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
Tag Archives: DevOps
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
A Chat with Andrew on MLOps: From Model-centric to Data-centric AI
DIB Guide: Detecting Agile BS
Agile is a buzzword of software development, and so all DoD software development projects are, almost by default, now declared to be “agile.” The purpose of this document is to provide guidance to DoD program executives and acquisition professionals on how to detect software projects that are really using agile development versus those that are simply waterfall or spiral development in agile clothing (“agile-scrum-fall”). Read More
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
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