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Biomedical information including, scientific literature, regulatory documents, clinical trials, and proprietary research is growing exponentially, and humans are struggling to keep up.

And heres where Causaly helps overcome this informational bottleneck through its sophisticated algorithms that mimic human reading and extract the meanings encoded in language.

Based out of London, Causaly accelerates how humans acquire knowledge and develop insights in Biomedicine. Recently, the company has raised $17 million (approx 12 million) funding in Series A round led by Index Ventures, joined by Marathon, Pentech, and EBRD.

As a part of the funding round, Carlos Gonzalez-Cadenas, partner at Index, joins the board.

With the new funding, Causaly plans to expand its technology team, grow its sales team to allow it to expand into the US, and increase its capacity to work with clients to generate insights from private, proprietary data.

Founded by Artur Saudabayev and Yiannis Kiachopoulos in 2018, Causaly uses machine learning to run deep searches and find answers to complex research questions that would have previously taken weeks months to find with traditional keyword search.

Finding a new drug can take over a decade in research, development, and clinical trials, and requires thousands of experts working together with complex evidence, says Kiachopoulos. Causaly is the first platform to map correlations and relationships within scientific data, allowing researchers and scientists to innovate rather than having to laboriously find the relationships themselves. This means better decisions about which research areas to prioritise and faster learning cycles. Switching from a standard database of documents to Causaly is like going from using a Rolodex of phone numbers to having a smartphone. Its intuitive, interactive, and shows you where you want to go.

Its worth mentioning that Causalys AI reads the entire volume of biomedical literature ever published in seconds.

The companys technology is the fastest way for world-leading researchers to find evidence, explore hidden connections between complex physiological mechanisms, and make new predictions in biomedical science.

At present, the company is working with nine large pharmaceutical companies, including Gilead and Novartis, as well as institutions such as the National Institute of Environmental Health Sciences.

Causalys technology has a wide range of potential applications, including healthcare, cosmetics, consumer goods, and any industry that touches human health.

Causalys platform transforms the biomedical workflow from one of search, read, and synthesise to ask questions and analyse, says Carlos Gonzalez-Cadenas, partner at Index Ventures. Causaly allows researchers to ask extremely complex questions easily, and get results that would have been nearly impossible otherwise. In an era when Covid has reminded us of the significance of biomedical innovation, Causaly is poised to help unleash the potential of new research for the benefit of humanity.

One significant use-case for Causaly is the process of screening for biomarkers that are associated with particular diseases or their relationship to treatment response. This typically takes weeks to months of work and is prone to missing important discoveries due to a large amount of literature to trawl through.

Causalys Clients have shaved off 80% of the time for biomarker screenings and the technology has proposed innovative solutions when identifying possible applications for cancer treatments.

Causaly is the latest example of humans using technology to improve our relationship to knowledge, Kiachopoulos says. After Gutenberg invented the printing press, libraries became a way to categorise all the new information and make it manageable. Then with the digital revolution, libraries moved online, turning into documents and databases. Now we need to move a step beyond static repositories of documents towards much richer, multidimensional and interactive modes of knowledge discovery.

Originally posted here:
UK's machine learning startup Causaly raises 12M to boosts its drug discovery AI - UKTN (UK Technology News

MLOps, also known as DevOps for machine learning, is facing a talent crunch. GitLabs latest survey also shows that developers roles are shifting toward the operations side. Most developers are taking on test and ops tasks, especially around cloud infrastructure and security.

Further, the report found close to 38% of developers now define or create the infrastructure their app runs on, about 13% monitor and respond to that infrastructure, and 26% of developers instrument the code theyve written for production monitoring (up by 18% from last year).

Nearly 43% of the survey respondents have been doing DevOps for three to five years. Thats a sweet spot where they have known success and are well-seasoned, highlighted the report.

MLOps engineering is poised to take off in a big way. We have curated a list of top MLOps learning resources to help you get a handle on the subject.

DeepLearning.AI recently introduced a new specialised source called Machine Learning Engineering for Production (MLOps) Specialisation. The course is currently available on Coursera. Curated by tech evangelists Andrew Ng, Robert Crowe, Laurence Moroney and Cristian Bartolom Armburu, the course will help individuals become machine learning experts and enhance production engineering capabilities.

Heres an overall highlight of the course:

Click here to know more about the course.

Coauthored by Mark Treveil and the team Dataiku, this book covers the following aspects:

The platform offers a one-stop solution to discover, learn and build all things machine learning. It provides a series of lessons around machine learning and MLOps, which includes the basics of applying machine learning to building production-grade applications and products. Goku Mohandas curated the course.

The course covers various aspects of the machine learning pipeline, including data, cost, utility and trust. The courses have been created to educate the community on developing, deploying, and maintaining applications built using ML.

Computer scientist Chip Huyens blog post has summarised all the latest technologies/tools used in MLOps. The complete list is available here.

In this list, there are about 285 MLOps tools. Interestingly, out of 180 startups present in the list, 65 startups had raised funds in 2020, and a large majority of them are still in the data pipeline category. Some of the Indian MLOPs startups mentioned in the list include Playment, Dataturks, Scribble Data and Dockship.

