Category Archives: Machine Learning
Unlock the Power of AI A Special Release by KDnuggets and … – KDnuggets
Hello,
I hope this email finds you well, coding away and innovating in the dynamic world of Machine Learning.
Today, I am excited to announce a collaboration between Machine Learning Mastery and KDnuggets. Together, we've created something unique to enrich your Machine Learning journey.
I present to you our brand new ebook, "Maximizing Productivity with ChatGPT". While we've been known for our technical, code-heavy books that have guided many through the intricate pathways of Machine Learning, this time we're offering something different but equally impactful.
This ebook shifts the focus from pure coding and technical aspects, to understanding, interacting, and leveraging one of the most advanced AI tools in the market - ChatGPT. This is an evolution from our prior books, aimed at broadening your perspective and deepening your understanding of AI applications.
You'll discover:
In celebration of this launch, we're offering an exclusive 20% early bird discount with the code "20offearlybird" at checkout. But don't delay - this offer ends soon!
Maximizing Productivity with ChatGPT
This ebook is a testament to the fact that not all roads to mastering Machine Learning and AI are paved with code alone. Harnessing the power of AI also involves understanding its applications and learning how to effectively interact with it. "Maximizing Productivity with ChatGPT" offers you exactly that - an avenue to explore and master the usage of AI beyond the traditional coding confines.
If you have any questions, please don't hesitate to hit reply and send me an email directly. Here's to harnessing the power of AI together.
- Jason, Machine Learning Mastery Founder
Read more from the original source:
Unlock the Power of AI A Special Release by KDnuggets and ... - KDnuggets
Drones stay on course in difficult conditions thanks to machine … – Professional Engineering
(Credit: MIT News, with figures from iStock)
A new machine-learning based approach can control drones and autonomous vehicles more effectively and efficiently in difficult conditions, according to its developers at the Massachusetts Institute of Technology (MIT) and Stanford University.
The technique, designed for dynamic environments where conditions can change rapidly, could help an autonomous vehicle learn to compensate for slippery road conditions to avoid going into a skid. Other potential applications include allowing a robotic free-flyer to tow different objects in space, or enabling a drone to closely follow a downhill skier despite being buffeted by strong winds.
The researchers approach incorporates structures from control theory into the process for learning a model. It does this in such a way that leads to an effective method of controlling complex dynamics, an MIT announcement said, such as those caused by wind on the trajectory of a flying vehicle. The structures are like hints that can help guide how to control a system, the announcement added.
The focus of our work is to learn intrinsic structure in the dynamics of the system that can be leveraged to design more effective, stabilising controllers, said assistant professorNavid Azizan from MIT. By jointly learning the systems dynamics and these unique control-oriented structures from data, were able to naturally create controllers that function much more effectively in the real world.
The technique immediately extracts an effective controller from the model, the announcement said, as opposed to other machine-learning methods that require a controller to be derived or learned separately with additional steps. With this structure, the researchers approach is also able to learn an effective controller using less data than other approaches. This could help their learning-based control system achieve better performance, faster, in rapidly changing environments.
This work tries to strike a balance between identifying structure in your system and just learning a model from data, said lead authorSpencer M Richards, a graduate student at Stanford University. Our approach is inspired by how roboticists use physics to derive simpler models for robots. Physical analysis of these models often yields a useful structure for the purposes of control one that you might miss if you just tried to naively fit a model to data. Instead, we try to identify similarly useful structure from data that indicates how to implement your control logic.
The researchers found that their method was data-efficient, achieving high performance even with little data. It could reportedly model a highly dynamic rotor-driven vehicle using only 100 data points, for example. Methods that used multiple learned components saw their performance drop much faster with smaller datasets.
This efficiency could make the technique especially useful in situations where a drone or robot needs to learn quickly in rapidly changing conditions.
The general approach could also be applied to many types of dynamical systems, from robotic arms to free-flying spacecraft operating in low-gravity environments.
The work was supported, in part, by the NASA University Leadership Initiative and the Natural Sciences and Engineering Research Council of Canada. The research will be presented at the International Conference on Machine Learning (ICML), running this week at the Hawaii Convention Centre.
Want the best engineering stories delivered straight to your inbox? TheProfessional Engineeringnewslettergives you vital updates on the most cutting-edge engineering and exciting new job opportunities. To sign up, clickhere.
Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.
Read the original post:
Drones stay on course in difficult conditions thanks to machine ... - Professional Engineering
Google reveals how AI and machine learning are shaping its … – ComputerWeekly.com
Google has lifted the lid on how artificial intelligence (AI) and machine learning (ML) are assisting it with helping consumers and businesses shrink the environmental footprint of their activities by allowing them to make real-time adjustments that can curb their greenhouse gas (GHG) emissions.
Details of its work in this area can be found in the tech giants most recent annualEnvironmental report. Covering the 12 months to 31 December 2022, the document provides updates on how the tech giants efforts to run its datacentres and offices on carbon-free energy (CFE) round-the-clock are progressing and how its bid to reduce the water consumed by its operations is going.
We achieved approximately 64% round-the-clock CFE across all of our datacentres and offices, [and] this year, we expanded our CFE reporting to include offices and third-party datacentres, in addition to Google-owned and operated datacentres, said the company.
At the end of 2022, our contracted watershed projects have replenished 271 million gallons of water equivalent to more than 400 Olympic-sized swimming pools to support our target to replenish 120% of the freshwater we used.
The report also documents how, seven years after declaring itself as being an AI-first company, this technology is underpinning the companys own climate change mitigation efforts.
To this point, the company said it was using AI to accelerate the development of climate change-fighting tools that can provide better information to individuals, operational optimisation for organisations, and improved predicting and forecasting.
As an example, the company pointed to the way Google Maps uses AI to help users plan journeys in a more eco-friendly way by minimising the amount of fuel and battery power they use to get from A to B.
Eco-friendly routing has helped prevent 1.2 metric tonnes of estimated carbon emissions since launch equivalent to taking approximately 250,000 fuel-based cars off the road for a year, it reported.
The technology is also proving useful in the companys work to reduce the environmental footprint of its AI models by helping the datacentres in which they are hosted run in a more energy-efficient way.
Weve made significant investments in cleaner cloud computing by making our datacentres some of the most efficient in the world and sourcing more carbon-free energy, it said in the report. Were helping our customers make real-time decisions to reduce emissions and mitigate climate risks with data and AI.
To reinforce this point, the company cited the roll-out of its Active Assist feature to Google Cloud customers, which uses machine learning to identify unused and potentially wasteful workloads so they can be stopped to save money and cut the organisations carbon emissions at the same time.
On the flipside, though, the report went on to acknowledge that ramping up the use of AI in this way also increases the amount of work its datacentres are doing, which is giving rise to concerns about the environmental impact and energy consumption habits of its AI workloads.
With AI at an inflection point, predicting the future growth of energy use and emissions from AI compute in our datacentres is challenging, the report continued.
Historically, research has shown that as AI/ML compute demand has gone up, the energy needed to power this technology has increased at a much slower rate than many forecasts predicted. We have used tested practices to reduce the carbon footprint of workloads by large margins; together, these principles have reduced the energy of training a model by up to 100x and emissions by up to 1,000x.
The report added: We plan to continue applying these tested practices and to keep developing new ways to make AI computing more efficient.
View original post here:
Google reveals how AI and machine learning are shaping its ... - ComputerWeekly.com
Forum Launched to Promote Safe Development of Large Machine … – Fagen wasanni
OpenAI, Microsoft, Google, and Anthropic have come together to create a forum that aims to support the safe and responsible development of large machine-learning models. This collaboration between top leaders in artificial intelligence is focused on coordinating safety research and establishing best practices for what are known as frontier AI models. These models exceed the capabilities of existing advanced models and have the potential to pose significant risks to public safety.
Generative AI models, such as the one powering chatbots like ChatGPT, have the ability to generate responses in the form of prose, poetry, and images by extrapolating vast amounts of data at high speeds. While these models offer various applications, industry experts and government bodies like the European Union have emphasized the need for appropriate measures to mitigate the risks associated with AI technologies.
In a statement, Microsoft President Brad Smith highlighted the responsibility of companies in ensuring the safety, security, and human control of AI technology. The Frontier Model Forum, the industry body established by the collaboration, will work closely with policymakers, academics, and governments to facilitate information sharing and promote responsible practices. The forum will prioritize the development and sharing of a public library of benchmarks and technical evaluations for frontier AI models.
