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Machine Learning

honeybadger

Machine-learning life-cycle management using MLflow

May 25, 2023 By Aditya Raj In Honeybadger

Are you looking to streamline your machine learning projects from start to finish? Look no further than MLflow! In this blog, we'll discuss how to use MLflow to manage the entire lifecycle of your ML project – from creating and training models to comparing results and deploying them.

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ODSC East 2023 MLOps Keynote: MLOps in the Era of Generative AI

May 22, 2023 By Iguazio In Iguazio
ChatGPT sparks the imagination and highlights the importance of Generative AI and foundation models as the basis for modern AI applications. However, this also brings a new set of AI operationalization challenges. Challenges like handling massive amounts of data, large scale computation and memory, complex pipelines, transfer learning, extensive testing, monitoring, and so on. In this talk, we explore the new technologies and share MLOps orchestration best practices that will enable you to automate the continuous integration and deployment (CI/CD) of foundation models and transformers, along with the application logic, in production.
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Qlik's Fully Integrated AI and ML Capabilities - Today and the Path Forward

May 18, 2023 By Qlik In Qlik
In this first video, we introduce our overall approach, discuss what makes it unique, and describe our end-to-end capabilities and benefits that drive deeper and more meaningful insight, make that insight more accessible to all decision-makers, and speed up the time-to-value through automation. Unlike other solutions, Qlik offers fully integrated AI and ML built into our platform as foundational services, not a “bolt-on” or one-size-fits-all approach. We deliver a complete set of augmented capabilities, where we can “cross-pollenate” by leveraging ML-driven capabilities in Qlik Sense and Qlik Sense analytics in AutoML.
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iguazio

Integrating MLOps with MLRun and Databricks

May 15, 2023 By Xingsheng Qian In Iguazio
Every organization aiming to bring AI to the center of their business and processes strives to shorten machine learning development cycles. Even data science teams with robust MLOps practices struggle with an ecosystem that is in a constant state of change and infrastructure that is itself evolving. Of course, no single MLOps stack works for every use case or team, and the scope of individual tools and platforms vary greatly.
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Run predictions with your data using Cloudera Machine Learning

May 15, 2023 By Cloudera In Cloudera
Cloudera Machine Learning offers a comprehensive solution for the full machine learning lifecycle, from finding and exploring data to hosting custom ML web apps. With nearly 700 enterprise customers using ML in production, it provides a trusted and efficient way to deploy complex models and support a diverse range of AI use cases.
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