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sprinkledata12 · 1 year

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Top 30 Data Analytics Tools for 2023

Top 30 Data Analytics Tools

Data is the new oil. It has become a treasured commodity today for data analytics and has taken on a serious status. With the daily growing data volume, it is now at a scale that no human can deal with the amount manually. Businesses worldwide have found growth in their organizations by incorporating data analytics into their existing technology platforms.

The concept of data analytics has evolved over time and will continue to rise. Data analytics has become an important part of managing a business today, where every business owner wants their business to grow and increase its revenue in order to maintain a competitive edge in this ever-changing marketplace, they need to be able to use data effectively.

What is Data Analytics?

Data analytics is the science of studying raw data with the intent of drawing conclusions from it. It is used in multiple industries to allow companies and organizations to make more promising data-driven business decisions.

Data analytics covers an entire spectrum of data usage, from collection to analysis to reporting. Understanding the process of data analytics is the ultimate power and it will be the future of almost every industry.

There are multiple types of data analytics including descriptive, diagnostic, predictive, and prescriptive analytics.

Let’s learn about the different types of data analytics in detail.

‍Types of Data Analytics:

Descriptive Data Analytics:

Descriptive data analytics is the process of examining data to summarize what is actually happening. It provides a basic understanding of how the business operates and helps to identify which factors are affecting the business and which aren't. It supports the exploration and discovery of insights from your existing data and based on that provides a basic understanding of your business.

Diagnostic Data Analytics:

Diagnostic Data Analytics is used to diagnose any business problems. It generally answers the question: why did it happen? Data can be examined manually, or used by an automated system to generate a warning. Diagnostic data analytics is an advanced analytical approach that is used to find the cause of the problem faced by a business.

Predictive Data Analytics:

Predictive data analytics is a form of analytics that uses both new and historical data to forecast activities, behavior, and trends. It is used to analyze current data to make predictions about future events. One important use case for predictive analysis is to help retailers understand customer buying patterns and optimize inventory levels to maximize revenues.

Prescriptive Data Analytics:

Prescriptive data analytics is the last level of analytics that is performed on the outcome of other types of analytics. It is the process of defining an action based on descriptive and predictive data analytics results. In this stage, different scenarios are analyzed to determine how each scenario is likely to play out given past data. This can help businesses know what action to take for a good outcome.

These four types of data analysis techniques can help you find hidden patterns in your data and make sense of it. All these types of data analytics are important in other ways and can be used in different business scenarios.

Importance of Data Analytics:

Data analytics is extremely important for any enterprise and has become a crucial part of every organization's strategy in the past decade. The reason for this is simple: Big data has opened up a world of opportunities for businesses. Data analysts have become essential in helping companies process their huge sets of data for making meaningful decisions.

The benefits offered by analyzing data are numerous some of them are mentioned below:

It helps businesses to determine hidden trends and patterns.

Improves efficiency and productivity of the business by helping them to take data-driven decisions.

Identifies weaknesses and strengths in the current approach.

Enhances decision-making, which helps businesses to boost their revenue and helps solve business problems.

It helps to perform customer behavior analysis accurately to increase customer satisfaction

Data analytics lets you know what is working and what can improve. According to experts, the lack of data analysis and usage can result in failed business strategies and also cause loss of customers. So in order to take your business to the next level, one must always adopt data analytics techniques and should be familiar with the steps involved in it.

Data Analysis Process: Steps involved in Data Analytics

Steps in data analytics are a set of actions that can be performed to create useful and functional data. In this section, we will detail the stages involved in data analytics.

Understanding Business Requirements

One of the most important factors behind successful data analysis is a proper understanding of the business requirements. An analyst needs to have a clear idea about what kind of problem the business is facing and what can be done to overcome the problem. The other important task is to understand what type of data needs to be collected to solve the given problem.

Collecting Data

When it comes to data analytics, it is very important that the right kind of data is collected. After understanding the business problem the analyst should be aware of the type of data to be collected to solve the problem. Data can be collected in many ways, including survey forms, interviews, market research, web crawlers, log files, event log files, and even through social media monitoring apps.

