We propose Expert Monitoring, an approach that leverages domain expertise to enhance the detection and mitigation of concept drift in machine learning (ML) models. Our approach supports practitioners by consolidating domain expertise related to concept drift-inducing events, making this expertise accessible to on-call personnel, and enabling automatic adaptability with expert oversight.

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search "unreasonable" local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and "reasonable" model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable.

The past few years have seen an explosion in the use of artificial intelligence on embedded and edge devices. Starting with the keyword spotting models that wake up the digital assistants built into every modern cellphone, "edge AI" products have made major inroads into our homes, wearable devices, and industrial settings. They represent the application of machine learning to a new computational context. ML practitioners are the champions at building datasets, experimenting with different model architectures, and building best-in-class models. ML experts also understand the potential of machine learning to transform the way that humans and technology work together.

Most notably, text-to-image models (AI art) became extremely popular. Search engines were swapped for sophisticated chatbots such as ChatGPT. With open-source alternatives such as PaLM RLHF on the horizon, AI and machine learning will become more accessible to neophyte developers. However, becoming a true machine learning engineer requires more skill than just scripting or coding. As such, more people are beginning to consider it as a potential career path.

AI/ML Job: ML Engineer ML Engineer at Nobias Remote › Worldwide, 100% remote position (Posted Feb 18 2023) Please mention that you found the job at Jobhunt.ai Apply now! Job description Calling all ML engineers, computational biologists, and medicinal or computational chemists Nobias is a new pharma startup using a variety of AI tools to speed drug development. We are an early stage biotech using large repositories of biological data, cutting edge deep learning techniques, automated reasoning, and computational biochemistry to discover and develop new medicines, diagnostics, and insights for a variety of diseases. We have a world-class team, and are looking for the best people at the frontiers of technology and biology. If you have strong programming chops, a background in ML, and deep knowledge of biology or computational biology/chemistry, we would love to talk to you.

Moving across the typical machine learning lifecycle can be a nightmare. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. Supporting the operations of data scientists and ML engineers requires you to reduce--or eliminate--the engineering overhead of building, deploying, and maintaining high-performance models. To do that, you'd need to take a systematic approach to MLOps--enter platforms! Machine learning platforms are increasingly looking to be the "fix" to successfully consolidate all the components of MLOps from development to production. Not only does the platform give your team the tools and infrastructure they need to build and operate models at scale, but it also applies standard engineering and MLOps principles to all use cases. But here's the catch: understanding what makes a platform successful and building it is no easy feat.

Seldon, a London-based data-centric machine learning operations (MLOps) platform, has secured a $20M Series B funding round led by new Portuguese investor Bright Pixel (former Sonae IM) with participation from existing investors AlbionVC (backed Ophelos), Cambridge Innovation Capital, and Amadeus Capital Partners. The funding will help Seldon expand its machine learning product's market fit and unlock enterprise-ready solutions based on open source. "AI is in everything, and Seldon is uniquely positioned to ensure a return on ML investment by providing robust, scalable, and secure infrastructure, pioneering a data-centric approach to ML pipelines, prioritizing team collaboration across the organization, and making sure teams can solve meaningful problems at scale by building trust in machine learning, even under the most intense regulatory conditions. "We're excited to bring together new investor Bright Pixel Capital and our existing partners, who believe in our vision and can help us become the trusted MLOps partner of any organization worldwide." Currently, numerous companies are investing a lot of resources into artificial intelligence, but they are having difficulty expanding their models for practical use. This is due to bottlenecks in team workflows, increased regulation and compliance restraints, a lack of trust in model outputs, and ensuring peak model performance are all top of mind for AI-powered enterprises. Here's where Seldon helps Data Scientists, ML Engineers, and other stakeholders in the company to quickly and efficiently adopt machine learning to address these challenges. Founded in 2014, Seldon is a data science and machine learning operations platform that aims to empower Data Scientists, ML Engineers, and MLOps teams to deploy, monitor, explain, and manage their ML models. With Seldon, organisations can minimise risk and drastically cut down time-to-value from their models. The UK company offers both an open-source framework, "Core," which focuses on model deployment, and an enterprise product, "Deploy Advanced," which builds on this functionality to power model monitoring, explainability, and management. Seldon claims that it has achieved a 400% YoY growth rate in its open-source frameworks installed and running since its series A in November 2020. "Seldon has differentiated itself by presenting a unique solution that can reduce the friction for users deploying and explaining ML models across any industry.

As the field of machine learning (ML) continues to evolve and its impact on society and various aspects of our lives grows, it is becoming increasingly important for practitioners and innovators to consider a broader range of perspectives when building ML models and applications. This desire is driving the need for a more flexible and scalable ML infrastructure. At Spotify, we strongly believe in a diverse and collaborative approach to building ML applications. Gone are the days when ML was the domain of only a small group of researchers and engineers. We want to democratize our ML efforts such that contributors of all backgrounds, including engineers, data scientists, and researchers, can leverage their unique perspectives, skills, and expertise to further ML at Spotify.

After gaining Python and Machine Learning knowledge, it's time to practice. And for that, you need to use Data Science tools like Jupyter and Anaconda. Spend your few hours and play with these tools. Understand what they're for and why you should use them.

Companies hire data and machine-learning professionals to help them with cutting-edge ML models. They spend often 80% of their time cleaning or dealing with data that is riddled with missing values, outliers, large load times, and a constantly changing schema. It is not uncommon for people to be far from their expectations. Data scientists may initially be enthusiastic to work on advanced models and insights, but this enthusiasm quickly fades amid daily schema changes, tables that stop updating, and other surprises that silently ruin models and dashboards. Although "data science" can be applied to many roles, such as product analytics or putting statistical models into production, there is one thing that is always true: data scientists, ML engineers, and data analysts often sit at the tail of the data pipeline.