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Cloud Native Computing Foundation Reaffirms Istio Maturity with … – PR Newswire

July 16, 2023 | Author: admin

Fast-growing service mesh has added end users, events, features, and maintainers to reach Foundation's top maturity level

SAN FRANCISCO, July 12, 2023 /PRNewswire/ --The Cloud Native Computing Foundation (CNCF), which builds sustainable ecosystems for cloud native software, today announced the graduation of Istio. Istio is an open source service mesh that provides a uniform and efficient way to secure, connect, and monitor services in cloud native applications.

Istio provides zero-trust networking, policy enforcement, traffic management, load balancing, and monitoring without requiring applications to be rewritten. It pioneered the modern service mesh pattern security, traffic routing and observability using sidecar containers when it launched in 2017. In 2022, the project continued to drive innovation in the space by introducing a complementary architecture, ambient mesh offering the same benefits without needing sidecars.

"Today, the Istio project takes its place alongside the projects that enable it and upon which it is built, including Kubernetes, Envoy, Prometheus, and SPIFFE," said Craig Box, Istio Steering Committee member and VP of Open Source and Community at ARMO. "On behalf of the project's leadership, we wish to thank every contributor, both corporate and individual, who have collectively brought us to graduation within the CNCF."

Istio was initially developed by Google and IBM and built on the Envoy project from Lyft. The project now has maintainers from more than 16 companies, including many of the largest networking vendors and cloud organizations worldwide. End users range from digital native startups to the world's largest financial institutions and telcos, with case studies from companies including eBay, T-Mobile, Airbnb and Salesforce.com. Istio is the third most active CNCF project in terms of the number of PRs opened and merged.

This year, the Istio community welcomed the maintainers of the Open Service Mesh project, with the team from Microsoft becoming Istio contributors. The combined group continues to drive the development of the Kubernetes Gateway API, which traces its lineage directly to Istio's traffic management model.

The inaugural Istio Day at KubeCon + CloudNativeCon Europe 2023, the first Istio event arranged by CNCF, was the second best attended of all the co-located events. As a result, the program will be a full-day event on November 6th during the upcoming KubeCon + CloudNativeCon North America 2023. The CFP is open through August 6, 2023.

The two IstioCon events have attracted an audience of more than 4,000 end users, developers and maintainers. A third annual IstioCon will be hosted by CNCF on the 25th and 26th of September, 2023. A full day of in-person content in Chinese will be offered alongside KubeCon + CloudNativeCon + Open Source Summit China in Shanghai, with two days of virtual content in English for the worldwide Istio audience. The CFP is open for sessions in Chinese and English through July 23, 2023.

"Service mesh adoption has been steadily rising over the past few years as cloud native adoption has matured across industries," said Chris Aniszczyk, CTO of CNCF. "Istio has helped drive part of this maturation, and the project has progressed quickly since joining CNCF late last year. We look forward to watching and supporting this continued growth as the Istio team adds new features and simplifies the service mesh experience."

As a CNCF graduated project, Istio joins the ranks of respected technologies that have proven their value and viability in the cloud native ecosystem. Graduation validates Istio's commitment to openness, collaboration, and innovation.

Quotes from maintainers and supporting organizations are available here.

To learn more about Istio:

About Cloud Native Computing Foundation

Cloud native computing empowers organizations to build and run scalable applications with an open source software stack in public, private, and hybrid clouds. The Cloud Native Computing Foundation (CNCF) hosts critical components of the global technology infrastructure, including Kubernetes, Prometheus, and Envoy. CNCF brings together the industry's top developers, end users, and vendors and runs the largest open source developer conferences in the world. Supported by more than 800 members, including the world's largest cloud computing and software companies, as well as over 200 innovative startups, CNCF is part of the nonprofit Linux Foundation. For more information, please visit http://www.cncf.io.

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see our trademarkusage page. Linux is a registered trademark of Linus Torvalds.

Media ContactKatie MeindersThe Linux Foundation[emailprotected]

SOURCE Cloud Native Computing Foundation

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Data Global Center Blade Server Market to Reach $33.5 Billion by … – GlobeNewswire

July 16, 2023 | Author: admin

Dublin, July 13, 2023 (GLOBE NEWSWIRE) -- The "Data Center Blade Server: Global Strategic Business Report" report has been added to ResearchAndMarkets.com's offering.

The global market for Data Center Blade Server estimated at US$17.2 Billion in the year 2022, is projected to reach a revised size of US$33.5 Billion by 2030, growing at a CAGR of 8.7% over the analysis period 2022-2030.

Tier 1, one of the segments analyzed in the report, is projected to record a 7.9% CAGR and reach US$5.8 Billion by the end of the analysis period.

Taking into account the ongoing post pandemic recovery, growth in the Tier 2 segment is readjusted to a revised 9.7% CAGR for the next 8-year period.

