In the ever-evolving landscape of Artificial Intelligence (AI), the development and deployment of Large Language Models (LLMs) have become pivotal in shaping intelligent applications across various domains. However, realizing this potential requires a rigorous and systematic evaluation process. Before delving into the metrics and challenges associated with evaluating LLM systems, let’s pause for a moment to consider the current approach to evaluation. Does your evaluation process resemble the repetitive loop of running LLM applications on a list of prompts, manually inspecting outputs, and attempting to gauge quality based on each input? If so, it’s time to recognize that evaluation is not a one-time endeavor but a multi-step, iterative process that has a significant impact on the performance and longevity of your LLM application. With the rise of LLMOps (an extension of MLOps tailored for Large Language Models), the integration of CI/CE/CD (Continuous Integration/Continuous Evaluation/Continuous Deployment) has become indispensable for effectively overseeing the lifecycle of applications powered by LLMs. — Read More
#mlopsTag Archives: MLOps
Beyond Jupyter Notebooks: MLOps Environment Setup & First Deployment
Why it’s time for “data-centric artificial intelligence”
Machine learning pioneer Andrew Ng argues that focusing on the quality of data fueling AI systems will help unlock its full power.
The last 10 years have brought tremendous growth in artificial intelligence. Consumer internet companies have gathered vast amounts of data, which has been used to train powerful machine learning programs. Machine learning algorithms are widely available for many commercial applications, and some are open source.
Now it’s time to focus on the data that fuels these systems, according to AI pioneer Andrew Ng, SM ’98, the founder of the Google Brain research lab, co-founder of Coursera, and former chief scientist at Baidu.
Ng advocates for “data-centric AI,” which he describes as “the discipline of systematically engineering the data needed to build a successful AI system.” Read More
LandingLens for Machine Vision
The LandingLens platform includes a wide array of features to help teams develop and deploy reliable and repeatable inspection systems utilizing deep learning technology for a wide range of tasks in a production environment. We describe this software tool as a composition of three modules: Data, Model, and Deployment. With a data-centric approach throughout, LandingLens manages data, accelerates troubleshooting, and scales to deployment. Read More
Paper
A Tour of End-to-End Machine Learning Platforms
Machine Learning (ML) is known as the high-interest credit card of technical debt. It is relatively easy to get started with a model that is good enough for a particular business problem, but to make that model work in a production environment that scales and can deal with messy, changing data semantics and relationships, and evolving schemas in an automated and reliable fashion, that is another matter altogether. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!
As little as 5% of the actual code for machine learning production systems is the model itself. What turns a collection of machine learning solutions into an end-to-end machine learning platform is an architecture that embraces technologies designed to speed up modelling, automate the deployment, and ensure scalability and reliability in production. I talked about lean D/MLOps, data and machine learning operations, before, because machine learning operations without data is pointless, so an end-to-end machine learning platform needs a holistic approach. Read More
Machine Learning Engineering for Production (MLOps)
MLOps: Comprehensive Beginner’s Guide
MLOps, AIOps, DataOps, ModelOps, and even DLOps. Are these buzzwords hitting your newsfeed? Yes or no, it is high time to get tuned for the latest updates in AI-powered business practices. Machine Learning Model Operationalization Management (MLOps) is a way to eliminate pain in the neck during the development process and delivering ML-powered software easier, not to mention the relieving of every team member’s life.
Let’s check if we are still on the same page while using principal terms. Disclaimer: DLOps is not about IT Operations for deep learning; while people continue googling this abbreviation, it has nothing to do with MLOps at all. Next, AIOps, the term coined by Gartner in 2017, refers to the applying cognitive computing of AI & ML for optimizing IT Operations. Finally, DataOps and ModelOps stand for managing datasets and models and are part of the overall MLOps triple infinity chain Data-Model-Code.
While MLOps seems to be the ML plus DevOps principle at first glance, it still has its peculiarities to digest. We prepared this blog to provide you with a detailed overview of the MLOps practices and developed a list of the actionable steps to implement them into any team. Read More
A Chat with Andrew on MLOps: From Model-centric to Data-centric AI
How MLOps Can Help Get AI Projects to Deployment
Did you know that most AI projects never get fully deployed? In fact, a recent survey by NewVantage Partners revealed that only 15% of leading enterprises have gotten any AI into production at all. Unfortunately, many models get built and trained, but never make it to business scenarios where they can provide insights and value. This gap – deemed the production gap – leaves models unable to be used, wastes resources and stops AI ROI in its tracks. But it’s not the technology that is holding things back. In most cases, the barriers to businesses and organizations becoming data-driven can be reduced to three things: people, process and culture. So, the question is, how can we overcome these challenges and start getting real value from AI? To overcome this production gap and finally get ROI from their AI, enterprises must consider formalizing an MLOps strategy.
MLOps, or machine learning operations, refers to the culmination of people, processes, practices and underpinning technologies that automate the deployment, monitoring and management of machine learning models into production in a scalable, fully governed way. Read More
Creating End-to-End MLOps pipelines using Azure ML and Azure Pipelines
In this 7-part series of posts we’ll be creating a minimal, repeatable MLOps Pipeline using Azure ML and Azure Pipelines.
The git repository that accompanies these posts can be found here. Read More
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