Cloud Hosting

MSPs, Edge Providers, and Large Enterprises will find CloudFabrix's domain-agnostic, distributed, Robotic Data Automation fabric-based AIOps model appealing

PLEASANTON, Calif., Oct. 13, 2022 /PRNewswire/ -- CloudFabrix the inventor of Robotic Data Automation Fabric Platform and the Data-centric AIOps Leader, was named a Leader and Innovator in the 2022 Gigaom Radar for AIOps report for a 2nd consecutive year. The report evaluated 24 major AIOps vendors across Key criteria and evaluation metrics that should be applied when selecting an AIOps solution. The report segregates the 24 vendors across Leaders, Challengers, and New Entrants. CloudFabrix was identified as a Leader, Fast Mover, and Innovator that provides a complete AIOps solution, with a unique Robotic Data Automation Fabric and Distributed AIOps model, including for AIOps at the edge.

According to the report, "This year proved to be one of explosive growth in AIOps tooling and solutions. In some cases, AIOps functionality was achieved by bolting an artificial intelligence and machine learning (AI/ML) engine to existing software, via acquisition or internal development, and marketing it as an AIOps solution. Other vendors built entire platforms around homegrown or acquired AI/ML, jumping into a crowded arena competing with pure AI/ML solutions and platform tools." CloudFabrix's platform is homegrown, built with microservices, is cloud native and can run entirely in the cloud, in a hybrid deployment, or on-premises.

This year's report points out one key differentiation, among the 24 surveyed vendors dividing them into domain-agnostic and platform solutions and what it means for end users. The domain-agnostic solutions can be added to any environment with minimal interruption to the business, while platforms may require the displacement of several existing monitoring solutions.

CloudFabrix scored high ranks across the 3 categories, as identified by the report -

CloudFabrix is among 6 of the 24 vendors identified as Leaders and Fast Movers CloudFabrix's Data-centric AIOps solution is in the domain-agnostic category and integrates well with existing solutions, a business may have.

Story continues

CloudFabrix is among 4 of the 24 vendors identified as Innovation players Each provides a complete AIOps solution with unique capabilities. CloudFabrix, with its Robotic Data Automation Fabric, provides a distributed AIOps model that's unique.

CloudFabrix is among 3 of the 24 vendors identified as AIOps for Edge vendors In the area of emerging technologies, IoT and other edge technologies may require some consideration. A few vendors have explored AIOps for the edge and made strides.

The report identifies CloudFabrix's key capabilities -

Ease of Deployment - CloudFabrix unifies observability, AIOps, and automation within a single SaaS cloud platform (cfxCloud) built on AWS. The platform is also available in AWS Marketplace.

Composable Services - RDAF powers multiple services deployed on top of the AIOps platform, including Log Intelligence, Asset Intelligence, and Service Intelligence.

Continuous ML - Unsupervised and supervised learning are both provided, along with topology detection with data models. Supervised learning is used in the Incident Room to detect possible root causes.

Broad data integration support RDAF is used to integrate data with new sources. In terms of IT operations management (ITOM) and SIEM, log data can be ingested from Splunk and Elasticsearch, and many more.

Log Intelligence - New this year is its Log Intelligence service.

Data Fabric for Edge AI /IoT -The low-latency distributed data fabric allows cfxCloud to ingest, integrate, transform, and load data from or to any system.

Low Code / No Code - users can interact with and operationalize it using a set of more than 800 existing bots and create others via a self-service pipeline.

Service Management - Support is also provided for datastores and data lakes, IT service management (ITSM), configuration management database (CMDB), the collaboration platforms Slack, Microsoft Teams, and Twilio; and Terraform, Ansible, and Chef for automation.

Supporting Quotes

"CloudFabrix continues to impress us with its innovation and its ability. They have shown a pulse on the AIOps market and a quest to constantly improvise in the areas where they were at a disadvantage, across the 2 years we have evaluated them. We are hopeful they continue on this path as Digital Transformation and AIOps are becoming mainstream for enterprises," said Ron Williams, Principal Analyst, Gigaom.

Shailesh Manjrekar, Vice President of AI and Marketing, CloudFabrix said, "We are delighted and honored to be recognized as a leader by Gigaom for 2 consecutive years. This endorses us as a "Fast Mover," demonstrated by our recent launch of "Persona-based Composable Analytics for AIOps." He further asserted, "Our success and scalability are demonstrated by our recent wins with large global MSPs and Enterprises. We continue to strive to delight our customers and make their autonomous enterprise journey, a reality by democratizing Data-First, AI-First, and Automation everywhere strategies."