The U.S. Market is Estimated at $5.9 Billion, While China is Forecast to Grow at 10.1% CAGR

The Data Center Blade Server market in the U.S. is estimated at US$5.9 Billion in the year 2022. China, the world's second largest economy, is forecast to reach a projected market size of US$3.5 Billion by the year 2030 trailing a CAGR of 10.1% over the analysis period 2022 to 2030.

Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at 7.5% and 8% respectively over the 2022-2030 period. Within Europe, Germany is forecast to grow at approximately 8.2% CAGR. Led by countries such as Australia, India, and South Korea, the market in Asia-Pacific is forecast to reach US$7 Billion by the year 2030.

Select Competitors (Total 11 Featured) -

Key Attributes:

Key Topics Covered:

I. METHODOLOGY

II. EXECUTIVE SUMMARY

1. MARKET OVERVIEW

2. FOCUS ON SELECT PLAYERS

3. MARKET TRENDS & DRIVERS

4. GLOBAL MARKET PERSPECTIVE

III. MARKET ANALYSIS

IV. COMPETITION

For more information about this report visit https://www.researchandmarkets.com/r/9z9ohy

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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The new high-paying jobs in generative AI – InfoWorld

July 16, 2023 | Author: admin

I predicted that cloud providers would see a rise in demand for their services in 2024, given the push to use generative AI and the amount of resources (and money) this technology takes to operate. Now mainstream publications are also making this call, and we can all agree that generative AI will grow, and thus cloud computing will too. Simple math.

Like any change in a market, some will take advantage of the new opportunities, and some will be left behind. Recently, Ive been getting many questions about what the workforce supporting cloud-powered generative AI will look like. More importantly, how can you take personal advantage?

Lets explore some new roles that will likely emerge and how you can position yourself to serve in them.

Professionals specializing in designing and optimizing cloud architectures to support generative AI workloads will be in huge demand. How do I know? We dont have enough cloud architects as is, and the mistakes occurring because of the lack of knowledge are starting to take their toll.

Companies will need trained, experienced cloud architects who understand how AI systems work and play well with existing cloud-based systems. If youre interested, youll need training on how a cloud operates and the specific techniques that generative AI services use, such as data, knowledge models, APIs, and other forms of integration, plus how to ensure the scalability, security, and performance of AI systems.

AI and data experts manage and preprocess large data sets used to train generative AI models. Most people understand that AI systems depend on high-quality, accurate data. AI data engineers ensure data quality, implement pipelines, and optimize data storage and retrieval. Their focus is more on data operations, but understanding how AI systems work, including training data, is essential.

This position will require an excellent working knowledge of databases, data integration, and how AI systems ingest data for training. This role also needs to understand data curation, quality, security, and governance. I suspect that most AI data engineers will come from the data operations side of things, not the AI side.

These individuals curate and select the most relevant and effective generative AI models for specific applications. They need to deeply understand the AI landscape and stay updated on the latest advancements, including the most helpful third-party tools and how models can be streamlined.

Again, this is more focused on operations. However, it requires specialized operations skills that most current ops team members wont have. These people will likely come from the data ops side, but deep AI experience is essential.

Yes, this is a thing. With generative AIs potential ethical implications, AI ethicists are crucial in ensuring responsible AI usage. Duties will include assessing and mitigating biases, privacy concerns, and potential societal impacts of these new generative AI systems in the cloud.

This position could come from many different areas. They could be primarily nontechnical roles. I suspect that many will have a business ethics background, but understanding technology will be a vital component of this role, even if that is not understood now.

Not to be confused with those who train people about AI, these professionals specialize in fine-tuning and optimizing generative AI models. Specifically, they work with data scientists and domain experts to prepare models for specific tasks and improve their performance and accuracy.

Think AI-focused CTO or professional who can bridge the gap between technical AI capabilities and business goals. Their role will be identifying opportunities for generative AI deployment, developing strategies, and managing AI projects to drive business outcomes.

Most of these people will come from IT leadership roles with some technical background. They may have been project leaders or worked for the CIO at some point. They will need an eclectic mix of skills to be successful.

I suspect that Im missing a few other roles that will be important, but they will likely be derivatives of the ones listed here. If any of these would be a good career move, then set up your training to head in that direction. Also, position yourself with existing or new jobs so you can move into these roles when they become available. Given that demand will outpace supply, these jobs will pay well, at least for the first few years.

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Largest Children’s Hospital in the United States Standardizes on … – PR Newswire

July 16, 2023 | Author: admin

Texas Children's Hospital consolidates seven business systems on Oracle to increase efficiency, reduce costs, and improve the employee experience

AUSTIN, Texas, July 13, 2023 /PRNewswire/ -- Texas Children's Hospital, the largest children's hospital in the United States, has implemented Oracle Fusion Cloud Applications Suiteto support its mission to create a healthier future for children and women throughout its global community. With Oracle Fusion Applications, Texas Children's Hospital has been able to consolidate seven business systems on one integrated platform to improve recruitment and employee retention, increase efficiency, help reduce costs, and enable its staff to dedicate more time to patients.

