AI research company OpenAI is releasing a new machine learning tool that translates the English language into code. The software is called Codex and is designed to speed up the work of professional programmers, as well as help amateurs get started coding.
In demos of Codex, OpenAI shows how the software can be used to build simple websites and rudimentary games using natural language, as well as translate between different programming languages and tackle data science queries. Users type English commands into the software, like “create a webpage with a menu on the side and title at the top,” and Codex translates this into code. The software is far from infallible and takes some patience to operate, but could prove invaluable in making coding faster and more accessible. Read More
Tag Archives: DevOps
Eden AI launches platform to unify ML APIs
Large cloud vendors like Amazon, Google, Microsoft, and IBM offer APIs enterprises can use to take advantage of powerful AI models. But comparing these models — both in terms of performance and cost — can be challenging without thorough planning. Moreover, the siloed nature of the APIs makes it difficult to unify services from different vendors into a single app or workflow without custom engineering work, which can be costly.
These challenges inspired Samy Melaine and Taha Zemmouri to found Eden AI (previously AI-Compare) in 2020. The platform draws on AI APIs from a range of sources to allow companies to mix and match models to suit their use case. Eden AI recently launched what it calls an AI management platform, which the company claims simplifies the use — and integration — of various models for end users. Read More
New toolkit aims to help teams create responsible human-AI experiences
Microsoft has released the Human-AI eXperience (HAX) Toolkit, a set of practical tools to help teams strategically create and responsibly implement best practices when creating artificial intelligence technologies that interact with people.
The toolkit comes as AI-infused products and services, such as virtual assistants, route planners, autocomplete, recommendations and reminders, are becoming increasingly popular and useful for many people. But these applications have the potential to do things that aren’t helpful, like misunderstand a voice command or misinterpret an image. In some cases, AI systems can demonstrate disruptive behaviors or even cause harm. Read More
Machine Learning Engineering for Production (MLOps)
GitHub Copilot: Your AI pair programmer
GitHub Copilot uses OpenAI technology to suggest lines and functions, as well as ways to write tests, and discover new APIs. GitHub says it works bestl for JavaScript, Python, TypeScript, Go, and Ruby. The capability is powered by OpenAI Codex, which was trained on a large concentration of public source code, making it more powerful than GPT-3’s code generator. Read More
#devopsMLOps: 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
NoCodeZ AI
Are Feature Stores The Next Big Thing In Machine Learning?
According to a Gartner study, 85 percent of AI projects will flatline by 2022. Even the most diligent machine learning models may not meet expectations when deployed in an enterprise setting, mainly due to two reasons — inadequate data infrastructure and talent scarcity.
In the machine learning pipeline, search for appropriate data and dataset preparation are among the most time-consuming processes. A data scientist spends around 80 percent of his/her time in managing and preparing data for analysis. The demand-supply gap for qualified data scientists is another pressing challenge.
Enter, feature store. Read More
New AWS tool uses machine learning to watch your services and data for anomalies
AWS has made available Amazon Lookout for Metrics, a service that uses machine learning (ML) to automatically monitor various metrics across business and operational data, detect anomalies and alert the user so they can take appropriate action.
According to AWS, Lookout for Metrics is based on technology used by Amazon itself in business operations, and so reflects 20 years of the firm’s experience in anomaly detection and machine learning. It was built to allow developers to set up autonomous monitoring of important metrics to detect anomalies and identify their root cause in a matter of few clicks. This, AWS claimed, would make it easier to diagnose the root cause of anomalies such as unexpected dips in revenue, high rates of abandoned shopping carts, spikes in payment transaction failures, or increases in new user sign-ups. Read More
Rick's Cafe AI 