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researchdive · 2 years

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Know about Data Science Platform Market Influencing Factors by Top Companies

Over the last decade, data science has been rapidly progressing both as a technology and as a discipline. Best practices have been created by the leading businesses and it is now becoming part of the operational core for organizations. However, there is a need for a next step for product evolution in data science platform that supports and provides both business users an integrated solution for managing, building, and optimizing predictive models. Nowadays, data science platform is the most talked about topic in data science meet-ups, conferences, and top publications. According to a Research Dive analyst review, the concept of data science platform is not novel in the big data space but the need of data science platform in business is still unknown to many.

Download an Exlcusive PDF Sample of Data Science Platform Market@ https://www.researchdive.com/download-sample/77 Need for a Data Science Platform 1) To Enable Better Teamwork with Data Scientists If the data scientists are solving the same problem in several ways, the productivity will decrease as it won’t deliver effectual value to the organization. One of the best solutions to ensure effective teamwork with data scientists is to provide them with a centralized flexible platform and the required set of tools to work upon. By using a data science platform, it ensures that all the contributions of the data scientists i.e. data models, data visualizations, and code libraries exist in a single shared reachable location. This helps data scientists to reuse the code, facilitate better discussion around research projects, and share best practices to make data science easily scalable and less resource exhaustive. 2) Help Minimalize Engineering Effort With data science platforms, the data scientists get help in moving analytical models into production without any need of additional engineering effort or DevOps. For instance, if a company wants to build a product recommendation engine then the data scientist will require the efforts of a software engineer for testing, refining and integrating the data model before the users start seeing the product recommendations on the basis of their behavior. A data science platform makes sure that the data models are accessible behind an API so that the data scientists do not have to depend much on engineering efforts. 3) Help to Offload a Number of Low Value Tasks The burden of data scientists is released with the help of data science platforms. The burden of low value tasks such as reproducing past results, configuring environments for non-technical users, running reports, and scheduling jobs is offloaded from data scientists. 4) Facilitate Faster Research and Experimentation Data scientists do not have to deal with extra data management tasks, as data science platforms allow people to see what and how others are working on. Moreover, whenever there is a new hire in the data science team, the employee can quickly start working as it is easier to restore the work of the people who leave through a unified platform over various isolated tools.

Customize report as per your format and definition of Data Science Platform Market@ https://www.researchdive.com/request-for-customization/77 The Market Overview Currently, the global market for data science platform is progressing rapidly and is about to positively grow in the near future. According to the Research Dive report, the global data science platform market is projected to garner a revenue of $224.3 billion at a 31.1% CAGR from 2019 to 2026. This is majorly due to the growing adoption of analytical tools across the globe for learning the unobserved customer purchasing pattern. The key prominent players of the market are adopting several strategies such as product development along with many approaches such as collaborations and R&D activities to stand strong in the global market. The major players of the global data science market include Alphabet Inc. (Google), Databricks, Domino Data Lab, Inc., Civis Analytics, Dataiku, Cloudera, Inc., IBM Corporation, Anaconda, Inc., Microsoft Corporation., and Altair Engineering, Inc.

#Data Science Platform #Data science Platform market #Data Science #Data Security #Data Analytics #Software and Services #Cyber security #Cyber attack

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poojascmi · 2 years

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Data science platform offers a flexible environment, enables organizations to incorporate data-driven decisions into operational and customer-facing systems to enhance business outcomes, and improve the customer experience.

Explore complete report-https://cmiblogdailydose.blogspot.com/2022/07/a-data-science-platform-is-type-of.html

#Data science platform #Data science platform market #data science #technology

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tagapagsalaysay · 1 year

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Irene and Stanley Attempt Data Analytics by Hand

#post #doodles #irene #Sucks that this doesnt fit the platform it was intended to be in #Also umm im not arguing about semantics of stat vs data science/analytics whatever on a joke comic #Had to reupload because i was like ughhhhh fuck off i know I drew a statistical process Fuck this nerd shit.#mom i want stanley narrator /n the stanley narrator we have at home #comix

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aainaalyaa · 1 year

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“Les données sont suffisantes pour être lues. Vous n'avez pas besoin de contrôler les masses — Après un certain temps, il devient rassis. Si vous devez créer une plateforme, construisez-la pour les gens, pour la communauté, pas pour le premier million, pas pour votre contrôle, pas pour le renseignement. C'est pourquoi Tik Tok et Pinterest ont remporté la partie .