The website offers collective resources for facilitating machine learning operations with GitHub. It gives access to use GitHub for automation, collaboration and reproducibility in machine learning workflows.

The site has blog posts explaining how to GitHub for data science and MLOps; open-source GitHub Actions tool that facilitates MLOps; documents and resources for getting started with MLOps; repository templates, examples and related projects that demonstrate various GitHub features for data science and MLOps; recorded talks, demos and tutorials and more.

The website is a collection of resources to understand MLops, starting from books, newsletters, workflow management, data engineering in MLOps (DataOps), communities, articles, feature stores, model deployment and serving, infrastructure, economics and more.

A complete list of links and resources for MLOps is available on GitHub.

Modelled on a Kubernetes SIG, the MLOps community is an open platform where machine learning enthusiasts, developers and industry professionals collaborate and discuss the best practices around machine learning operations (MLOps or DevOps for ML).

The meeting happens every Wednesday at 5 pm UK time on Zoom. The sessions are recorded and published on the website and Youtube channel.

Amit Raja Naik is a senior writer at Analytics India Magazine, where he dives deep into the latest technology innovations. He is also a professional bass player.

Original post:
7 Best Resources To Learn MLOps In 2021 - Analytics India Magazine

Allied Market Research recently published a report titled, Artificial Intelligence & Advanced Machine Learning Market in BFSI by solution (Smart Wallets and Voice Assisted Banking) and End User (Insurance, Banking, and Capital Markets) Global Opportunity Analysis and Industry Forecast, 2021-2028.

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Artificial Intelligence & Advanced Machine Learning Market Global Sales, Revenue, Price and Gross Margin Forecast To 2030 | Next IT Corp., Prisma...

PR.com2021-05-20

Palo Alto, CA, May 20, 2021 --(PR.com)-- Provectus, an AI-first consultancy and solutions provider, today announced its partnership with Tecton, the enterprise feature store company, with the aim of standardizing and enhancing the machine learning production stack for enterprises.

Provectus and Tecton will closely collaborate in three major areas:

Joint contributions and enhancement of Feast. Provectus will contribute to the next generation of Feast, the leading open-source feature store. While Tecton will remain the major contributor to Feast, Provectuss efforts will be focused on bringing Feast to AWS as well as on core data models and APIs.

Provectus - a partner of choice. Provectus will become a partner of choice for Tecton, providing users of both the Tecton and Feast feature stores with consulting and professional services.

Design and standardization of open-source feature store APIs. Provectus and Tecton, along with users of ML feature stores, will join forces to define common, non-opinionated vendor agnostic APIs for feature stores.

Feature Stores for Machine Learning is a relatively new concept. They constitute the most critical piece in the modern ML production stack. As such, feature stores need to be clearly defined, standardized, and aligned with industry best practices. Provectus and Tectonhave the experience and expertise to accomplish those goals.

When we introduce a feature store to our clients, the platform acts as an essential component that provides a clear path to production for online ML use cases such as Recommendations and Fraud Detection. You will have to build it one way or another, says Stepan Pushkarev, CTO at Provectus.

Tectons founding team previously built the Michelangelo feature store at Uber. Willem Pienaar, who now serves as tech lead at Tecton, pioneered the development of Feast, one of the first open source feature stores. Provectus has deep expertise designing and building feature stores for various enterprise clients.

Strategy-wise, composability, cloud agnosticism, and a multi-cloud approach are fundamental guidelines for the CIOs of modern enterprises. The fact is, machine learning infrastructure is not an exception, says Pushkarev. For Provectus, collaboration with Tecton and Feast is a chance to unite the community towards a better machine learning stack for enterprises.

In the past few years, feature stores have evolved considerably; Provectus, Feast, and Tecton share a common vision of their future, which is outlined in the recent blog posts A State of Feast and Feature Store as a Foundation for ML. A new version of Feast has recently emerged and was announced by Willem Pienaar & Jay Parthasarthy. At the apply() conference, the Provectus team gave a presentation on the roadmap for Feast and AWS integration.

About ProvectusProvectus is an Artificial Intelligence consultancy and solutions provider, helping companies in Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses achieve their objectives through AI. Provectus is headquartered in Palo Alto, CA. For more information, visit provectus.com

About TectonTecton provides an enterprise-ready feature store for machine learning that enables organizations to manage the complete lifecycle of features, from engineering new features to serving them in production for real-time predictions. Tecton is headquartered in San Francisco, CA. For more information, visit tecton.ai

About FeastFeast is an open source feature store for machine learning that helps data scientists and ML engineers bridge the gap between data and machine learning models. It provides the fastest path to production for ML features. For more information, visit feast.dev

Contact Information:ProvectusIryna Ryslyayeva+1-800-950-9840Contact via Emailhttps://provectus.com/

Read the full story here: https://www.pr.com/press-release/836789

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Provectus Announces a New Partnership with Tecton to Collaborate on Feature Store for Machine Learning - IT News Online

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Originally posted here:
Global Machine Learning Market To Power Robustly And To Witness Profitable Growth During The Forecast Period 2020-2026 The Manomet Current - The...