Texas Children's Hospital, one of the nation's top ranked pediatric hospitals and the top ranked pediatric hospital in the state of Texas, is also recognized as one of the largest and most comprehensive pediatric and women's health care organizations in the world. To keep up with the demands of its growing operations and to ensure its staff and clinicians can spend as much time as possible focusing on patient care, Texas Children's Hospital needed to streamline and simplify its existing business processes. After careful evaluation, Texas Children's Hospital decided to move finance, HR, and supply chain processes to the cloud with Oracle Fusion Applications.

"Our previous systems required a lot of manual effort to use and maintain and this was becoming an unsustainable burden on our employees," said Myra Davis, executive vice president and chief information innovation officer, Texas Children's Hospital. "With Oracle Fusion Applications, we've been able to streamline and automate business processes and this allows our staff and clinicians to spend more time with patients. Oracle provided hands-on support during implementation to ensure we rapidly gained value from our new system, and we continue to benefit from quarterly updates that enable us to constantly improve productivity."

WithOracle Fusion Cloud Enterprise Resource Planning (ERP), Oracle Fusion Cloud Enterprise Planning Management (EPM),Oracle Fusion Cloud Human Capital Management (HCM), and Oracle Fusion Cloud Supply Chain & Manufacturing (SCM), Texas Children's Hospital has been able to break down organizational silos, standardize processes, and manage its finance, planning, HR, and supply chain operations on a single integrated platform

"Recent events pushed the world's largest industry to a near breaking point and highlighted the challenges of running countless disconnected systems," said Steve Miranda, executive vice president of applications development, Oracle. "With Oracle Fusion Applications, Texas Children's Hospital has been able to increase visibility into its business and reduce the administrative burden on its employees. With Oracle's complete suite of healthcare-focused solutions, we are committed to solving the healthcare industry's biggest challenges and helping customers find new efficiencies, drive down costs, and continually improve patient outcomes."

The implementation was managed by Oracle Consulting.

About Oracle Oracle offers integrated suites of applications plus secure, autonomous infrastructure in the Oracle Cloud. For more information about Oracle (NYSE: ORCL), please visit us at oracle.com.

About Texas Children's Hospital Texas Children's, a not-for-profit health care organization, is committed to creating a healthier future for children and women throughout the global community by leading in patient care, education, and research.Consistently ranked as the best children's hospital in Texas, and among the top in the nation, Texas Children's has garnered widespread recognition for its expertise and breakthroughs in pediatric and women's health.

Trademarks Oracle, Java, MySQL and NetSuite are registered trademarks of Oracle Corporation. NetSuite was the first cloud company--ushering in the new era of cloud computing.

SOURCE Oracle

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The edge computing market size is expected to grow from USD 53.6 billion in 2023 to USD 111.3 billion by 2028, at a Compound Annual Growth Rate (CAGR)…

July 16, 2023 | Author: admin

ReportLinker

during the forecast period. The requirement of companies to collect and analyze data at the very source from where it is generated, growth of IoT networks, increased bandwidth, reduced latency, cost effectiveness, emergence of edge native cloud platforms have made connecting edge devices and sensors the need of the hour, hence compelling enterprises to adopt and harness the power of edge computing.

New York, July 12, 2023 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Edge Computing Market by Component, Application, Organization Size, Vertical and Region - Global Forecast to 2028" - https://www.reportlinker.com/p05181598/?utm_source=GNW

By component, services segment to exhibit significant growth during the forecast period

The services segment plays a crucial role in the edge computing market, providing a range of essential functions and support to ensure the successful implementation and operation of edge computing solutions.Services in the edge computing market encompass a variety of offerings that cater to the unique requirements and complexities of edge deployments.

Consulting and advisory services form a significant part of the services segment.These services assist organizations in understanding the potential benefits of edge computing, assessing their specific needs, and devising an appropriate strategy for implementation.

Consultants guide businesses in identifying the optimal edge infrastructure, network architecture, and edge device placement to achieve their desired outcomes.Next, the integration and deployment services are also vital in the edge computing ecosystem.

These services aid organizations in seamlessly integrating edge computing solutions into their existing IT infrastructure. They involve activities such as hardware and software installation, configuration, testing, and system integration. By leveraging these services, businesses can effectively bridge the gap between their current infrastructure and the edge environment. Furthermore, managed services play a significant role in ensuring the ongoing smooth operation of edge computing deployments. These services encompass monitoring, maintenance, and support for the edge infrastructure, as well as managing software updates, security patches, and performance optimization. Managed services relieve organizations of the complexities associated with managing distributed edge environments and enable them to focus on their core competencies. And then the training and education services contribute to the growth of the edge computing market. These services provide organizations with the knowledge and skills required to operate and manage edge computing solutions effectively. Training programs may cover topics such as edge architecture, edge analytics, security practices, and data governance, empowering businesses to derive maximum value from their edge deployments.Summarily, the services segment in the edge computing market is vital for supporting organizations throughout their edge computing journey, encompassing consulting, integration, deployment, managed services, and training. These services enable businesses to overcome implementation challenges, optimize performance, and achieve the desired business outcomes from their edge computing initiatives. By application, AR & VR segment to exhibit decent growth during the forecast period

AR is more common and has more practical applications.The technology behind AR requires devices to process visual data and represent visual elements in real time.

