Evaluating ML model performance is essential for ensuring the reliability, quality, accuracy and effectiveness of your ML models. In this blog post, we dive into all aspects of ML model performance: which metrics to use to measure performance, best practices that can help and where MLOps fits in.

AutoML (Automated Machine Learning) helps organizations deploy Machine Learning (ML) models faster, by making the ML pipeline process more efficient and less error-prone. If you’re getting started with AutoML, this article will take you through the first steps you need to find a tool and get started. If you’re at an advanced stage, it will help you validate you’re on the right track.

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.

Deploying trained models takes models from the lab to live environments and ensures they meet business requirements and drive value. Model deployment can bring great value to organizations, but it is not a simple process, as it involves many phases, stakeholders and different technologies. In this article, we provide recommendations for data professionals who want to improve and streamline their model deployment process.

The open source ML tooling ecosystem has become vast in the last few years, with many tools both overlapping in their capabilities as well as complimenting each other nicely. In part because AI/ML is a still-immature practice, the messaging around what all these tools can accomplish can be quite vague. In this article, we’ll dive into three tools to better understand their capabilities, and how they fit into the ML lifecycle.

Seagate is the world’s leading data storage solution. Together with Iguazio, Seagate is able to manage data engineering at scale while harnessing petabytes of data, efficiently utilize resources, bridge the gap between data engineering and data science and create one production-ready environment with enterprise capabilities. In this new webinar, Vamsi Paladugu, Sr.

8 years ago, when I founded Iguazio together with my co-founders Yaron Haviv, Yaron Segev & Orit Nissan-Messing, I never thought I would be making this announcement on our company blog: McKinsey acquired Iguazio! When we first embarked on this journey, we realized that while AI has the ability to transform any industry - from banking to retail to manufacturing - in reality most data science projects fail.

Enterprises who are actively increasing their AI maturity in a bid to achieve business transformations often find that with increased maturity comes increased complexity. For use cases that require very large datasets, the tech stacks required to meet business needs quickly become unwieldy.

In 2022, AI and ML came into the mainstream consciousness, with generative AI applications like Dall-E and GPT AI becoming massively popular among the general public, and ethical questions of AI usage stirring up impassioned public debate. No longer a side project for forward-thinking businesses or CEOs that find it intriguing, AI and ML are now moving towards the center of the business.

The IDC MarketScape: Worldwide Machine Learning Operations Platforms 2022 Vendor Assessment is an annual study that evaluates technology vendors based on a comprehensive framework. It provides an in-depth quantitative and qualitative assessment of MLOps solution vendors in a long-form research report, to help buyers make important technology decisions that will create long term business success.