The Frontier Model Forum plans to establish an advisory board in the near future and secure funding to support its initiatives. The forum will not engage in lobbying activities but instead focus on advancing AI safety. Anna Makanju, Vice President of Global Affairs at OpenAI, emphasized the urgency of this work and expressed the forums readiness to make swift progress in this critical area.
The collaboration between OpenAI, Microsoft, Google, and Anthropic demonstrates a collective commitment to ensuring the safe and responsible development of AI models. By coordinating research efforts and sharing best practices, this forum aims to address potential risks associated with the growing capabilities of machine-learning models.
Follow this link:
Forum Launched to Promote Safe Development of Large Machine ... - Fagen wasanni
Wipro Earns Advanced Specialization in AI and Machine Learning … – Wipro
What the AI and Machine Learning on Microsoft Azure Advanced Specialization Means for Wipro and Its Customers
Partners like Wipro with the AI and Machine Learning on Microsoft Azure Advanced Specialization have the tools and knowledge necessary to develop AI solutions per customers requirements, build AI into their mission-critical applications and put responsible AI into action.
Achieving the AI and Machine Learning in Microsoft Azure Specialization is a proud moment for us, showcasing our deep expertise through third-party audit validation, said Don McCormick, Vice President and Head of the Wipro-Microsoft Partnership. It also highlights our commitment to foster a strong partnership with Microsoft, utilizing our solutions and accelerators built with Microsoft technologies to empower our clients to fully realize the benefits of AI and machine learning. This is our fourteenth Microsoft Advanced Specialization and we are honored to be recognized for our partnership with Microsoft. We look forward to continuing to work together to drive innovation for all our customers.
Learn more about Wipros partnership with Microsoft Azure.
Go here to see the original:
Wipro Earns Advanced Specialization in AI and Machine Learning ... - Wipro
Advancing Patient Care: 5 Brands Harnessing AI and Machine … – Microbioz India
Overview
The incorporation of Artificial Intelligence (AI) and Machine Learning (ML) in the healthtech industry has sparked an innovation in patient care, medical research, and healthcare efficiency. These pioneering technologies are strengthening the healthcare providers and researchers with several treatment options. However, as the healthtech segment continue to evolve, maintaining a balance between innovation and security is extremely important in order to protect sensitive patient data and ensure ethical AI practices. Here, we explore five leading brands that leverage Artificial Intelligence and Machine Learning to drive innovation in healthtech while prioritizing data privacy and security.
IBM Watson Health stands at the forefront of AI and ML-driven healthtech innovation. Their flagship project, Watson for Oncology, harnesses cognitive computing to analyze vast volumes of medical literature, clinical trials, and patient data to offer personalized treatment options for cancer patients. The system can suggest evidence-based treatment plans, helping oncologists make well-informed decisions. With a strong emphasis on data security and privacy, IBM Watson Health adheres to regulatory standards, ensuring the protection of patient data and compliance with HIPAA (Health Insurance Portability and Accountability Act) guidelines. The brands commitment to transparency in AI decision-making processes fosters trust among healthcare providers and patients alike.
NVIDIA Clara is a comprehensive AI platform designed explicitly for healthcare. Leveraging the power of NVIDIAs high-performance GPUs, Clara provides healthcare professionals with advanced imaging and visualization tools. These tools enable faster and more accurate medical imaging diagnosis, surgical planning, and drug discovery.
Recognizing the sensitivity of medical data, NVIDIA has implemented strong security measures within the Clara platform, ensuring data encryption, access control and audit trails. Additionally, the platform adheres to industry standards, such as DICOM (Digital Imaging and Communications in Medicine), to facilitate seamless integration with existing healthcare systems while safeguarding patient privacy.
Noventiq is a leading global provider of solutions and services in the realms of digital transformation and cybersecurity. Noventiqs expertise lies in facilitating and enabling digital transformation processes, empowering their customers to adapt to the evolving digital landscape. It provides cloud protection services and AI algorithms, ensuring that customer data and applications hosted in the cloud are secure and protected from unauthorized access to health related data to maintain patient privacy.
Siemens Healthineers combines AI and ML technologies to enhance medical imaging, diagnostics, and precision medicine. Their AI-Rad Companion platform assists radiologists by automating image analysis, facilitating faster diagnosis, and reducing the chance of human error.