Without the edge technology, these visual elements need to send back to the centralized data centers, where these elements can be added before sending it back to that device.This arrangement will offer significant latency.

Edge technology will enable IoT devices to integrate AR displays instantly, enabling users to look and take in new AR details without much loading time.AR devices have applications beyond entertaining applications, such as retail, where it is being utilized to display product information.

Thus, edge architecture will play a vital role in providing these applications with minimal latency. AR is an extremely complex technology. The device must understand data from multiple sensors to react in a real-time environment. Edge infrastructure enables these devices to react in real-time immediately, without delay in data transfer speed. Thus, edge technology will introduce speed and accuracy to make these devices more accurate across applications. By vertical, government and defense vertical to grow significantly during the forecast period

Growing expectations from citizens and dropping budgets during the global financial crisis are limiting the ability of policymakers, administrators, and key decision-makers to meet the citizens needs.To aptly serve the needs of citizens, government agencies must advance and expand the deployment of advanced technologies for the development of infrastructure for smart cities, such as traffic monitoring, parking management, and waste management.

Government agencies are increasingly infusing edge computing in their IT infrastructure to gain greater data visibility in far-flung locations and achieve faster data analysis.This enables them to identify assets at greater risk and explore new mission scenarios for minimizing loss and optimizing efficiency.

For instance, the US Air Force deployed Dell and Microsofts cloud and edge computing solutions and saved USD 1 million in weekly tanker refuelling costs. US Marine and other special forces also use such applications to achieve situational awareness.

Latin American region is showing promising growth in the edge computing market during the forecast period in 2023

Companies in the region are focusing on providing better services and establishing communications withtheir customers. Owing to low-cost software requirements and various cloud computing benefits, such as easy adaptability, multitenancy, and a high degree of abstraction, various companies across Latin America have adopted cloud computing technology. However, with the peaking data volumes, Latin American companies are expected to adopt edge computing to eliminate network congestion issues and data loss risks. Cloud computing addresses inefficiencies by flattening peak loads and optimizing data centers, and edge computing add to the benefits by providing low latency connectivity and high bandwidth for data transmission. Owing to the proliferation of 5G, data-driven enterprises would demand quick and real-time access to data; hence, edge computing is expected to grow in this region.

In the process of determining and verifying the market size for several segments and subsegments gathered through secondary research, extensive primary interviews were conducted with the key people.

The breakup of the profiles of the primary participants is as follows: By Company Type: Tier I: 35%, Tier II: 25%, and Tier III: 40% By Designation: C-Level: 25%, D-Level: 30%, and Others: 45% By Region: North America: 42%, Europe: 25%, APAC: 18%, Row: 15%

The report profiles the following key vendors:Cisco (US), AWS (US), Dell Technologies (US), Google (US), HPE (US), Huawei (China), IBM (US), Intel (US), Litmus Automation (US), Microsoft (US), Nokia (Finland), ADLINK (Taiwan), Axellio (US), Capgemini (France), ClearBlade (US), Digi International (US), Fastly (US), StackPath (US), Vapor IO (US), GE Digital (US), Moxa (Taiwan), Sierra Wireless (Canada), Juniper Networks (US), EdgeConnex (US), Belden (US), Saguna Networks (Israel), Edge Intelligence (US), Edgeworx (US), Sunlight.io (UK), Mutable (US), Hivecell (US), Section (US), EdgeIQ (US).

Research CoverageThe report segments the edge computing market by the component segment which includes software, hardware, and services.

Based on the application, the market is segmented into smart cities, Industrial Internet of Things (IIoT), remote monitoring, content delivery, Augmented Reality (AR) and Virtual Reality (VR), and other applications (autonomous vehicles, drones, and gaming).

The market is also segmented based on organization sizes as small and medium-sized enterprises and large enterprises.

Different verticals using edge computing solutions include manufacturing, energy and utilities, government and defense, healthcare and life sciences, media and entertainment, retail and consumer goods, telecommunications, transportation and logistics, and other verticals (education and BFSI).

The geographic analysis of the edge computing market is spread across five major regions: North America, Europe, Asia Pacific, Middle East and Africa, and Latin America.