Data wrangling

In data wrangling, data is cleaned and managed so that it can be utilized in order to perform data analysis. This process can involve converting data from one format to another, filtering out invalid or incorrect data, and transforming data so that it can be more easily analyzed. Data wrangling is an important step in data analysis because it can help ensure that the data used in the analysis is of high quality and is in a suitable format.

There are many steps involved in data wrangling, including

1. Gathering data from a variety of sources.

2. Cleaning and standardizing the data.

3. Exploring the data to identify patterns and relationships.

4. Transforming the data into a format that can be used for different tasks.

5. Saving the wrangled data in a format that can be easily accessed and used in the future.

The steps involved in data wrangling can vary depending on the type and format of data you are working with, but the final goal is always the same, to transform raw data into a format that is more useful for performing accurate analysis.

Exploratory Data Analysis (EDA):

Exploratory Data Analysis (EDA) is a statistical approach used to achieve insights into data by summarizing its major features. This procedure is used to comprehend the data’s distribution, outliers, trends, and other factors. EDA can be used to select the best-fitting statistical models and input variables for a dataset.

A typical EDA process might begin with a series of questions, such as

What are the primary components of the dataset?

What are the most significant variables?

Are there any outliers or unusual observations or behaviors?

After asking these basic questions, the analyst should then investigate the data visually, using charts such as histograms, scatter plots, and box plots. These visual methods can help to identify features such as trends, and unusual observations. This process of EDA can help to reveal important insights into the data, and can be used to guide further analysis.

EDA can provide insights that may not be obvious from merely looking at the data itself. Overall, it is an essential tool for data analysis and should be used whenever possible.

Communicating Results:

Communicating results is the final and the most vital aspect of the data analysis life cycle because it allows others to understand the conclusions of the analysis. Results also need to be communicated in a clear and concise way so they can be easily understood by people with less technical acumen as well. Additionally, conveying results allows for feedback and discussion to improve the quality of the findings during the analysis procedure.

The data analytics life cycle generally goes through these five-step procedures that help to find precise conclusions. But apart from the benefits, some challenges are faced during the data analytics process.

Overall Challenges in Data Analysis:

There can be many types of challenges encountered during the data analysis journey but the two most common challenges are mentioned below:

Data issues

Data analysis-related issues.

1. Data Issues:

Data-related problems are one such type of issue encountered during the data analysis journey. Some data-related issues are mentioned below:

Incorrect or inaccurate data

Incomplete data

Data that is not timely ingested

Unorganized data

Irrelevant data

Data integration issues

Handling large datasets

The data team needs to guarantee to provide the correct data and a good and reliable data integration platform should be preferred to ensure correct and timely ingestion of data. A proper ETL tool that provides safe and secure data storage should be selected.

2. Data Analysis Related Issues:

The data analysis process can be challenging if the data is not well-organized, some challenges are mentioned below:

Absence of skills to interpret data.

Data cleaning and preparation can be very time-consuming.

Choosing the right statistical method can be a challenge.

The results of the analysis can be misinterpreted.

Communicating the results in a simpler way can be tough

To overcome these challenges businesses should use low-code data analytics platforms that will help to save manpower and thus reduce costs. With careful planning and execution, one can easily perform analysis without any hassles. By using the right tools and techniques, businesses can overcome these challenges and make better data-driven decisions.

Need for Data Analysis Tools:

In a world where data is continuously being generated, it is becoming hard to make sense of it all without the help of data analysis tools.

There are many reasons why we need data analysis tools. They help us to process, understand, and make use of data effectively. Data analysis tools help us to see patterns and trends in data without actually coding. Nowadays, businesses don't need a highly skilled person to perform the data analysis process in fact they can perform the analysis on their own because of the tools present in the market.

The data analysis tools in the market can also help to enhance communication and collaboration within your organization through alerts and email functionalities. In some cases, they can also help to automate decision-making processes.

