The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Generative AI is ubiquitous these days, and organizations are rapidly integrating GenAI into their business processes. However, building GenAI applications comes with its own set of specific challenges. The models are often large, meaning inference costs for running these models can quickly get out of hand, and model selection often requires balancing performance against costs and latency. Other common challenges include the misuse of generative models or data leakage. While implementing measures such as rate limiting, monitoring, and guardrailing in your GenAI applications can help overcome these problems, doing so for every individual project brings significant overhead for your engineering teams. It also becomes easy to lose track of global usage of generative AI within your organization and leads to many cases of reinventing the wheel as teams solve the same problems over and over again.
ML6 presents a recommended CI/CD pattern for promoting ML model code across environments using Amazon SageMaker.
Introduction Digital transformation is changing the manufacturing industry. More and more companies are incorporating digital technologies into their production processes. The benefits and importance of this is undeniable. However, as those technologies are being introduced, it is important to be aware that they can also introduce new security risks to critical infrastructure.
MLOps — bringing AI models to production Developing a machine learning model is often the first step in creating an AI solution, but never the last. In order to make a model actionable, it needs to be served and its predictions delivered to an application. But what if suddenly many users send requests at the same time or when the system becomes unresponsive? And what if the data on which the model was trained is no longer representative of current real-world data, and the performance of the model starts to deteriorate? This is where Machine Learning Operations (MLOps) come in: a combination of tools and processes to create and automatically maintain a robust and up to date AI system.
Introduction The blog post discusses the performance of Automated Machine Learning (AutoML) tools that aim to automate the tasks of applying machine learning to real-world problems without expert knowledge. The blog argues that the tools available today do not deliver upon these promises and uses two different approaches to answer the question "What can you expect from Automated Machine Learning?" The first approach is zooming in on the technical aspects of AutoML, and the second approach is zooming out to show the bigger picture. The technical aspects show that there is no consensus on the exact components that should be automated, and different AutoML tools offer a wide variety of features. The bigger picture shows that AutoML is just a single step in a bigger problem-solving story. Without expertise, humans in this story risk making mistakes at the interfaces. Therefore, AutoML users need at least some expert knowledge to work with AutoML effectively.
Getting your models into production with MLOps
In this blog post , we discuss the recent release of Google Cloud's Vertex AI platform, a unified platform for building and deploying machine learning models. As we believe Google Cloud's tool, Vertex AI is a game-changer for machine learning practitioners, as it simplifies the process of building, deploying, and managing machine learning models. The platform offers a wide range of tools and features, including AutoML, which allows users to create custom models without any coding, and a streamlined user interface for managing workflows.
The blog post explains how to deploy large-scale transformer models efficiently in production using the Triton inference server. The post discusses the challenges associated with deploying transformer models and the benefits of using Triton for deployment. It also describes the ensemble modeling technique and how it can be used to improve the performance of transformer models in production.