Key Benefits of Buying the Report The report will help the market leaders/new entrants in the edge computing market with information on the closest approximations of the revenue numbers for the overall edge computing market and the subsegments across regions. The report provides the impact of recession on the aforesaid market, among top vendors worldwide, along with figures which are the closest approximations, estimated and projected. The report will help stakeholders understand the competitive landscape and gain more insights to better position their businesses and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities. It would help stakeholders understand the market dynamics better, their competitors better and gain more insights to uplift their positions in the market. The competitive landscape section includes a competitor ecosystem, market diversification parameters such as new product launch, product enhancement, partnerships, agreement, integration, collaborations, and acquisitions. The Market quadrant of edge computing vendors have been precisely incorporated as a figure which helps readers understand market players categorization and their performance. In-depth exhaustive assessment of market shares, growth strategies and service offerings of leading players in the edge computing market strategies. The report also helps stakeholders understand the competitive analysis by these market players via competitive benchmarking tables.Read the full report: https://www.reportlinker.com/p05181598/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Microsoft Is Big Winner as Corporate Tech Spending Shifts to AI – Barron’s

July 16, 2023 | Author: admin

Piper Sandlers latest survey of corporate technology buyers shows overall spending outlooks have softened. But there are also changes in budget priorities with a clear winner: artificial intelligence applications.

Microsoft ticker: MSFT) will be a big beneficiary of the move toward AI as companies stated a higher intention to use more of its cloud-computing services, Piper analyst Brent Bracelin said. He has an Overweight rating for Microsoft stock and a $400 price target.

On Wednesday, the investment bank published a report after asking 147 chief information officers, or CIOs, which areas they intend to spend more or less this year and in the future.

Results suggest that IT budgets are likely to moderate in 2023, Pipers tech team wrote. They said expectations for 2023 spending fell to a 3.6% growth rate year-over-year, which was down by 1.3 percentage points, compared with six months ago.

The analysts said generative AI rose nine spots in priority to become the top emerging technology trend for the next three years with 75% of CIOs either testing or implementing projects. Security, storage, and application software were also top spending priorities, while hardware devices and servers were near the bottom.

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Enterprise testing and implementation of Gen AI appears to be consuming incremental budget dollars, Pipers team said.

The release of ChatGPT late last year and its rapid success have sparked a surge in interest for generative artificial-intelligence products that train on text, images, and videos to create content and provide analytical output. The chatbot uses a language model that generates humanlike responses based on word relationships it has found by digesting what has been written on the internet or in other text.

In early trading Thursday, Microsoft shares rose by 1.3% to $341.55.

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After looking at the CIO responses, Pipers team predicted hardware suppliers such as makers of computer servers were most likely to face budget cuts later this year.

Write to Tae Kim at tae.kim@barrons.com

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Fourteen things you need to know about collaborating with data scientists – Nature.com

July 16, 2023 | Author: admin

Think of your relationship as a partnership, rather than as a transaction, say data scientists.Credit: Morsa Images/Getty Images

Data science is increasingly an integral part of research. But data scientists can wear many hats: interdisciplinary translator, software engineer, project manager and more. Fulfilling all these roles is challenging enough; but this difficulty can be exacerbated by differing expectations and, frankly, an undervaluing of data scientists contributions.

For example, although our primary role is data analysis, researchers often approach data scientists for help with data acquisition and wrangling as well as software development. Although in one sense this is technical work, which perhaps only a data scientist can do, thinking of it as such overlooks its deep connection with reproducible research. The work also involves elements of data management, project documentation and adherence to best practices. Solely emphasizing a projects technical requirements can lead collaborators to view the work as a transaction rather than as a partnership. This misunderstanding, in turn, poses obstacles to communication, project management and reproducibility.

As data scientists with a collective 17 years of experience across dozens of interdisciplinary projects, we have seen at first hand what does and doesnt work in collaborations. Here, we offer tips for how to make working relationships more productive and rewarding. To our fellow data scientists: this is how we strike the balance. To the general audience: these are the parts of data science with which everyone on the team should engage.

Set boundaries and norms for how communication will happen. Do members want to meet virtually or in person? When, how often, and on what platform should they meet? Decide how you will record tasks, project history and decisions. Make sure all members of the team have access to the project records so that everyone is kept abreast of its status and goals. And identify any limitations due to IT policies or privacy concerns. For example, many US government agencies restrict employees to an approved list of software tools.

Err on the side of over-communicating by including everyone on communications and making the projects repositories available to all members of the team. Involve collaborators in technical details, even if they are not directly responsible for these aspects of the project.

Different disciplines can attach very different meanings to the same term. Map, for example, means different things to geographers, geneticists and database engineers. When discrepancies arise, ask for clarification. Learn about the other disciplines on your team and be prepared to learn their jargon and methods.

Questions from people outside your domain can reveal important workflow difficulties, illuminate misunderstandings or expose new lines of enquiry. Dont allow questions to linger; if you need time to consider the answer, acknowledge that it was asked and follow it up. Address all questions with respect.