Resources:

About CloudFabrix

CloudFabrix is the leading Data-centric AIOps Platform vendor and the inventor of Robotic Data Automation Fabric (RDAF). RDAF delivers integrated, enriched and actionable data pipelines to operational and analytical systems. RDAF unifies Observability, AIOps and Automation for Operational Systems and enriches analytical systems. CloudFabrix empowers Business and IT leaders with AI-powered actionable intelligence to make faster and better decisions and accelerate IT planning and Autonomous operations. For more information, visit cloudfabrix.com

Media Contact / Press Enquiry:Shailesh Manjrekar346592@email4pr.com408-421-4214

Cision

View original content to download multimedia:https://www.prnewswire.com/news-releases/cloudfabrix-was-named-a-leader-and-innovator-in-the-2022-gigaom-radar-for-artificial-intelligence-for-operations-aiops-301648587.html

SOURCE CloudFabrix

Go here to see the original:
CloudFabrix Was Named a Leader and Innovator in the 2022 Gigaom Radar for Artificial Intelligence for Operations (AIOPs) - Yahoo Finance

A team of LSU researchers has developed a way to determine which drug therapies work best against an individual's unique type of cancer, possibly providing a way to find cures more quickly and make treatment more affordable.

The interdisciplinary team includes researchers from the School of Veterinary Medicine, College of Science, College of Engineering and the Center for Computation & Technology. It created CancerOmicsNet, a new drug discovery engine run by artificial intelligence.

Using algorithms originally designed to map complex social networks, like those utilized by Facebook, researchers generated three-dimensional graphs of molecular datasets that include cancer cell lines, drug compounds and interactions among proteins inside the human body.

The graphs are then analyzed and interconnected by AI, forming a much clearer picture of how a specific cancer would respond to a specific drug.

Dr. Michal Brylinski, associate professor of computational biology at LSU, said that the team used established datasets to train the CancerOmicsNet engine into using artificial intelligence.

"Once its trained, then you can ask for something that you dont know and this is the input data," he said. "So you ask what inhibitor you think is going to be effective against this cancer and then AI makes a prediction. Thats the implication to unseen data and then something like that goes to a wet lab and we can validate it.

Wet lab research was conducted by researchers at the LSU School of Veterinary Medicine and led by associate professor of research Brent Stanfield.

They developed the AI algorithm and everything, so our role in the study is just to be the practical applications of the technology," Stanfield said. "They developed the algorithm, identified the drugs and then we tested the drugs in our high-capacity systems to demonstrate their efficacy to kill cancer cells.

Researchers studied notoriously aggressive breast, prostate and pancreatic cell lines to train the AI to recognize connections between specific cancers and cancer drugs that control the production of the enzyme kinase within the body.

Kinase acts as a biological catalyst for cell communication and cell growth. Using drugs that lower kinase activity can suppress the growth of cancerous cells.

Brylinski said the research team used CancerOmicsNet to pick out six combinations of cancer cell lines with the drugs likely to be the most toxic to their gene expression profile and tested them, with encouraging results.

According to acceptable criteria, four out of six worked and this success rate is extremely high because if you just picked up six random drugs and say those drugs are going to work on this cancer, then theyre probably not going to work on that cancer," he said. "Four out of six was very encouraging and this is where we stand right now."

Using CancerOmicsNet like molecular speed dating, the AI can help researchers quickly match cancer cell lines with the drugs likely to be the most toxic to their growth and genetic profile.

Brylinski said knowledge gained through CancerOmicsNet can help overcome the challenge of determining how effective a particular kinase-inhibiting drug could be in the future.

The ultimate goal, he said, is to expand their research to potentially apply it in clinical settings.

"If we have a patient with a certain cancer, they can do a biopsy and then they can profile this cancer with respect to gene expression, genetic mutations and everything," Brylinski said. "Then they can input that data to CancerOmicsNet and it can suggest some therapy for this particular cancer and say this drug could be effective and 'another drug could not be effective.'

The effectiveness of various cancer drugs was initially believed to be tied to molecular consistency, the idea that cancer treatment should be targeted to a specific to a location in the body.

Michelle Collins, dean of the College of Nursing and Health at Loyola University New Orleans and a scientist not involved in the LSU research, said CancerOmicsNet is an example of how our current medical understanding of cancer treatment meets advances in genetic studies and AI.

When cancer drugs first came out, they were one size fits all and werent really tailored to the individual and so you see the medications work better on some people than others," she said. "And with the advent of genetics and genomics, which are the future of medicine, were now going to be able to tailor treatments to the patient and not just in oncology.

Collins said she sees CancerOmicsNet being extremely beneficial to oncological studies and treatment in the future.

I think it has the potential to really revolutionize the field of oncology, because well be able to treat people with medication that is more timely tailored to them," she said. "All of that is good if youre a patient with cancer.