— Mle. AainaA-Ridtz A R, Saat Saat Terakhir

#Renseignement #Intelligence #TikTok #Pinterest #Community #Platform #Builders #Social Spirit #Saat saat Terakhir #Catatan #citations de livres #aainaa ridtz #Social Community #Big Data #Data Intelligence #Data Science

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analyticspursuit · 1 year

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SQL, or Structured Query Language, is an essential skill for professionals in the tech industry. It is used to manage and manipulate data in databases and is a common requirement for data analysis, software development, and business intelligence jobs.

With the increasing demand for professionals with SQL skills, it is essential to have a solid understanding of the language and to practice regularly. Many coding platforms are available for practicing SQL, each with its features and exercises.

Throughout this article, we will discuss these platforms and highlight the pros and cons of each, as well as provide examples of exercises that can be completed on each platform. By the end of this article, you'll better understand which platform best suits your learning style and needs.

#sql #sql beginner #sql platforms #coding platforms #data science #sql skills #improve your sql skills

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cuelebre-sweden · 2 years

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Cuelebre: Increase Your Business ROI With AI-Powered Data Analytics

We, Cuelebre is the Best AI & Data Engineering Services Company in Sweden. Improve your business strategy with help of AI & Data Analytics

Strategic Consultation

Data & Platform Engineering

Data Science AI/ML Models

Advanced Business Analytics

DataOps and Maintenance

#data analytics #AI-Powered Data Analytics #Business ROI #Strategic Consultation #Data & Platform Engineering #Data Science AI/ML Models #Advanced Business Analytics #DataOps and Maintenance

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jcmarchi · 8 days

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Kamal Ahluwalia, Ikigai Labs: How to take your business to the next level with generative AI - AI News

New Post has been published on https://thedigitalinsider.com/kamal-ahluwalia-ikigai-labs-how-to-take-your-business-to-the-next-level-with-generative-ai-ai-news/

Kamal Ahluwalia, Ikigai Labs: How to take your business to the next level with generative AI - AI News

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AI News caught up with president of Ikigai Labs, Kamal Ahluwalia, to discuss all things gen AI, including top tips on how to adopt and utilise the tech, and the importance of embedding ethics into AI design.

Could you tell us a little bit about Ikigai Labs and how it can help companies?

Ikigai is helping organisations transform sparse, siloed enterprise data into predictive and actionable insights with a generative AI platform specifically designed for structured, tabular data.

A significant portion of enterprise data is structured, tabular data, residing in systems like SAP and Salesforce. This data drives the planning and forecasting for an entire business. While there is a lot of excitement around Large Language Models (LLMs), which are great for unstructured data like text, Ikigai’s patented Large Graphical Models (LGMs), developed out of MIT, are focused on solving problems using structured data.

Ikigai’s solution focuses particularly on time-series datasets, as enterprises run on four key time series: sales, products, employees, and capital/cash. Understanding how these time series come together in critical moments, such as launching a new product or entering a new geography, is crucial for making better decisions that drive optimal outcomes.

How would you describe the current generative AI landscape, and how do you envision it developing in the future?

The technologies that have captured the imagination, such as LLMs from OpenAI, Anthropic, and others, come from a consumer background. They were trained on internet-scale data, and the training datasets are only getting larger, which requires significant computing power and storage. It took $100m to train GPT4, and GP5 is expected to cost $2.5bn.

This reality works in a consumer setting, where costs can be shared across a very large user set, and some mistakes are just part of the training process. But in the enterprise, mistakes cannot be tolerated, hallucinations are not an option, and accuracy is paramount. Additionally, the cost of training a model on internet-scale data is just not affordable, and companies that leverage a foundational model risk exposure of their IP and other sensitive data.

While some companies have gone the route of building their own tech stack so LLMs can be used in a safe environment, most organisations lack the talent and resources to build it themselves.

In spite of the challenges, enterprises want the kind of experience that LLMs provide. But the results need to be accurate – even when the data is sparse – and there must be a way to keep confidential data out of a foundational model. It’s also critical to find ways to lower the total cost of ownership, including the cost to train and upgrade the models, reliance on GPUs, and other issues related to governance and data retention. All of this leads to a very different set of solutions than what we currently have.

