Deploying Gen AI in Production with NVIDIA NIM & MLRun - MLOps Live #33 with NVIDIA
In this webinar, we explored how to successfully deploy your Gen AI applications while mitigating these challenges, using NVIDIA NIM and MLRun.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Machine Learning
MLRun 1.7: LLM Monitoring & Fine-tuning a Gen AI Banking Chatbot
See how to use MLRun 1.7 to fine-tune a generative AI banking chatbot, ensuring it answers only relevant banking inquiries. Watch the full tutorial and follow along!
Why Monitoring Matters to ML Data Intelligence in Databricks
Machine learning operations (MLOps) is a practice that focuses on the operationalization of machine learning models. It involves automating and streamlining the lifecycle of ML models, from development and training to deployment and monitoring. Much like data operations (DataOps), MLOps aims to improve the speed and accuracy of the data you’re accessing and analyzing.
Gen AI for Marketing - From Hype to Implementation
Gen AI has the potential to bring immense value for marketing use cases, from content creation to hyper-personalization to product insights, and many more. But if you’re struggling to scale and operationalize gen AI, you’re not alone. That’s where most enterprises struggle. To date, many companies are still in the excitement and exploitation phase of gen AI. Few have a number of initial pilots deployed and even fewer have simultaneous pilots and are building differentiating use cases.
The Impact of AI and Machine Learning In Quality Assurance
Some of the popular AI tools people and corporations are using now include ChatGPT, Google Gemini, and Microsoft Copilot. This has resulted in higher usage and adoption of this technology and this has caused some worry among people, particularly in terms of employment. However, for software testers, these changes should be seen as a chance to improve rather than a threat.
Build and Manage ML Features for Production-Grade Pipelines with Snowflake Feature Store
When scaling data science and ML workloads, organizations frequently encounter challenges in building large, robust production ML pipelines. Common issues include redundant efforts between development and production teams, as well as inconsistencies between the features used in training and those in the serving stack, which can lead to decreased performance. Many teams turn to feature stores to create a centralized repository that maintains a consistent and up-to-date set of ML features.
How ClearML Stacks Up Against Alternate Solutions - Weights & Biases
At first glance, ClearML’s AI Development Center and alternatives such as Weights & Biases seem to offer similar capabilities for MLOps. For example, both solutions support experiment management, data management, and orchestration. However, each product is designed to solve a different use case. It is important to understand how these approaches affect the user experience.
The Cloud Exit: Cost, Security, and Performance Driving the Move Back to On-Premises
The last decade has seen a giant shift by organizations into the cloud for software, storage, and compute, resulting in business benefits ranging from flexibility and lower up-front costs to easier maintenance. But lately we have seen more and more companies re-evaluating their cloud strategies and opting to move their data back to on-premises infrastructure due to several key factors.
Customer Facing AI Agent
This demo recorded during our MLOps Live Webinar #32 showcases a customer-facing AI agent developed for a jewelry retailer. This can be used as a marketing tool to offer personalized product recommendations and purchasing information and support.
Gen AI for Marketing - From Hype to Implementation - MLOps Live #32 with McKinsey and Iguazio
In this MLOps Live session we were joined by Eli Stein, Partner and Modern Marketing Capabilities Leader at McKinsey, to delve into how data scientists can leverage generative AI to support the company’s marketing strategy. We showcased a live demo of a customer-facing AI agent developed for a jewelry retailer, which can be used as a marketing tool to offer personalized product recommendations and purchasing information and support. Following the demo, we held an interactive discussion and Q&A session. Enjoy!