Recognizing the importance of data security in the healthcare domain, Siemens Healthineers adheres to international data protection standards and implements state-of-the-art encryption protocols to protect patient data at all stages of processing and transmission. Their robust compliance measures assure both healthcare providers and patients that their data remains secure and private.
Cerner Corporation is a global leader in electronic health record (EHR) systems and clinical information solutions. Through their AI-enabled HealtheDataLab, they empower healthcare researchers with access to vast amounts of anonymized patient data for population health studies and medical research.
Cerner Corporation places utmost importance on data privacy and compliance with healthcare regulations, ensuring that all data is de-identified and anonymized before use in research. Their commitment to patient data security has gained the trust of healthcare institutions worldwide, enabling valuable AI-driven insights without compromising patient privacy.
AI and Machine Learning have undoubtedly ushered in a new era of innovation in healthtech, promising improved patient care, faster diagnoses, and groundbreaking medical research. The five brands mentioned above illustrate the balance between innovation and security, setting the gold standard for responsible AI deployment in healthcare. As technology continues to advance, these brands serve as beacons, guiding the healthtech industry toward a future that respects patient privacy, complies with regulations, and harnesses the full potential of AI to revolutionize healthcare for the better.
Continued here:
Advancing Patient Care: 5 Brands Harnessing AI and Machine ... - Microbioz India
PAVE Expands AI Team with New Machine Learning Experts – Fagen wasanni
Vehicle inspection platform PAVE has hired four new machine learning experts to strengthen its artificial intelligence (AI) team. The recruits were sourced through the Vector Institute, an organization that collaborates with various sectors to enhance Canadian life through AI-based innovation.
The new hires, namely Abhishek Chandar, Roisul Islam Rumi, Shamisa Kaspour, and Vinitha Rajagopal Muthu, possess experience in AI, machine learning, and computer vision. PAVE utilizes AI and advanced machine learning, alongside human review, to build structured data and train its inspection algorithms for vehicle recognition and determining information during inspections.
PAVE was selected by the Vector Institute to participate in their FastLane Applied Projects program, which is focused on computer vision use cases. As part of the program, PAVE was matched with the four machine learning associates who are currently working with the company. The FastLane program supports AI innovation by providing access to talent and technical expertise, reducing the time and cost of recruiting, and promoting collaboration.
The machine learning team at PAVE is crucial in advancing its vehicle inspection platform. The new hires will contribute to enhancing this role by developing cutting-edge algorithms and implementing novel approaches for identifying damage on vehicles automatically.
In addition to expanding its AI team, PAVE has recently finalized partnerships with TRADE X, a B2B cross-border vehicle marketplace, and NCCI, a provider of risk resolution outsourcing solutions. These partnerships aim to bring greater transparency and efficiency to the vehicle inspection process for sellers, dealers, and consumers.
PAVEs commitment to AI innovation and collaborations positions it as a leading player in the evolving vehicle inspection market. For more news on PAVE and the changing landscape of vehicle inspections, please visit Autoremarketingcanada.com.
Read more here:
PAVE Expands AI Team with New Machine Learning Experts - Fagen wasanni
Tecton Partners with Google Cloud to Accelerate Machine Learning … – Fagen wasanni
Machine learning startup Tecton has entered into a strategic partnership with Google Cloud to make its Tecton Feature Platform available to Google Cloud users. The platform automates the process of collecting, preparing, managing, and updating high-quality data required for training machine learning models. It ensures that the models have access to real-time predictive and generative AI applications. Tectons partnership with Google Cloud will help solution providers speed up the development of machine learning models while keeping costs under control.
Tecton was founded in 2019 by the developers behind Ubers Michelangelo machine learning platform. The company has raised $160 million through multiple funding rounds. Its platform is used for various applications, such as pricing, customer scoring, recommendation engines, automated loan processing, and fraud detection systems. These applications involve making complex decisions at scale and with high reliability. Tectons platform automates the process of creating machine learning features that power these models.
Google Cloud offers its Vertex AI system for training and deploying machine learning models and customizing large language models. Its data processing infrastructure services like DataProc and BigQuery are also commonly used in machine learning projects. The Tecton platform serves as a connective fabric, integrating these systems to build production-ready ML features. It automates the entire ML feature lifecycle, from definition and data transformation to online serving and operational monitoring.