How can companies create a strategy to maximise the benefits of generative AI?

While much has been written about Large Language Models (LLMs) and their potential applications, many customers are asking “how do I build differentiation?”

With LLMs, nearly everyone will have access to the same capabilities, such as chatbot experiences or generating marketing emails and content – if everyone has the same use cases, it’s not a differentiator.

The key is to shift the focus from generic use cases to finding areas of optimisation and understanding specific to your business and circumstances. For example, if you’re in manufacturing and need to move operations out of China, how do you plan for uncertainty in logistics, labour, and other factors? Or, if you want to build more eco-friendly products, materials, vendors, and cost structures will change. How do you model this?

These use cases are some of the ways companies are attempting to use AI to run their business and plan in an uncertain world. Finding specificity and tailoring the technology to your unique needs is probably the best way to use AI to find true competitive advantage.

What are the main challenges companies face when deploying generative AI and how can these be overcome?

Listening to customers, we’ve learned that while many have experimented with generative AI, only a fraction have pushed things through to production due to prohibitive costs and security concerns. But what if your models could be trained just on your own data, running on CPUs rather than requiring GPUs, with accurate results and transparency around how you’re getting those results? What if all the regulatory and compliance issues were addressed, leaving no questions about where the data came from or how much data is being retrained? This is what Ikigai is bringing to the table with Large Graphical Models.

One challenge we’ve helped businesses address is the data problem. Nearly 100% of organisations are working with limited or imperfect data, and in many cases, this is a barrier to doing anything with AI. Companies often talk about data clean-up, but in reality, waiting for perfect data can hinder progress. AI solutions that can work with limited, sparse data are essential, as they allow companies to learn from what they have and account for change management.

The other challenge is how internal teams can partner with the technology for better outcomes. Especially in regulated industries, human oversight, validation, and reinforcement learning are necessary. Adding an expert in the loop ensures that AI is not making decisions in a vacuum, so finding solutions that incorporate human expertise is key.

To what extent do you think adopting generative AI successfully requires a shift in company culture and mindset?

Successfully adopting generative AI requires a significant shift in company culture and mindset, with strong commitment from executive and continuous education. I saw this firsthand at Eightfold when we were bringing our AI platform to companies in over 140 countries. I always recommend that teams first educate executives on what’s possible, how to do it, and how to get there. They need to have the commitment to see it through, which involves some experimentation and some committed course of action. They must also understand the expectations placed on colleagues, so they can be prepared for AI becoming a part of daily life.

Top-down commitment, and communication from executives goes a long way, as there’s a lot of fear-mongering suggesting that AI will take jobs, and executives need to set the tone that, while AI won’t eliminate jobs outright, everyone’s job is going to change in the next couple of years, not just for people at the bottom or middle levels, but for everyone. Ongoing education throughout the deployment is key for teams learning how to get value from the tools, and adapt the way they work to incorporate the new skillsets.

It’s also important to adopt technologies that play to the reality of the enterprise. For example, you have to let go of the idea that you need to get all your data in order to take action. In time-series forecasting, by the time you’ve taken four quarters to clean up data, there’s more data available, and it’s probably a mess. If you keep waiting for perfect data, you won’t be able to use your data at all. So AI solutions that can work with limited, sparse data are crucial, as you have to be able to learn from what you have.

Another important aspect is adding an expert in the loop. It would be a mistake to assume AI is magic. There are a lot of decisions, especially in regulated industries, where you can’t have AI just make the decision. You need oversight, validation, and reinforcement learning – this is exactly how consumer solutions became so good.

Are there any case studies you could share with us regarding companies successfully utilising generative AI?

One interesting example is a Marketplace customer that is using us to rationalise their product catalogue. They’re looking to understand the optimal number of SKUs to carry, so they can reduce their inventory carrying costs while still meeting customer needs. Another partner does workforce planning, forecasting, and scheduling, using us for labour balancing in hospitals, retail, and hospitality companies. In their case, all their data is sitting in different systems, and they must bring it into one view so they can balance employee wellness with operational excellence. But because we can support a wide variety of use cases, we work with clients doing everything from forecasting product usage as part of a move to a consumption-based model, to fraud detection.

You recently launched an AI Ethics Council. What kind of people are on this council and what is its purpose?