Diagrams, screenshots, process descriptions, and summary statistics can serve as a unifying language for team members and emphasize the bigger picture, avoiding unnecessary detail. Use them when you can.

Before starting the research, identify the goals and expected outputs of the collaboration. As a team, create a project timeline with concrete milestones, making sure to allow time for project set-up and data exploration. Ensure all team members are aware of the timeline and address any concerns before proceeding.

One potential pitfall of working collaboratively is that a projects scope can easily expand. To guard against this, when new ideas emerge, decide as a team if the new task helps you to meet the original goal. You might need to set the idea aside to stay on target. Perhaps this idea is the source of the next collaboration or grant application. A clear red flag is the question, You know what would be cool?

Agree early on about how and where the team will share files. This might involve your own servers, cloud storage, shared document-editing platforms, version-control platforms or a combination of these. Everyone should have appropriate levels of access. If theres a chance that the project will produce code or data for public use, develop a written plan for long-term storage, distribution, maintenance, and archiving. Discuss licensing early.

Develop a data-processing pipeline that extends from raw data to final outputs, avoiding hard-to-reproduce graphical interfaces or ad hoc steps whenever possible in favour of coded alternatives written in languages such as Python, R and Bash. Use a version-control system, such as git, to track changes to the project files, and an environment manager, such as conda, to track software versions.

Be proactive about documenting technical steps. Before you begin, write draft documentation to reflect your plan. Edit and expand the documentation as you progress, to clarify details. Maintain the documentation after the project concludes so that it serves as a reference. Write in plain language and keep jargon to a minimum. If you must use jargon, define it.

Although you cant anticipate all project outputs in advance, discuss attribution, authorship and publication responsibilities as early as possible. This clarity provides a point of reference for reassessing participants roles if the project direction changes.

Collaborating with people who have diverse backgrounds and skill sets often sparks creativity. Be open to ideas, but be willing to put them on the back burner or discard them if they dont fit the project scope and timeline. Working with domain experts in one-on-one advice sessions, incubator projects, and in-the-moment data-analysis sessions often surfaces new data sources or potential modelling applications, for example. More than a few of our current grant projects have their roots in what was at first an improvisational exercise.

Disciplines are vast, and knowing when to defer to others expertise is essential for project momentum and keeping contributions equitable. Striking this balance is especially important around project infrastructure. Not everyone needs to write or run code, for example, but learning how to use technical platforms, such as code repositories or data storage, rather than relying on others to do so, balances the workload. If collaborators want to be involved in technical details, or if the project will be handed over to them in the long term, data scientists might need to teach collaborators as well.

Recognize when a project has run its course, whether it has been successful or not. Ongoing requests for work such as new analyses often weigh unequally on those responsible for project infrastructure. If the project didnt achieve its stated goals, look for a silver lining: it doesnt mean failure if there are insights, results or new lines of enquiry to explore. Above all, respect the timeline and the fact that you and your collaborators have other responsibilities.

Interdisciplinary collaborations that integrate data science can be challenging, but we have found these guidelines to be effective. Many involve skills that you can develop and refine over time. Thoughtful communication, careful project organization and equitable working relationships transform projects into genuine collaborations, yielding research that would not otherwise be possible.

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10 Best Data Science Communities to Join in 2023 – Analytics Insight

July 16, 2023 | Author: admin

Weve created a list of the finest 10 best data science communities in 2023

Asdata science gets more popular, so does the number of groups and resources committed to it. Whether youre just starting or have been working in the area for years, several resources are available for help, education, and cooperation.

1. Reddit: Redditis one of the webs largest and most activedata analytics communities. Its a terrific place to ask questions, exchange thoughts, and stay current on the latest news and advancements in the area, and it has over 1.5 million members.

2. Kaggle: Kaggle is an excellent place to begin. Kaggle is one of the largest online data science slack channels, with over 1.5 million users. You can discover datasets, code samples, and discussion forums for any data science topic imaginable.

3. IBM Data Community: There are several ways to participate in the data analytics forum, and IBM Data Community is an excellent resource for data scientists of all levels of expertise. The community provides a variety of materials, such as blogs, articles, webinars, and online courses.

4. Tableau: Tableau Public is one of these. If you use Tableau to visualize data, this is the community for you.TableauPublic is a free online platform where you can share your visualizations with the rest of the world. You may also look at other data scientists work, get comments on your own, and engage in challenges and contests.

5. Stack Overflow: You may discover solutions to queries regarding coding, data analysis, machine learning, and other topics on Stack Overflow. You can also ask your questions and receive responses from the community. Furthermore, Stack Overflow provides various tools for data scientists, such as articles, tutorials, and courses.

6. Open Data Science: If youre searching for a comprehensive data science community, Open Data Science is a beautiful place to start. It is one of the largest online groups for data scientists, with over 30,000 members. In addition to a jam-packed Slack channel, Open Data Science provides many resources such as publications, courses, events, and employment.

