Machine Learning

GenAI for Financial Services - MLOps Live #25 with McKinsey

Jan 31, 2024 By Iguazio In Iguazio

Generative AI has sparked the imagination with the explosion of tools like ChatGPT, CodePilot and others, highlighting the importance of LLMs as the basis for modern AI applications. However, implementing GenAI in the enterprise is challenging, and it becomes even more difficult for banks, insurance companies, and other financial services companies. Many Financial Service companies are struggling and end up missing out on the great value of GenAI and the competitive edge it can provide.

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Unlock the Power of Your Marketing Data with Snowflake Connector for Google Analytics

Jan 29, 2024 By Jakub Puchalski In Snowflake

Imagine seamlessly integrating your Google Analytics data with Snowflake, allowing you to combine it effortlessly with other key sources like CRM, ERP, social media metrics, email campaign data, and whatever data sources compose the full scope of your data estate. The good news is that it’s possible with the native Snowflake Connector for Google Analytics, now available in public preview.

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Real-time data processing for machine learning with Striim and BigQuery

Jan 24, 2024 By Simson Chow In Google BigQuery

Learn how to integrate Striim with BigQuery ML for real-time data processing in machine learning.

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Easily Train, Manage, and Deploy Your AI Models With Scalable and Optimized Access to Your Company's AI Compute. Anywhere.

Jan 24, 2024 By ClearML In ClearML

Now you can create and manage your control plane on-prem or on-cloud, regardless of where your data and compute are. We recently announced extensive new orchestration,scheduling, and compute management capabilities for optimizing control of enterprise AI & ML. Machine learning and DevOps practitioners can now fully utilize GPUs for maximal usage with minimal costs.

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Accelerate Your Machine Learning Workflows in Snowflake with Snowpark ML

Jan 23, 2024 By Sandeep Gupta In Snowflake

Many developers and enterprises looking to use machine learning (ML) to generate insights from data get bogged down by operational complexity. We have been making it easier and faster to build and manage ML models with Snowpark ML, the Python library and underlying infrastructure for end-to-end ML workflows in Snowflake.

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Implementing Gen AI in Practice

Jan 22, 2024 By Yaron Haviv In Iguazio

Across the industry, organizations are attempting to find ways to implement generative AI in their business and operations. But doing so requires significant engineering, quality data and overcoming risks. In this blog post, we show all the elements and practices you need to to take to productize LLMs and generative AI. You can watch the full talk this blog post is based on, which took place at ODSC West 2023, here.

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How Sense Uses Iguazio as a Key Component of Their ML Stack

Jan 16, 2024 By Alexandra Quinn In Iguazio

Sense is a talent engagement platform that improves recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers.

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How HR Tech Company Sense Scaled their ML Operations using Iguazio

Jan 16, 2024 By Alexandra Quinn In Iguazio

Sense is a talent engagement company whose platform improves the recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. The AI/Ml team is made up of ML engineers, data scientists and backend product engineers.

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Making BigQuery ML feature preprocessing reusable and modular

Jan 13, 2024 By Mike Henderson In Google BigQuery

BigQuery ML’s approach to feature engineering, modularity, allows for easy reuse of feature pipelines within BigQuery, and portability to Vertex AI.

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Establishing A Framework For Effective Adoption and Deployment of Generative AI Within Your Organization

Dec 20, 2023 By ClearML In ClearML

Adopting and deploying Generative AI within your organization is pivotal to driving innovation and outsmarting the competition while at the same time, creating efficiency, productivity, and sustainable growth. Acknowledging that AI adoption is not a one-size-fits-all process, each organization will have its unique set of use cases, challenges, objectives, and resources.

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