Our AI Ethics Council is all about making sure that the AI technology we’re building is grounded in ethics and responsible design. It’s a core part of who we are as a company, and I’m humbled and honoured to be a part of it alongside such an impressive group of individuals. Our council includes luminaries like Dr. Munther Dahleh, the Founding Director of the Institute for Data Systems and Society (IDSS) and a Professor at MIT; Aram A. Gavoor, Associate Dean at George Washington University and a recognised scholar in administrative law and national security; Dr. Michael Kearns, the National Center Chair for Computer and Information Science at the University of Pennsylvania; and Dr. Michael I. Jordan, a Distinguished Professor at UC Berkeley in the Departments of Electrical Engineering and Computer Science, and Statistics. I am also honoured to serve on this council alongside these esteemed individuals.

The purpose of our AI Ethics Council is to tackle pressing ethical and security issues impacting AI development and usage. As AI rapidly becomes central to consumers and businesses across nearly every industry, we believe it is crucial to prioritise responsible development and cannot ignore the need for ethical considerations. The council will convene quarterly to discuss important topics such as AI governance, data minimisation, confidentiality, lawfulness, accuracy and more. Following each meeting, the council will publish recommendations for actions and next steps that organisations should consider moving forward. As part of Ikigai Labs’ commitment to ethical AI deployment and innovation, we will implement the action items recommended by the council.

Ikigai Labs raised $25m funding in August last year. How will this help develop the company, its offerings and, ultimately, your customers?

We have a strong foundation of research and innovation coming out of our core team with MIT, so the funding this time is focused on making the solution more robust, as well as bringing on the team that works with the clients and partners.

We can solve a lot of problems but are staying focused on solving just a few meaningful ones through time-series super apps. We know that every company runs on four time series, so the goal is covering these in depth and with speed: things like sales forecasting, consumption forecasting, discount forecasting, how to sunset products, catalogue optimisation, etc. We’re excited and looking forward to putting GenAI for tabular data into the hands of as many customers as possible.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: data, ethics, generative ai, Ikigai Labs, llm

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bbindemand · 15 days

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The Future of B2B Platforms: Machine Learning and Data Science in Online Wholesale Sales

Description:

👉Introduction to B2B Platforms:

B2B platforms serve as digital marketplaces where businesses can buy and sell goods and services.Traditionally, these platforms have facilitated transactions between businesses in a straightforward manner.

👉he Role of Machine Learning:

Machine learning algorithms have the ability to analyse vast amounts of data and extract valuable insights.In the context of B2B platforms, these algorithms can be used to identify patterns in purchasing behaviour, predict future demand, and personalize the user experience.

👉Leveraging Data Science:

Data science encompasses a range of techniques and tools for extracting knowledge and insights from data. In the realm of B2B platforms, data science can be applied to optimize pricing strategies, improve inventory management, and enhance supply chain efficiency.

👉Personalization and Customization:

One of the key benefits of machine learning and data science on B2B platforms is the ability to personalize the user experience. By analyzing data on customer behaviour and preferences, platforms can deliver tailored recommendations, product suggestions, and promotional offers.

👉Predictive Analytics:

Predictive analytics algorithms use historical data to forecast future trends and outcomes. In the context of B2B platforms, these algorithms can help businesses anticipate demand fluctuations, identify potential risks, and optimize inventory levels.

👉Enhanced Decision-Making:

Machine learning and data science empower businesses to make data-driven decisions across all aspects of their operations. From pricing and inventory management to marketing and customer service, these technologies provide actionable insights that enable businesses to stay agile and responsive in a rapidly evolving marketplace.

👉Streamlined Procurement Processes:

For buyers, B2B platforms powered by machine learning and data science offer streamlined procurement processes and greater transparency. Through features such as automated sourcing, real-time inventory tracking, and predictive reorder capabilities, buyers can optimize their purchasing decisions and ensure a seamless experience from order placement to delivery.

👉Driving Growth and Innovation:

Ultimately, the integration of machine learning and data science into B2B platforms is driving growth and innovation in the online wholesale sales space. By harnessing the power of these technologies, businesses can unlock new revenue streams, improve operational efficiency, and gain a competitive edge in the digital marketplace.

