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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.
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By Alexandra Quinn
An MLOps platform enables streamlining and automating the entire ML lifecycle, from model development and training to deployment and monitoring. This helps enhance collaboration between data scientists and developers, bridge technological silos, and ensure efficiency when building and deploying ML models, which brings more ML models to production faster.
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By Alexandra Quinn
Building a smart call center app based on generative AI is a promising solution for improving the customer experience and call center efficiency. But developing this app requires overcoming challenges like scalability, costs and audio quality. By building and orchestrating an ML pipeline with MLRun, which includes steps like transcription, masking PII and analysis, data science teams can use LLMs to analyze audio calls from their call centers. In this blog post, we explain how.
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By Peng Wei
Generative AI has recently emerged as a groundbreaking technology and businesses have been quick to respond. Recognizing its potential to drive innovation, deliver significant ROI and add economic value, business adoption is rapid and widespread. They are not wrong. A research report by Quantum Black, AI by McKinsey, titled "The Economic Potential of Generative AI”, estimates that generative AI could unlock up to $4.4 trillion in annual global productivity.
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By Alexandra Quinn
NLP is a field of AI that enables machines to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. Recently, ChatGPT and similar applications have created a surge in consumer and business interest in NLP. Now, many organizations are trying to incorporate NLP into their offerings.
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By Gilad Shaham
AI has fundamentally changed the way business functions. Adoption of AI has more than doubled in the past five years, with enterprises engaging in increasingly advanced practices to scale and accelerate AI applications to production. As ML models become increasingly complex and integral to critical decision-making processes, ensuring their optimal performance and reliability has become a paramount concern for technology leaders.
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By Alexandra Quinn
The retail industry has been shaped and fundamentally transformed by disruptive technologies in the past decade. From AI assisted customer service experiences to advanced robotics in operations, retailers are pursuing new technologies to address margin strains and rising customer expectations.
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By Alexandra Quinn
MLOps accelerates the ML model deployment process to make it more efficient and scalable. This is done through automation and additional techniques that help streamline the process. Looking to improve your MLOps knowledge and processes? You’ve come to the right place. In this blog post, we detail the steps you need to take to build and run a successful MLOps pipeline.
Generative AI has already had a massive impact on business and society, igniting innovation while delivering ROI and real economic value. According to research by QuantumBlack, AI by McKinsey, titled “The economic potential of generative AI”, generative AI use cases have the potential to add $2.6T to $4.4T annually to the global economy. This potential spans more than 60 use cases across all industries.
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By Alexandra Quinn
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.
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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.
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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.
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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.
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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.
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By Iguazio
A panel featuring Gilad Shaham, Director of Product at Iguazio, Ofer Shemesh ( Ziprecruter ), and Amir Alush ( Visual Layer)
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By Iguazio
Hear from Iguazio's Director of Product Management, Gilad Shaham, as he explains the proven production-first approach for scaling your ML operations.
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By Iguazio
In this session, Jiri shares enterprise secrets to establishing efficient systems for ML/AI and how his team: Watch Jiri and Yaron's fascinating deep dive into HCI’s journey to MLOps efficiency.
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By Iguazio
Watch Julien Simon (Hugging Face), Noah Gift (MLOps Expert) and Aaron Haviv (Iguazio) discuss how you can deploy models into real business environments, serve them continuously at scale, manage their lifecycle in production, and much more in this on-demand webinar!
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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.