Generative AI (GenAI) engineering refers to the field of engineering that focuses on the development, implementation and optimization of generative AI models. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. By developing GenAI models, engineers can create new and innovative ways to generate content. The vendors in this segment are made up by incumbent and startup vendors covering full-model life cycle management, specifically adjusted to and catering to development, refinement and deployment of generative models (e.g., LLMs) and other GenAI artifacts in production applications. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.
"MLOps and Engineering accelerated with Fast Autoscaling for GPU clusters"
The product has come a long way and is enterprise grade with really innovative features. Fast Autoscaling, fractional GPU usage and a great interface is what makes the product really useful for ML engineering. The customer service is par excellen and the team worked very closely with our internal ML engineers to solve for our models scalability gaps.
"Databricks AI Tools: Expanding AI Capabilities with Innovative Features"
our experience with databricks AI tools has been positive. Tools like AI/BI Genie chatbot provides effective Text-to-SQL conversion for rapid insights , Mosiac AI excels in complex narrative generation and root cause analysis, and its ability to publish models to inference endpoints on web apps add significant value. Databricks assistant enhances workflows with powerful data visualization and code assistance within Notebook. Together, these tools greatly expand our AI capabilities.
"ECK Solutions: Streamlining Enterprise Search Processes"
We use ECK solutions for enterprise search and it has been a great fit.
"NVIDIA DGX : A Deep dive analysis on its hardware and software service capabilities."
High availability time, fast service response time, and excellent hardware response
"Microsoft Copilot Studio : Powerful AI-Powered Automation, Improved Customization"
The tool is highly positive. The tool has transformed daily operations by introducing AI-powered operations automation and insightful data analytics that streamlines tasks across various workflows.Copiolet studio stands out for its reliability and powerful capabilities, adding significant value to productivity and decision making process
"Good supply of Large Language Models"
Great and extensive list of available Large Language Models. With Amazon Bedrock you have access to AWS own LLMS, and Anthropic Claude.
"A bit slow but accurate at least!"
Watson loads a bit slower than I would have liked to see. It grows its capabilities.
"Our GenAI companion in that sector"
Datarobot already prepared itself to embracing GenAI perspective so It ensures strong structure and consulting
"AI Gateway by Portkey: Implementing Essential Fallbacks and Ensuring Uptime"
Portkey has been saving our team a lot of time and effort around AI Observability/Logging, as well as using different AI providers via their Gateway.
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"Shift in MLops Landscape with Domino's Hybrid Approach"
Domino is leading the charge on MLops with their hybrid approach.