In conclusion, the future of B2B platforms lies in harnessing the potential of machine learning and data science to revolutionize online wholesale sales. By leveraging these advanced technologies, businesses can deliver personalized experiences, optimize decision-making processes, and drive growth in the dynamic world of

Description:

👉Introduction to B2B Platforms:

👉he Role of Machine Learning:

👉Leveraging Data Science:

👉Personalization and Customization:

👉Predictive Analytics:

👉Enhanced Decision-Making:

👉Streamlined Procurement Processes:

👉Driving Growth and Innovation:

#B2B commerce #B2B platforms #Machine Learning #Data Science

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adroit--2022 · 1 year

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#adroit market research #data science platform #data science platform 2020 #data science platform trends

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aishavass · 1 year

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Rising need to extract insights from huge volumes of unstructured and structured data is the major factor driving the demand for the data science platform...

#adroit market research #data science platform #data science platform 2020 #data science platform trends

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elucidata · 2 months

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The Lone-wolf Scientist Building Data Platforms

The expertise to build such tools rarely exists with the discovery scientists themselves. They are usually hacky scripters who can get (almost) all analysis done. But building complex software is a different ball game.

Source Link

#Science #Data #Platform #Technology

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researchdive · 2 years

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Data Science Platform Market Will Reflect Significant Growth Prospects of US$224.3 billion during 2017-2026

Data science platform market is estimated to rise with a CAGR of 31.1% by generating a revenue of $224.3 billion by 2026. Asia-Pacific holds the highest growth rate, expecting to reach $80.3 billion during the forecast period.

Data science is the preparation, extraction, visualization, and maintenance of information. Data science uses scientific methods and processes to draw the outcomes from the data. With the help of data science tools and practices one can recognize the data patterns. The person dealing with data science tools and practices uses meaningful insights from the data to assist the companies to take the necessary decision. Basically, data science helps the system to function smarter and can take autonomous decisions based on historical data.

Request FREE PDF Sample of Data Science Platform Market@ https://www.researchdive.com/download-sample/77

Many companies have a large set of data that are not being utilized. Data science is majorly used as a method to find specific information from a large set of unstructured and structured data. Concisely, data science is a vast and new field which helps to build, asses and control the data by the user. These analytical tools help in assessing business strategies and taking decisions. The rising use of data analytics tools in data science is considered to be major driving factor for the data science platform market.

Data science is mostly used to find hidden information from the data so that business decisions and strategies can be conceived. If the data prediction goes wrong, business has to face a lot of consequences. Therefore, professional expertise are required to handle the data carefully. But as the data science platform is new, the availability of the workforce with relevant experience is considered to be the biggest threat to the market.

Grow Your Business Globally, Ask to Analyst Specific Requirements on Data Science Platform Market@ https://www.researchdive.com/connect-to-analyst/77

Service type is predicted to have the maximum growth rate in the estimated period. Service segment is projected to grow at a CAGR of 32.0% by generating a revenue of $76.0 billion by 2026. Increasing difficulties in terms of operational work in many companies and rising use of Business Intelligence (BI) tools are predicted to be major drivers for the service type segment.

Manufacturing is predicted to have the highest growth rate in the forecast period. Data scientists have acquired a key position in the manufacturing industries. Data science is being broadly used for increasing production, reducing the cost of production and boosting profit in manufacturing area. Data science has also helped the companies to predict potential problems, monitor the work and analyze the flow of work in the manufacturing work area. Manufacturing segment is expected to grow at a CAGR of 31.9% and is predicted to generate a revenue of $43.28 billion by 2026.

North Americas has the largest market size in 2018. North America market is predicted to grow at a CAGR of 30.1% by generating a revenue of $80.3 billion by 2026. The presence of large number of multinational companies and rising use of data with the help of analytical tools in these companies gives a boost to the market in this region. Asia-Pacific region is predicted to grow at a CAGR of 31.9% by generating a revenue of $48.0 billion by 2026. Asia-Pacific is accounted to have the highest growth due to increasing investments by companies and the increased use of artificial intelligence, cloud, and machine learning.

Customize Data Science Platform Market Report as per your Format & Definition@ https://www.researchdive.com/request-for-customization/77

The major key players in the Data Science Platform Marketare

Microsoft Corporation

Altair Engineering, Inc.

IBM Corporation

Anaconda, Inc.

Cloudera, Inc.

Civis Analytics

Dataiku

Domino Data Lab, Inc.

Alphabet Inc. (Google)

Databricks among others.

#Data Science Platform Market #Data Science Platform #Data Science #Data Analytics #Data Science Management

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