Criteria For Choosing the Right Data Analysis Tool:

There is a wide variety of data analysis tools available in the market but the best-fitted tool for you will depend on the specific data set and the desired business outcome. When choosing a data analysis tool, it is essential to assess the specific features and capabilities of the tool, and the user’s needs should also be considered. For example, if you are looking to perform complex statistical analysis, then a statistical software package would be the best choice. On the other hand, if you are looking to create interactive dashboards, then a no-code data analytics platform would be a more suitable fit.

Below listed are some criteria that one should consider before choosing the right data analytics platform according to the requirements.

1. No-code Data Analytics Platform:

No-code data analytics platforms equip users with the capability to quickly analyze data with ease without having to write even a single line of code. This can save users a lot of time and effort by making data analysis more streamlined.

Some benefits provided by such data platforms are mentioned below:

No technical skills required: Analysis of data on these types of platforms can be performed by users of all skill types and different experience levels. Data analysis is made more accessible to individuals which allows them to benefit from it.

Supports Different Data types: Wide variety of data can be analyzed be it structured or unstructured, which makes these platforms more versatile.

Easy Integration: Easy integration with different sources is one of the best features provided by no-code data platforms.

Flexible pricing plans: No-code platforms provide scalability and are proven to be very cost-effective. This feature makes them useful for businesses of all sizes and stature.

If you are looking for a good and reliable no-code data analytics platform that has all these features then Sprinkle Data is the best option.

2. Availability of Different Types of Charts:

Charts can help to picture data, and spot trends and patterns effortlessly. They help to make intricate data more coherent and can help individuals to make better decisions. Charts used with proper statistical techniques can be useful in making predictions about future behavior as well. They also can be used to interpret and find relationships between different variables and are useful in finding outliers in data. Different types of charts can be used to perform accurate analysis, some important chart types include:

Bar/column charts are one of the most typically used chart types and are especially helpful in comparing data points.

Line charts are used for depicting changes over time.

Pie charts are helpful in determining proportions across various categories

Scatter plots are useful for visualizing relationships between two numerical data points and are primarily used to identify trends and outliers in data.

Histograms are used to give information about the data distribution.

An area chart is based on a line chart and is primarily used to depict quantitative data by covering the area below the line.

Combo Chart is a combination of a line and a bar chart that depicts trends over time.

Funnel charts help to portray linear processes with sequential or interconnected phases in the analysis.

A map is a geographical chart type used to visualize data point density across different locations.

A stacked bar chart is a form of bar chart depicting comparisons of different data categories.

Charts are an integral part of any data analytics tool and can add meaning to the analysis. They help to communicate the conclusions of the analysis in a concise manner. So always choose a data analysis tool that has these charts with additional attributes like labels, a benchmark value, and different colors to easily differentiate.

All the chart types mentioned above are available in the Sprinkle Data analytics tool accessible with just a single click.

3. Dashboard With a Good Visual Interface

A dashboard is a visual user interface that provides easy access to key metrics and consists of a sequence of charts, tables, and other visual elements that can be customized and systematized to provide insights into specific datasets with advantages like delivering visibility into an organization's performance in real time.

The key features that a dashboard should contain are mentioned below:

Interactivity: Dashboards with good interactivity permit users to filter and drill down into data for more detailed analysis.

Easily Editable layout: Customized dashboard show only the data that is relevant to the analysis.

Easy to share: Dashboards that can be easily shared with others to explore and analyze the data.

Less Runtime: A data analytics platform whose Dashboards take less time to run should be picked.

Monitoring: In case of a dashboard failure proper email alerts should be provided to the user with the reason for the error.

User-Friendly Interface: A dashboard with a user-friendly interface like drag and drop functionality is easy to use.

Live Dashboard: If you need to track data in real-time a live dashboard is the best option for your business.

If you are confused about which data analytics platform should be preferred to get all these features then you should prefer Sprinkle Data.

The best dashboard for your needs is the one that must follow all these criteria and will depend on the type of data you need to track, and the level of detail you need to acquire.