Using the Tecton platform helps developers build better machine learning models by leveraging high-quality data. By automating data transformation and management, ML systems can be deployed into production faster. The platform also provides enterprise management and collaboration features that are often missing in ML initiatives.
Solution providers and strategic service providers performing AI and machine learning development work can use the Tecton-Google Cloud combination to work more efficiently. This partnership offers advanced machine learning feature engineering capabilities and accelerates the building of machine learning applications. It provides solution providers with another option to help their customers succeed in their ML initiatives.
Read more here:
Tecton Partners with Google Cloud to Accelerate Machine Learning ... - Fagen wasanni
Evogene’s ChemPass AI Tech-Engine is Introduced with New … – PR Newswire
The new application, TargetSelector, streamlines target-protein discovery and enables researchers in various industries to identify novel targets for innovative products
REHOVOT, Israel, July 25, 2023 /PRNewswire/ --Evogene Ltd. (Nasdaq: EVGN) (TASE: EVGN), a leading computational biology company targeting to revolutionize life-science product discovery and development across multiple market segments, is proud to announce the latest addition to its ChemPass AI tech-engine a breakthrough technology for target-protein discovery. The integration of TargetSelector, a new application that streamlines target-protein discovery for active molecule identification, assists researchers in finding suitable target proteins for new products while reducing development time, resources and most importantly, increasing the probability of success.
Proteins play a fundamental role in a wide array of biological processes and serve as the primary targets for developing innovative therapeutics, ag-chemical, ag-biological, and other life science solutions. The precise identification of these protein targets is pivotal in advancing research and discovery across various domains, including pharmaceuticals, agriculture, and environmental applications.
The challenge of finding a target-protein that is novel, safe, and druggable from the thousands of proteins in a relevant organism is enormous. Leveraging predictive machine learning algorithms and genomic data, users gain valuable insights into product requirements such as homology, druggability, essentiality, and biological pathways, efficiently narrowing down the list of potential target-protein, thus optimizing the discovery process.
"ChemPass AI tech-engine is a cutting-edge platform for the identification of small molecules. The addition of the TargetSelector application now enables a broader scope of finding the optimal target-protein for these molecules," said Dr. Nir Arbel, CPO at Evogene. "Our subsidiary AgPlenus, which focuses on developing ag chemicals, will be the first to benefit from this new improvement, applying it to identify novel mechanismsof action for pesticides. I believe that this significant advancement in Evogene's ChemPass AI tech-engine, positions us to forge strategic partnerships with industry leaders, unlocking innovation, expediting product development, and delivering groundbreaking solutions that tackle pressing global challenges."
About ChemPass AI:
ChemPass AI tech engine is a cutting-edge computational platform for discovering and optimizing small molecules for various life-science products, such as therapeutics and ag-chemicals. Developed at the intersection of docking techniques and machine learning, ChemPass AI brings together the power of artificial intelligence, predictive biology, and molecular interactions to accelerate target-protein and active molecule discovery processes like never before.
ChemPass AIhas been trained on vast repositories of molecular data encompassing diverse chemical structures and biological targets. This wealth of knowledge empowers the platform to recognize intricate patterns, subtle interactions, and complex relationships between small molecules and their target-proteins. As a result, ChemPass AI can rapidly evaluate an organism's protein set (proteome) as well as billions of potential candidates, ranking them according to their likelihood of success and shortening the time needed to identify promising target-proteins and leads (small molecules).
About Evogene:
Evogene Ltd. (Nasdaq: EVGN) (TASE: EVGN) is a computational biology company leveraging big data and artificial intelligence,aiming to revolutionize the development of life-science based products by utilizing cutting-edge technologies to increase the probability of success while reducing development time and cost.
Evogene established three unique tech-engines - MicroBoostAI,ChemPass AIandGeneRator AI. Each tech-engineis focused on the discovery and development of products based on one of the following core components: microbes (MicroBoost AI), small molecules (ChemPass AI), and genetic elements (GeneRator AI).
Evogene uses its tech-engines to develop products through strategic partnerships and collaborations, and its five subsidiaries including:
For more information, please visit: http://www.evogene.com.