7. Data Science Central: Its one of the largest online groups for data scientists, with over 600,000 members. Several resources are available on the site, including articles, tutorials, webinars, and an active community where users may ask questions and discuss their work. Data Science Central is an excellent resource in your toolbox at any level of your data science journey.

8. Dataquest: Dataquest is a fantastic resource. They provide articles, webinars, and courses on various topics ranging from machine learning to deep learning. Its also a terrific location to compete in data science challenges and learn from the finest in the industry if you prefer a more hands-on approach.

9. Driven Data: Driven Data is among the most well-known data science communities. Driven Data organizes challenges and contests that are available to anybody with an interest in data science. This is a terrific opportunity to exercise your coding muscles and put your problem-solving abilities to the test.

10. Data Community DC: Data Community DC is a Washington, DC-based professional network for data scientists. The group provides its members with various services and activities, such as monthly gatherings, an online forum, and a mentorship program.

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10 Best Data Science Communities to Join in 2023 - Analytics Insight

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The First Half of 2023: Data Science and AI Developments – KDnuggets

July 16, 2023 | Author: admin

A lot has happened in the first half of 2023. There have been significant advancements in data science and artificial intelligence. So much that its been hard for us to keep up with them all. We can definitely say that the first half of 2023 has shown rapid progress that we did not expect.

So rather than talking too much about how were all wood by these innovations, lets talk about them.

Im going to start off with the most obvious. Natural Language Processing (NLP). Something that was building in the dark, and in the year 2023 has come to light.

These advancements were proven in OpenAIs ChatGPT, which took the world by storm. Since their official release earlier on in the year, ChatGPT has moved from GPT-4 and now were expecting GPT-5. They have released plugins to improve people's day-to-day lives, and workflows for data scientists and machine learning engineers.

And we all know after ChatGPT released, Google released Bard AI which has proven to be successful amongst people, businesses, and more. Bard AI has been competing with ChatGPT for the best chatbot position, providing similar services such as improving tasks for machine learning engineers.

In the midst of the release of these chatbots, we have seen large language models (LLM) drop out of thin air. Large Model Systems Organization (LMSYS Org), an open research organization founded by students and faculty from UC Berkeley created ChatBot Arena - a LLM benchmark to make models more accessible to everyone using a method of co-development using open datasets, models, systems, and evaluation tools.

So now people are getting used to chatbots that answer questions for them and make their work and personal life much easier - what about data analysts and machine learning specialists?

Well theyve been using AutoML - a powerful tool for data professionals such as data scientists and machine learning engineers to automate data preprocessing, hyperparameter tuning, and perform complex tasks such as feature engineering. With the advancements in data science and AI, naturally we have seen a high demand for data and AI specialists. However, as the progress is moving at a rapid rate, we are seeing a shortage of these AI professionals. Therefore, being able to find ways to explore, analyze, and predict data in an automated process will improve the success of a lot of companies.

Not only will it be able to free up time for data specialists, but organizations will have more time to expand and be more innovative on other tasks.

If you were around for the outburst of chatbots, you would have seen the words Generative AI being thrown around. Generative AI is capable of generating text, images, or other forms of media based on user prompts. Just like the above advancements, generative AI is helping different industries with tasks to make their lives easier.

It has the ability to produce new content, replace repetitive tasks, work on customized data, and pretty much generate anything you want. If generative AI is new to you, you will want to learn about Stable Diffusion - it is the foundation behind generative AI. If you are a data scientist or data analyst, you may have heard of PandasAI - the generative AI python library, if not it is an open-source toolkit which integrates generative AI capabilities into Pandas for simpler data analysis.

But with these generative AI tools and softwares being released, Are Data Scientists Still Needed in the Age of Generative AI?

Deep Learning is continuing to thrive. With the recent advancements in data science and AI, more time and energy is being pumped into research of the industry. As a subset of machine learning concerned with algorithms and artificial neural networks, it is widely being used in tasks such as image classification, object detection, and face recognition.

As were experiencing the 4th industrial revolution, deep learning algorithms are allowing us to learn from data the same way humans do. We are seeing more self-driving cars on the roads, fraud detection tools, virtual assistants, healthcare predictive modeling, and more.

2023 has proven to show the works of deep learning through automated processes, robotics, blockchain, and various other technologies.

With all these that are happening, you must think these computers are pretty tired right? In order to meet the advancements of AI and data science, companies require computers and systems that can help to support them. Edge computing brings computation and data storage closer to the sources of data. When working with these advanced models, edge computing provides real-time data processing and allows for smooth communication between all devices.

For example, when LLMs were getting released every two seconds, it was obvious that organizations would require effective systems such as edge computing to be successful. Google released TPU v4 this year - computing resources that can handle the high computational needs of machine learning and artificial intelligence.

Due to these advancements, we are seeing more organizations move from the cloud to edge to fit their current and future requirements.