4. Cost Efficient:

A cost-effective data analytics platform helps to save money on software and hardware. These tools can help organizations save money in a number of ways. By enabling organizations to understand their data better, these tools can help to recognize zones where costs can be decreased. Moreover, a platform with flexible and scalable pricing plans should be adopted to pay a reasonable price according to the requirements.

Sprinkle Data has a flexible pricing plan that is fully customizable according to the needs of users enabling them to save costs while performing high-level analytics.

Ultimately, the best way to choose the right data analysis tool is to consult with experts in the field and try different tools to see which one works best for your specific needs.

Read More Here to know Top 30 Data Analytics Tools for 2023 :https://www.sprinkledata.com/blogs/data-analytics-tools

#etl tools #big data tools

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1stepgrow · 1 year

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Reasons to learn Data Science

Data science has become the backbone of the all the companies and industries like healthcare, finance, and more. Every company started to hire Data Scientists and Analysts nowadays because of the evolution of Data Science in all sectors. It is a new platform in the market, and people are learning to get better opportunities. Here this great infographic design shows the top 5 reasons to learn Data Science. For more information, please visit: 1stepGrow

#data science course #data science #online courses #data science online #big data tools #big data technology #data analytics #machine learning #Deep Learning

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21kworldschool · 2 years

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The Evolution Of Technology In Virtual Classroom

Technology and the possibility of a completely digital classroom have swiftly become significant components of contemporary education. The concept of online education had already started to take off before the recent coronavirus pandemic. Nearly 35% of college students said they took at least one course online in 2018 while enrolled in regular classes. The adaptability of online learning might offer tempting advantages in education.

Education has always had boundless possibilities. However, because of the widespread adoption of education technology and the realization of its benefits, everyone with an internet connection now has access to a vast array of educational resources.

The dominance of technology in education is here to remain, even though more schools, universities, and other educational institutions will soon reopen and help us all resume some sense of routine.

Here are some of the latest tech trends in the education section and how schools can incorporate them.

AR and VR

Augmented Reality and Virtual Reality in EdTech are technologies that create a truly immersive learning experience. They provide close virtual interaction between students and the subject of study, assisting students in developing a real-world understanding of the subject.

While schools can take the help of VR to create a realistic representation of the idea, AR enables learners to visualize it for greater comprehension. Students can utilize AR and VR to visually examine the human digestive system, bridging the gap between theory and practice.

The ideal fusion of sounds, visuals, and animations makes for an outstanding educational experience.

Artificial Intelligence

Artificial intelligence (AI) in education technology (EdTech) uses intelligent machines to analyze learner response patterns and offer each person a customized learning solution. It is a cutting-edge strategy that is very helpful for teachers and pupils. EdTech platforms can use AI to automate teaching and simplify learning for students.

Teachers can use this technology to examine and mark assignments, identify error patterns, design the required courses, and track individual student performance. They can also curate problem-specific solutions to ensure each student receives individualized attention.

Gamification

Gamification uses game design and game features in educational software to help students study pleasantly and interestingly. It shows how learning may be interactive and challenges the status quo of the traditional classroom approach.

The way that students interact with learning materials has been revolutionized by EdTech companies using the power of gaming.

Every time they provide the correct answer, students are rewarded with points or stars, which increases their motivation to learn. Additionally, it both simplifies and constructively challenges learning.

Institutions and educators can use online resources and digital tools for this purpose. These websites and applications allow teachers to design individualized e-learning sessions that take the shape of entertaining video games and quizzes.

Online Courses and MOOCs

A unique e-learning paradigm known as a "Massive Open Online Course" (MOOC) involves the delivery of a free online course to an infinite number of students. It can come from examinations, computerized readings, and recorded video lectures.

Similarly, individuals can learn about various topics through paid and free online courses, broadening their knowledge beyond the curriculum.

Online courses and MOOCs stand out because they give students adequate time to complete their coursework at their own pace and comfort level. Learners can also benefit from a personalized learning environment and online assessments to monitor their progress.