Forward-Looking Statements: This press release contains "forward-looking statements" relating to future events. These statements may be identified by words such as "may", "could", "expects", "hopes" "intends", "anticipates", "plans", "believes", "scheduled", "estimates", "demonstrates" or words of similar meaning. For example, Evogene and its subsidiaries are using forward-looking statement in this press release when it discusses TargetSelector's ability to assist researchers in finding suitable target proteins for new products while reducing development time, resources and increasing the probability of success, TargetSelector's ability to enable a broader scope of finding the optimal protein target for hit small molecules, AgPlenus' success in identifying novel mechanism of action pesticides, and ChemPass AI's ability to accelerate drug discovery processes by reducing the time and resources required. Such statements are based on current expectations, estimates, projections and assumptions, describe opinions about future events, involve certain risks and uncertainties which are difficult to predict and are not guarantees of future performance. Therefore, actual future results, performance or achievements of Evogene and its subsidiaries may differ materially from what is expressed or implied by such forward-looking statements due to a variety of factors, many of which are beyond the control of Evogene and its subsidiaries, including, without limitation, those risk factors contained in Evogene's reports filed with the applicable securities authority. In addition, Evogene and its subsidiaries rely, and expect to continue to rely, on third parties to conduct certain activities, such as their field-trials and pre-clinical studies, and if these third parties do not successfully carry out their contractual duties, comply with regulatory requirements or meet expected deadlines, Evogene and its subsidiaries may experience significant delays in the conduct of their activities. Evogene and its subsidiaries disclaim any obligation or commitment to update these forward-looking statements to reflect future events or developments or changes in expectations, estimates, projections, and assumptions.
Logo - https://mma.prnewswire.com/media/1947468/Evogene_Logo.jpg
Contact: Rachel Pomerantz Gerber Head of Investor Relations at Evogene [emailprotected] +972-8-9311901
SOURCE Evogene
Read the original:
Evogene's ChemPass AI Tech-Engine is Introduced with New ... - PR Newswire
The Global Machine Learning Chips Market is forecasted to grow by USD 22276.52 million during 2022-2027, accelerating at a CAGR of 30.91% during the…
ReportLinker
Global Machine Learning Chips Market 2023-2027. The machine learning chips market is forecasted to grow by USD 22276.52 million during 2022-2027, accelerating at a CAGR of 30.91% during the forecast period.
New York, July 24, 2023 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Machine Learning Chips Market 2023-2027" - https://www.reportlinker.com/p06478500/?utm_source=GNW The report on the machine learning chips market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors.The report offers an up-to-date analysis regarding the current market scenario, the latest trends and drivers, and the overall market environment. The market is driven by the increasing adoption of machine learning chips in data centers, growing investment in smart cities, and the development and integration of machine learning chips in autonomous vehicles.
The machine learning chips market is segmented as below:By End-user BFSI IT and telecom Media and advertising Others
By Technology System-on-chip (SoC) System-in-package Multi-chip module Others
By Geographical Landscape North America Europe APAC South America Middle East and Africa
This study identifies the increasing investments in semiconductors as one of the prime reasons driving the machine learning chips market growth during the next few years. Also, increasing investments in ai start-ups and growing adoption of socs in robotics will lead to sizable demand in the market.The report on the machine learning chips market covers the following areas: Machine learning chips market sizing Machine learning chips market forecast Machine learning chips market industry analysis
The robust vendor analysis is designed to help clients improve their market position, and in line with this, this report provides a detailed analysis of several leading machine learning chips market vendors that include Advanced Micro Devices Inc., Alphabet Inc., Baidu Inc., Broadcom Inc., Cerebras Systems Inc., Fujitsu Ltd., Graphcore Ltd., Huawei Technologies Co. Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., Microchip Technology Inc., NVIDIA Corp., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc.. Also, the machine learning chips market analysis report includes information on upcoming trends and challenges that will influence market growth. This is to help companies strategize and leverage all forthcoming growth opportunities.The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.The publisher presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters such as profit, pricing, competition, and promotions. It presents various market facets by identifying the key industry influencers. The data presented is comprehensive, reliable, and a result of extensive research - both primary and secondary. The market research reports provide a complete competitive landscape and an in-depth vendor selection methodology and analysis using qualitative and quantitative research to forecast the accurate market growth.Read the full report: https://www.reportlinker.com/p06478500/?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.
__________________________
Story continues