Iguazio

Herzliya, Israel
2014
  |  By Guy Lecker
GPU as a Service (GPUaaS) serves as a cost-effective solution for organizations who need more GPUs for their ML and gen AI operations. By optimizing the use of existing resources, GPUaaS allows organizations to build and deploy their applications, without waiting for new hardware. In this blog post, we explain how GPUaaS as a service works, how it can close the GPU shortage gap, when to use GPUaaS and how it fits with gen AI.
  |  By Alexandra Quinn
The manufacturing industry can benefit from AI, data and machine learning to advance manufacturing quality and productivity, minimize waste and reduce costs. With ML, manufacturers can modernize their businesses through use cases like forecasting demand, optimizing scheduling, preventing malfunctioning and managing quality. These all significantly contribute to bottom line improvement.
  |  By Alexandra Quinn
Gen AI is quickly reshaping industries, and the pace of innovation is incredible to witness. The introduction of ChatGPT, Microsoft Copilot, Midjourney, Stable Diffusion and many more incredible tools have opened up new possibilities we couldn’t have imagined 18 months ago. While building gen AI application pilots is fairly straightforward, scaling them to production-ready, customer-facing implementations is a novel challenge for enterprises, and especially for the financial services sector.
  |  By Alexandra Quinn
Financial services companies are leveraging data and machine learning to mitigate risks like fraud and cyber threats and to provide a modern customer experience. By following these measures, they are able to comply with regulations, optimize their trading and answer their customers’ needs. In today’s competitive digital world, these changes are essential for ensuring their relevance and efficiency.
  |  By Alexandra Quinn
Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. But a successful deployment of LLMs has to go beyond prototyping, which is where LLMOps comes into play. LLMOps is MLOps for LLMs. It’s about ensuring rapid, streamlined, automated and ethical deployment of LLMs to production. This blog post delves into the concepts of LLMOps and MLOps, explaining how and when to use each one.
  |  By Yaron Haviv
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.
  |  By Alexandra Quinn
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.
  |  By Alexandra Quinn
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.
  |  By Yaron Haviv
2023 was the year of generative AI, with applications like ChatGPT, Bard and others becoming so mainstream we almost forgot what it was like to live in a world without them. Yet despite its seemingly revolutionary capabilities, it's important to remember that Generative AI is an extension of “traditional AI”, which in itself is a step in the digital transformation revolution.
  |  By Alexandra Quinn
Successfully training AI and ML models relies not only on large quantities of data, but also on the quality of their annotations. Data annotation accuracy directly impacts the accuracy of a model and the reliability of its predictions. This is where human-annotated datasets come into play. Human-annotated datasets offer a level of precision, nuance, and contextual understanding that automated methods struggle to match.
  |  By Iguazio
In this session, Yaron Haviv, CTO Iguazio was joined by Ehud Barnea, PHD, Head of AI at Tasq.ai and Guy Lecker ML Engineering Team Lead, Iguazio to discuss how to validate, evaluate and fine tune an LLM effectively. They shared firsthand tips of how to solve the production hurdle of LLM evaluation, improving LLM performance, eliminating risks, along with a live demo of a fashion chatbot that leverages fine-tuning to significantly improve the model responses.
  |  By Iguazio
Iguazio would like to introduce two practical demonstrations showcasing our call center analysis tool and our innovative GenAI assistant. These demos illustrate how our GenAI assistant supports call center agents with real-time advice and recommendations during customer calls. This technology aims to improve customer interactions and boost call center efficiency. We're eager to share how our solutions can transform call center operations.
  |  By Iguazio
Many enterprises operate expansive call centers, employing thousands of representatives who provide support and consult with clients, often spanning various time zones and languages. However, the successful implementation of a gen AI-driven smart call center analysis applications presents unique challenges such as data privacy controls, potential biases, AI hallucinations, language translation and more.
  |  By Iguazio
Nuclio is a high-performance serverless framework focused on data, I/O, and compute intensive workloads. It is well integrated with popular data science tools, such as Jupyter and Kubeflow; supports a variety of data and streaming sources; and supports execution over CPUs and GPUs. The Nuclio project began in 2017 and is constantly and rapidly evolving; many start-ups and enterprises are now using Nuclio in production. In this video, Tomer takes you through a quick demo of Nuclio, triggering functions both from the UI and the CLI.
  |  By 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.
  |  By Iguazio
In this MLOps Live session, Gennaro, Head of Artificial Intelligence and Machine Learning at Sense, describe how he and his team built and perfected the Sense chatbot, what their ML pipeline looks like behind the scenes, and how they have overcome complex challenges such as building a complex natural language processing ( NLP) serving pipeline with custom model ensembles, tracking question-to-question context, and enabling candidate matching.
  |  By Iguazio
In this session, Yaron Haviv, CTO Iguazio was joined by Nayur Khan, Partner, QuantumBlack, AI by @McKinsey and Mara Pometti​, Associate Design Director, McKinsey & Company to discuss how enterprises can adopt GenAI now in live business applications. There was a very engaging Q&A session with many relatable questions asked.
  |  By Iguazio
The influx of new tools like ChatGPT spark the imagination and highlight the importance of Generative AI and foundation models as the basis for modern AI applications. However, the rise of generative AI also brings a new set of MLOps 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 9 minute demo video, we share MLOps orchestration best practices and explore open source technologies available to help tackle these challenges.
  |  By 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.

The Iguazio Data Science Platform automates MLOps with end-to-end machine learning pipelines, transforming AI projects into real-world business outcomes. It accelerates the development, deployment and management of AI applications at scale, enabling data scientists to focus on delivering better, more accurate and more powerful solutions instead of spending their time on infrastructure.

The platform is open and deployable anywhere - multi-cloud, on prem or edge. Iguazio powers real-time data science applications for financial services, gaming, ad-tech, manufacturing, smart mobility and telecoms.

Dive Into the Machine Learning Pipeline:

  • Collect and Enrich Data from Any Source: Ingest in real-time multi-model data at scale, including event-driven streaming, time series, NoSQL, SQL and files.
  • Prepare Online and Offline Data at Scale: Explore and manipulate online and offline data at scale, powered by Iguazio's real-time data layer and using your favorite data science and analytics frameworks, already pre-installed in the platform.
  • Accelerate and Automate Model Training: Continuously train models in a production-like environment, dynamically scaling GPUs and managed machine learning frameworks.
  • Deploy in Seconds: Deploy models and APIs from a Jupyter notebook or IDE to production in just a few clicks and continuously monitor model performance.

Bring Your Data Science to Life.