These e-learning approaches are advantageous to educators since they can persuade students to enroll in online courses connected to their teaching in school.

Big Data and Analytics

Students and teachers generate a wealth of data on user engagement and behavior patterns businesses may utilize to cut expenses and develop strategies.

Big Data tools can assist in organizing the abundance of information into files and dashboards that are simple to read. Schools and colleges can utilize this information to examine the current circumstance quickly, check automatic reports, and develop a cost-effective enrollment structure.

The competent authorities might also look at the students' learning habits to determine who needs extra help or financial assistance through sponsorships. Additionally, it promotes alumni involvement for the institution's reputation.

They say that change is a constant, and technological advancements are no different. And various innovations can be introduced into the educational system to enhance students' growth and learning. These innovations produce a trend that eventually improves teaching and learning methods.

#education system #big data tools #online courses #MOOC #Artificial Intelligence #Virtual Classroom

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revglue · 2 years

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Now big data is embedded in nearly all verticals, from retail to automobile, and heavily invested in marketing as it improves decision making and creating solutions.

How Affiliates should use big data to yield better results for their projects or campaigns?

Go to this guide: https://www.revglue.com/blog-detail/121-how-big-data-is-affecting-affiliate-marketing

#big data #big data tools #big data analytics #affiliate marketing #marketing #use of big data #big data use #big data usage

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techsevi · 2 years

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Big Data क्या है? अर्थ, प्रकार, उपयोग, फायदे, नुकसान

आज लगभग हर क्षेत्र में Artificial Intelligence (AI), Machine Learning (ML) और Data Science जैसी तकनीकों का इस्तेमाल हो रहा है। और इसके लिए बड़ी मात्रा में डाटा (Big Data) का उपयोग किया जा रहा है। लेकिन सवाल यह है कि यह Big Data आखिर है क्या? What is big data? और यह कैसे काम करता है? साथ ही इसे कैसे और कहाँ से प्राप्त किया जाता है? और इसका उपयोग क्या है? आइए, विस्तार से जानते हैं। Big Data (बिग…

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cbirt · 5 months

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TBtools, a toolkit used for data analysis and bioinformatics tasks, was released in 2020 and quickly found a large audience – thousands of researchers worldwide adopted it, and it has been cited more than 5000 times in the three years it has been operational. A new upgrade, TBtools-II, has now been developed, with more than 100 new features to make bioinformatics analysis easier than ever.

In recent years, bioinformatics analysis has become a mainstay of academic research worldwide – with new advances in biotechnology, it has become possible to extract a large amount of biological data from various sources. While this data is often instrumental in uncovering new insights, the quantity makes it impossible to analyze. Further, the variety of bioinformatics tools needed to clean and process the data as required can be numerous and daunting, not least because of different tasks requiring entirely different workflows. Valuable time is lost due to researchers being forced to learn to adapt to different platforms and interfaces before any analysis can be performed. Especially for researchers who work primarily in wet labs and may not have the coding proficiency required to operate these tools, such a lack of accessibility presents a significant challenge.

In 2020, the release of TBtools provided researchers with a viable solution to this problem: featuring more than 130 functions and receiving frequent updates and bug fixes, the toolkit has become ubiquitous in research labs. Despite its utility and functionality, the various needs of different users presented a significant challenge to the developers. Bioinformatics data analysis encompasses a wide variety of applications and tasks, and researchers working in certain fields require highly specific and personalized tools for data analysis. While the addition of these tools helped increase the usefulness of TBtools and helped it serve a wider section of researchers, it also bloated the toolkit significantly and made it harder to use and navigate.

