Microsoft access runtime
Where labeled datasets don’t exist, unsupervised learning - also known as self-supervised learning - can help to fill the gaps in domain knowledge.
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In addition, the API features capabilities for “unsupervised responsible AI,” including tools for understanding dataset imbalance (e.g., whether “sensitive” dataset features like race or gender are over- or under-represented) without the need for labeled training data and explainability dashboards that explain why models make certain predictions - and how to improve the training datasets. With the integration, developers can execute a variety of classical and machine learning models with only a few lines of code.īeyond this, SynapseML introduces new algorithms for personalized recommendation and contextual bandit reinforcement learning using the Vowpal Wabbit framework, an open source machine learning system library originally developed at Yahoo Research. SynapseML also enables developers to use models from different machine learning ecosystems through the Open Neural Network Exchange (ONNX), a framework and runtime co-developed by Microsoft and Facebook. With the HTTP on Spark project, users can embed any web service into their SparkML models and use their Spark clusters for massive networking workflows.” SynapseML also brings new networking capabilities to the Spark ecosystem. SynapseML enables developers to combine frameworks for use cases that require more than one framework, such as search engine creation, while training and evaluating models on resizable clusters of computers.Īs Microsoft explains on the project’s website, SynapseML expands Apache Spark, the open source engine for large-scale data processing, in several new directions: “ allow users to craft powerful and highly-scalable models that span multiple ecosystems. SynapseML aims to address the challenge by unifying existing machine learning frameworks and Microsoft-developed algorithms in an API, usable across Python, R, Scala, and Java. According to Algorithmia’s recent survey, 22% of companies take between one and three months to deploy a model so it can deliver business value, while 18% take over three months. While AI adoption and analytics continue to rise, an estimated 87% of data science projects never make it to production. For starters, composing tools from different ecosystems requires considerable code, and many frameworks aren’t designed with server clusters in mind.ĭespite this, there’s increasing pressure on data science teams to get more machine learning models into use.
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Scaling up AIīuilding machine learning pipelines can be difficult even for the most seasoned developer.
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Developers who use Azure Synapse Analytics will be pleased to learn that SynapseML is now generally available on this service with enterprise support ,” Microsoft software engineer Mark Hamilton wrote in a blog post. “Over the past five years, we have worked to improve and stabilize the SynapseML library for production workloads.