The 2023 Gartner report, A CTO’s Guide to the Generative AI Technology Landscape, recognizes Seldon Technologies as a Sample Vendor. Seldon is listed as a Sample Vendor in the GenAI market.
What is Generative AI?
According to Gartner, “Generative AI (GenAI) learns from previous existing training data like images, videos, or text to generate new content with similar yet unique characteristics. It can generate a wide variety of novel content at scale including: images, music, written text, software code, and product designs.
Generative AI uses a number of techniques that continue to evolve. Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms.”
Today, generative AI most commonly creates content in response to natural language requests–it doesn’t require knowledge of or entering code–but the enterprise use cases are numerous and include innovations in drug and chip design and material science development.”
Why should you be paying attention to GenAI?
There’s a lot of hype flying around about Generative AI and LLMs. Generative AI and LLMs are definitely buzzwords, but it’s clear these are potentially game-changing technologies.
Through leveraging LLMs, organizations can automate typical human tasks accurately, at scale and in a personalized way. However, vendors must also be taking risk and trust in technology incredibly seriously.
What is the GenAI technology Landscape?
The Generative AI Technology Landscape sourced by Gartner
The GenAI technology landscape is made up of four key layers which include applications, engineering tools, models, and infrastructure. As organizations move through different stages of AI maturity, they need different solutions to be able to customize, automate, and govern their applications.
GenAI applications are quickly becoming popular as more and more organizations incorporate GenAI into their operations and offerings. Over 80% of enterprises will have utilized GenAI APIs, models, and/or launched generative AI-powered applications by the year 2026.
What are GenAI Engineering Tools?
GenAI engineering tools help enterprises deploy models quickly while striking the balance between governance and time to market. These tools can be split into two main types: model-centric and data-centric.
When Should Organizations Implement Model Deployment Tools?
MLOps is the set of practices at the intersection of Machine Learning, DevOps, and Data Engineering as sourced by Wikipedia
Enterprises usually start to need dedicated model deployment tools once they reach an intermediate level of AI maturity. Some signs that an enterprise is ready to use model deployment tools are when the number of models, model complexity, and capacity requirements increase while working with a cross-functional team.
It’s also important to consider dedicated model deployment tools when concerns around model governance and reproducibility begin to arise. Enterprises who are at earlier stages of AI maturity, may be able to delay implementing model deployment tools for a while.
But at intermediate maturity, the limitations become apparent quickly, and the benefits of MLOps deployment tools start to outweigh their overhead. The tools then become critical for enterprises looking to scale and advance their AI maturity level.
How Does Seldon Optimize Enterprise Model Deployment?
Seldon enables the orchestration of models and monitoring components at scale
Seldon is at the forefront of revolutionizing model deployment by providing an enterprise platform that optimizes every aspect of the process. The platform simplifies serving and model management, making it easier for organizations to efficiently scale their deployments and manage their ML models.
Whether you’re working in a development or production environment, Seldon provides you with granular control over resource usage so you can meet the unique demands of your machine learning workflows.
By having this level of control, you can not only enhance operational efficiency but also ensure your models are running at their peak performance.
Interested in Using Seldon’s Model Deployment Tools for your GenAI Use Cases?
Essential engineering tools (including those for model deployment) empower you to automate, govern, and maximize the potential of your GenAI models.
Gartner recommends that CTOs “take an objective view of the adequate balance between accuracy, costs, security, and privacy principles and time to value when deploying GenAI models to determine the appropriate model needed. Not all use cases require the largest or the most customized models.”
We’d love to speak with you and provide a live demo of our products for managing and deploying GenAI at scale. Get in touch today to request a walkthrough personalized to your industry and AI maturity.
Gartner, A CTO’s Guide to the Generative AI Technology Landscape, By Arun Chandrasekaran, Radu Miclaus, Eric Goodness, Published 18 September 2023. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.