Brylinksi said that the ability to treat cancer with a more direct, focused clinical approach makes him excited to see how CancerOmicsNet develops over time.

"I dont know if were going to make some major breakthrough in oncology any time soon, but were contributing pieces where if enough people are doing this, the whole field is moving forward towards the goal of improving human health," he said. "Were very happy that we can make some contribution, which might not be a huge breakthrough down the road, but definitely something that is useful to improving human health and thats really cool actually.

Visit link:
Can artificial intelligence help identify best treatments for cancers? LSU researchers say yes - The Advocate

Artificial intelligence: its one of those mind-bending subjects that provokes fascination yet is misunderstood by almost everyone (myself included). If you bring it up in the pub to sound brainy, the conversation might only go as far as Alan Turing, Deep Blue versus Garry Kasparov and perhaps the new series of BBC drama The Capture, in which foreign spies seem to use AI to manipulate CCTV and live broadcasts.

But AI has long moved beyond the realms of gripping fiction and headline-grabbing experiments into the real world. For instance, DeepMind, a British subsidiary of Alphabet, recently released almost the entire make-up of the protein universe, mapped for the first time by its AI programme AlphaFold. This data is already being used to advance our understanding of life-threatening illnesses and help protect honeybees.

Amid all the scaremongering about AI, perhaps we should be more aware of these positive developments, particularly if were investors. Funding technology that is tangibly solving the worlds trickiest problems surely thats responsible investing at its finest?

Besides, AI applications that drive efficiency, reduce costs, serve vital needs, and cement a companys competitive advantage should generate significant value. Thats not to be sniffed at in this current investing climate.

The Sanlam Artificial Intelligence Fund has specifically hit on AI as a way to back winners in the stock market. Its managers Chris Ford and Tim Day marked the funds fifth anniversary recently by sharing some intriguing insights on how AI has improved in recent times.

Things that people called pipe dreams when we launched the fund in 2017 are now happening, says Chris Ford. What was impossible is now possible.

Examples include Alibabas equivalent scoring better than humans in Stamford Universitys reading and comprehension test, and the Canadian platform Blue Dot spotting the spread of Covid-19 nine days before the World Health Organisation put the alert out.

How has this translated into hard financial returns? The 715.5m fund has had an annualised return of 18.2 per cent since it was established, which is respectable, and its top performers have included household names like Tesla, Netflix, Microsoft and Ocado, alongside lesser-known AI innovators like NVIDIA, Zendesk, and Appen.

The managers even have their own AI application (Orbit) to help them stay across things. Its ongoing charges figure is 0.8 per cent not dirt cheap, but not bad for an active fund.

Do actively managed thematic funds like this have the edge, not just over their cheaper rivals but within the broader investment universe?

Sanlams fund, a traditional open-ended unit trust, has comfortably beaten the MSCI World index, which has had an annualised return of 8.41 per cent over the past five years. By contrast, only 39 per cent of thematic exchange-traded funds (ETFs) have survived and outperformed the index over that same period, according to data from Morningstar.

But the funds performance has markedly dipped in recent times, as the global rout in stock markets hits technology shares particularly hard. This is the downside of thematic investing: clever use of AI may not be the main reason why a company flourishes, or why investors feel well-disposed towards it.

The likes of Tesla, Ocado and Netflix can end up in choppy waters when investing conditions change, and it becomes clear that these firms have been overvalued by the market for what they do. Thematic investing can also lead to a good deal of overlap in your portfolio, with the same names cropping up again and again. This can undermine diversification and add to your risks.

Also, whilst many of the Sanlam holdings are doing great work (like Alphabets DeepMind), there is a good reason why its not classed as socially responsible. EthicsGrade is a ratings agency specialising in understanding the risks stemming from digitalisation, particularly AI, and it has awarded a top A rating to only a handful of firms, including Microsoft and Deutsche Telekom.

Fewer than a third of the 302 major companies it graded have a score of C or above. Too many were unrated altogether because they either ignored or provided little to no details on governance and ethics policies relating to AI.

Experts mostly agree that the more sinister AI possibilities as depicted in The Capture still look far-fetched. But its worrying that many of the major companies we buy from and invest in are being cavalier regarding privacy breaches, data misuse, racial and gender discrimination, the creation of harmful weapons and other potential dangers of AI.

AI is not going anywhere (particularly since the Chinese government is investing heavily in AI research and development). If it proves as beneficial for its first movers as many believe, AI could become the first big success story of so-called thematic investing, previously dismissed as no more than a trendy gimmick that promises more than it can deliver.

The big question is whether this technology will be treated with enough care by its creators and investors. If not, disappointing returns will be the least of our problems.

Originally posted here:
Investing in artificial intelligence: The pros and cons - iNews