easier and more approachable than ever.
Below are three of the key pillars of Azure Machine Learning service that give us an edge. I’ll be going into greater detail about each of these pillars in subsequent blogs, so stay tuned!
These three pillars apply largely to automated machine learning, also provided under Azure Machine Learning service. Automated machine learning helps users of all skill levels accelerate their pipelines, leverage open source frameworks, and scale easily. Automated machine learning, a form of deep machine learning, makes machine learning more accessible across an organization.
1. End-to-end ML lifecycle management
There’s a lot that goes into the machine learning lifecycle. Data preparation, experimentation, model training, model management, deployment, and monitoring traditionally require time and manual effort. Azure Machine Learning service seamlessly integrates with Azure services to provide end-to-end capabilities for the entire machine learning lifecycle, making it simpler and faster than ever. With Azure Machine Learning Service, you can:
- Create multiple or common workspaces to collaborate easily across teams.
- Centralize management of all model artifacts.
- Schedule runs in parallel.
- Manage scripts and data separately.
- Ensure ease of support and maintenance with CI/CD while driving quality over time and preventing model drift.
- Easily track your experiments and version your models.
- Manage and monitor your models directly in the Azure portal.
2. Power productivity and ease-of-use with an open platform
Data scientists and developers are empowered to easily build and train highly accurate machine learning and even deep-learning models through the frameworks and tools that they’re familiar with. You can now bring machine learning models to market faster with flexible open tools. With Azure Machine Learning, you can:
- Use your favorite open source frameworks.
- Use a familiar and rich set of tools, such as Jupyter Notebooks, with the Python extension for Visual Studio Code.
- Reduce friction and refocus on building models.
- Easily leverage the multi-cloud interoperability with built-in ONNX support.
3. Scale up and out to the cloud or edge easily
Previously, machine learning requires powerful compute capabilities in order to train models quickly. With hardware acceleration (GPUs, containers, etc.), scaling up or out is much easier. With Azure Machine Learning, you can:
- Use any data and deploy models anywhere.
- Scale out training from your local laptop or workstation to the cloud with compute on-demand.
- Get GPU and deep learning framework support.
- Distribute training for faster results by running models over a cluster of GPU-equipped computers in tandem.
- Feel confident in enterprise-grade security, audit, and compliance.
- Have reliable model deployment across cloud and edge.
- Get cost effective inferencing with batch prediction and scoring.
- Consume real-time scoring for targeted outcomes.
As you can see, Azure Machine Learning service provides an effective solution to a number of top concerns for individuals and organizations seeking to deploy machine learning models and are making an effort to advance machine learning for everyone’s benefit. Look out for more upcoming blogs in this series, where we will cover each of these three pillars in more detail.
Learn more about the Azure Machine Learning service.
Get started with a free trial of Azure Machine Learning service.