A lot has been happening, and its been happening in a short period of time. Its becoming very difficult for organizations such as the government to keep up. Governments from around the world are raising the question of how do these AI applications affect the economy and society, and what are the implications?.

People are concerned about the bias and discrimination, privacy, transparency, and security of these AI and data science applications. So what are the ethical aspects of AI and data science, and what should we expect in the future?

We already have the European AI Act pushing a framework that groups AI systems into 4 risk areas. OpenAI CEO Sam Altman testified about the concerns and possible pitfalls of the new technology at a US Senate committee on Tuesday the 16th. Although there are a lot of advancements happening in a short period of time, a lot of people are concerned. Over the next 6 months we can expect a few more laws getting passed and regulations and frameworks being put into place.

If you havent been keeping up with AI and data science in the last 6 months, I hope this article has provided you with a quick breakdown of whats been going on. It will be interesting to see over the next 6 months how these advancements get embraced whilst being able to ensure responsible and ethical use of these technologies.Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.

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Data scientists predict stock returns with AI and online news … – Cornell Chronicle

July 16, 2023 | Author: admin

For years, the financial press has helped inform investors of all stripes. Cornell researchers have discovered it can also inform the algorithm behind a new financial predicting model.

In their paper, News-Based Sparse Machine Learning Models for Adaptive Asset Pricing, published in Data Science in Science in April, the researchers draw from interdisciplinary fields such as machine learning, natural language processing (NLP) and finance to build a new, interpretable machine-learning framework that captures stock- and industry-specific information and predicts financial returns with greater accuracy than traditional models.

One of the knocks on machine learning is its not interpretable, said Martin Wells, the Charles A. Alexander Professor of Statistical Sciences in the Cornell Ann. S Bowers College of Computing and Information Science and the papers senior author. Often when researchers use big models such as these, they may not know what the outputs mean or what is underlying the model. This research leverages text data from the news to build interpretable machine-learning models where you can see the important features explicitly.

The text helps with clustering the data, bringing order to the chaotic results algorithms can produce, said lead author Liao Zhu, Ph.D. 20, who started working in the finance industry after finishing the paper. Our hypothesis is that the financial news could do better in helping us better understand what type of stocks are related to certain tradable assets.

These assets could include exchange-traded funds (ETF), a bundle of stocks that tracks an entire sector, he said.

The paper is a continuation of Zhus previous research that emerged from his early doctoral studies under Wells and Robert Jarrow, the Ronald P. & Susan E. Lynch Professor of Investment Management at the Samuel Curtis Johnson Graduate School of Management. Peter (Haoxuan) Wu, Ph.D. 23 is a co-author of the paper.

Applying traditional statistics methods to market data to explain stock returns is not new. Neither is using text data: Investors have used sentiment analysis, a subfield of natural language processing, to mine online text for positive or negative words associated with a company that, in theory, may signal a stock prices rise or fall.

The new research treads new ground by proposing a flexible prediction framework that bridges market data and text data without sentiment analysis, and integrates new, interpretable machine-learning algorithms. The researchers borrow the method of word embeddings from natural language processing and use an algorithm to create asset embeddings for a specific set of tradable assets from financial news. After converting both text and market data into numbers, the researchers then deploy custom-designed algorithms to crunch the numbers.

Our algorithm is not using the sentiment from the news but using the news as guidance for what assets or words to consider for each specific stock or industry, which reveals more stock- and industry-specific information, Zhu said.

To develop their models, researchers scraped a massive corpus of online financial news articles from 2013 to 2019 and fed it to their algorithm, which began mapping particular assets and words associated with specific stocks and industries. With an AI-optimized language map in hand, researchers had more insight into specific assets and words to consider.

Using this method, the team developed two separate models. The News Embedding UMAP Sparse Selection (NEUSS) model predicts returns for individual stocks, and the News Sparse Encoder with Rationale (INSER) model identifies important words for each specific industry before using them to predict industry returns more accurately.

For example, the NEUSS model may conclude from the financial news that an exchange-traded fund that tracks the semiconductor manufacturing sector is useful to predict the stock returns of a specific tech company, but may not be useful to predict returns of other stocks in, say, retail or wholesale. The INSER model may pick up the word plant as important for the energy industry, but this word may not be relevant for other industries like social media.

The hybrid, interpretable strategy worked. The NEUSS model beat out the traditional predictive benchmark called the Fama-French 5-factor model by 50%, while the INSER model beat the benchmark (without industry-specific information) by 10%.

The use of advanced machine-learning algorithms with different types of data is helping to revolutionize the finance field, Zhu and Wells said.

I think the AI revolution in finance is already there, Zhu said, and this paper is moving an aspect of that revolution forward.

Louis DiPietro is a writer for the Cornell Ann S. Bowers College of Computing and Information Science

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Data scientists predict stock returns with AI and online news ... - Cornell Chronicle

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