This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Tech Community.
Organizations are leveraging artificial intelligence (AI) and machine learning (ML) to derive insight and value from their data and to improve the accuracy of forecasts and predictions. In rapidly changing environments, Azure Databricks enables organizations to spot new trends, respond to unexpected challenges and predict new opportunities. Data teams are using Delta Lake to accelerate ETL pipelines and MLflow to establish a consistent ML lifecycle.
Solving the complexity of ML frameworks, libraries and packages
Customers frequently struggle to manage all of the libraries and frameworks for machine learning on a single laptop or workstation. There are so many libraries and frameworks to keep in sync (H2O, PyTorch, scikit-learn, MLlib). In addition, you often need to bring in other Python packages, such as Pandas, Matplotlib, numpy and many others. Mixing and matching versions and dependencies between these libraries can be incredibly challenging.
Figure 1. Databricks Runtime for ML enables ready-to-use clusters with built-in ML Frameworks
With Azure Databricks, these frameworks and libraries are packaged so that you can select the versions you need as a single dropdown. We call this the Databricks Runtime. Within this runtime, we also have a specialized runtime for machine learning which we call the Databricks Runtime for Machine Learning (ML Runtime). All these packages are pre-configured and installed so you don’t have to worry about how to combine them all together. Azure Databricks updates these every 6-8 weeks, so you can simply choose a version and get started right away.
Establishing a consistent ML lifecycle with MLflow
The goal of machine learning is to optimize a metric such as forecast accuracy. Machine learning algorithms are run on training data to produce models. These models can be used to make predictions as new data arrive. The quality of each model depends on the input data and tuning parameters. Creating an accurate model is an iterative process of experiments with various libraries, algorithms, data sets and models. The MLflow open source project started about two years ago to manage each phase of the model management lifecycle, from input through hyperparameter tuning. MLflow recently joined the Linux Foundation. Community support has been tremendous, with 250 contributors, including large companies. In June, MLflow surpassed 2.5 million monthly downloads.
Diagram: MLflow unifies data scientists and data engineers
Ease of infrastructure management
Data scientists want to focus on their models, not infrastructure. You don’t have to manage dependencies and versions. It scales to meet your needs. As your data science team begins to process bigger data sets, you don’t have to do capacity planning or requisition/acquire more hardware. With Azure Databricks, it’s easy to onboard new team members and grant them access to the data, tools, frameworks, libraries and clusters they need.
Alignment Healthcare, a rapidly growing Medicare insurance provider, serves one of the most at-risk groups of the COVID-19 crisis—seniors. While many health plans rely on outdated information and siloed data systems, Alignment processes a wide variety and large volume of near real-time data into a unified architecture to build a revolutionary digital patient ID and comprehensive patient profile by leveraging Azure Databricks. This architecture powers more than 100 AI models designed to effectively manage the health of large populations, engage consumers, and identify vulnerable individuals needing personalized attention—with a goal of improving members’ well-being and saving lives.
Building your first machine learning model with Azure Databricks