#bioinformatics #big data #bioinformatics analysis #bioinformatics tools

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newfangled-polusai · 5 months

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Top 5 Benefits of Low-Code/No-Code BI Solutions

Low-code/no-code Business Intelligence (BI) solutions offer a paradigm shift in analytics, providing organizations with five key benefits. Firstly, rapid development and deployment empower businesses to swiftly adapt to changing needs. Secondly, these solutions enhance collaboration by enabling non-technical users to contribute to BI processes. Thirdly, cost-effectiveness arises from reduced reliance on IT resources and streamlined development cycles. Fourthly, accessibility improves as these platforms democratize data insights, making BI available to a broader audience. Lastly, agility is heightened, allowing organizations to respond promptly to market dynamics. Low-code/no-code BI solutions thus deliver efficiency, collaboration, cost savings, accessibility, and agility in the analytics landscape.

#newfangled #polusai #etl #nlp #data democratization #business data #big data #ai to generate dashboard #business dashboard #bi report #generativeai #business intelligence tool #artificialintelligence #machine learning #no code #data analytics #data visualization #zero coding

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ebubekiratabey · 4 months

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purichana · 14 hours

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she map my reduce until i hadoooop ah flop poast

she spark my apache till i big. data. big data her.

#extremely funny #considering im studying big data tools this sem #asks #snoopy

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sprinkledata12 · 1 year

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No-Code Data Integration & Transformation, Ingest, Transform and Explore all your data without writing a single line of code. Combine all of your data into your data warehouse for 360 degree analysis through our ecosystem of integrations. Read more about Data Pipeline.

#Big Data Tools #OLTP vs OLAP

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jcmarchi · 5 days

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Mixtral 8x22B sets new benchmark for open models

New Post has been published on https://thedigitalinsider.com/mixtral-8x22b-sets-new-benchmark-for-open-models/

Mixtral 8x22B sets new benchmark for open models

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Mistral AI has released Mixtral 8x22B, which sets a new benchmark for open source models in performance and efficiency. The model boasts robust multilingual capabilities and superior mathematical and coding prowess.

Mixtral 8x22B operates as a Sparse Mixture-of-Experts (SMoE) model, utilising just 39 billion of its 141 billion parameters when active.

Beyond its efficiency, the Mixtral 8x22B boasts fluency in multiple major languages including English, French, Italian, German, and Spanish. Its adeptness extends into technical domains with strong mathematical and coding capabilities. Notably, the model supports native function calling paired with a ‘constrained output mode,’ facilitating large-scale application development and tech upgrades.

Mixtral 8x22B Instruct is out. It significantly outperforms existing open models, and only uses 39B active parameters (making it significantly faster than 70B models during inference). 1/n pic.twitter.com/EbDLMHcBOq

— Guillaume Lample (@GuillaumeLample) April 17, 2024

With a substantial 64K tokens context window, Mixtral 8x22B ensures precise information recall from voluminous documents, further appealing to enterprise-level utilisation where handling extensive data sets is routine.

In line with fostering a collaborative and innovative AI research environment, Mistral AI has released Mixtral 8x22B under the Apache 2.0 license. This highly permissive open-source license ensures no-restriction usage and enables widespread adoption.

Statistically, Mixtral 8x22B outclasses many existing models. In head-to-head comparisons on standard industry benchmarks – ranging from common sense, reasoning, to subject-specific knowledge – Mistral’s new innovation excels. Figures released by Mistral AI illustrate that Mixtral 8x22B significantly outperforms LLaMA 2 70B model in varied linguistic contexts across critical reasoning and knowledge benchmarks:

Furthermore, in the arenas of coding and maths, Mixtral continues its dominance among open models. Updated results show an impressive performance improvement in mathematical benchmarks, following the release of an instructed version of the model:

Prospective users and developers are urged to explore Mixtral 8x22B on La Plateforme, Mistral AI’s interactive platform. Here, they can engage directly with the model.

In an era where AI’s role is ever-expanding, Mixtral 8x22B’s blend of high performance, efficiency, and open accessibility marks a significant milestone in the democratisation of advanced AI tools.

(Photo by Joshua Golde)

See also: SAS aims to make AI accessible regardless of skill set with packaged AI models

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: 8x22b, ai, artificial intelligence, development, mistral ai, mixtral, Model, open source

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