OpenAI introduced its new open-source AI generator that generates 3D models based on text prompts. Point-E, a machine learning system, differs from other traditional 3D generators because it uses discrete data points to represent 3D shapes rather than create them.
3D modeling is a highly applicable technology in movies, video games, AR, VR, metaverse, etc. However, producing photorealistic 3D graphics still requires a lot of resources and effort, and doing so using text prompts is a further achievement.
Taking inspiration from the recently viral text-to-image systems like DALL-E, Lensa, and HuggingFace’s Stable Diffusion, Point-E attempts to enhance text-to-3D technology. Point-E, or Point Efficiency, uses point clouds as they are easily synthesized in terms of computational requirements. Unlike existing systems like DreamFusion, Point-E does not require hours of GPU functions. However, its resolution is not that great.
OpenAI’s research team, led by Alex Nichol, said, “Other systems leverage a large corpus of (text, image) pairs, allowing it to follow diverse and complex prompts, while our image-to-3D model is trained on a smaller dataset of (image, 3D) pairs.”
When prompted with a text, Point-E first creates a synthetic 3D rendering. It will then run this version through a series of diffusion models to generate a 3D, RGB, 1024-point cloud model. The next step generates a finer version of the same, with 4096 points. Each of these diffusion models was developed using “millions” of 3D models that had all been transformed into a standardized format.
This wireless electro-tactile system uses a skin-friendly hydrogel layer that sticks onto the palm of the hand and collects personalized tactile sensing data to bring a more realistic virtual touch experience to the metaverse.
The WeTac system developed by CityU consists of two parts: a palm patch with hydrogel electrodes as a tactile interface and a tiny flexible actuator that acts as a control panel. The whole actuator weighs only 19.2 grams and is small enough to be worn on one’s forearm.
It also has Bluetooth Low Energy (BLE) and a tiny rechargeable lithium-ion battery for wireless transmission and power. The thickness of the palm patch is a mere 220 microns to 1 mm, and the electrodes reach from the palm to the fingertips.
Through this, users will be able to experience objects in virtual scenarios, such as grasping a tennis ball during sports practice or touching a cactus in virtual social networks or games.
A coastal province called Zhejiang, in China is planning to build a US$28.7b industry metaverse by 2025, clubbing multiple companies together to facilitate technology development. The province presented the plan on December 15 as a part of its efforts to become one of the largest metaverse hubs.
Over the last few years, many Chinese provinces have expressed interest in developing metaverse-oriented technologies and making the country a metaverse hub. Recently, it was reported that the Chinese metaverse industry raises US$780m in funding and is expected to become a US$5.8 trillion industry in the coming decade.
In the presented document, Zhejiang authorities outline the idea and actions starting in 2023. One of these is the incubation of 50 startups, ten industry leaders, and other metaverse-related essential technologies, such as blockchain, virtual reality (VR), and artificial intelligence (AI). These technologies will bring companies together in production processes, industrial design, and governance.
Zhejiang’s document is not the only metaverse proposal. A few other local governments in China have also expressed their ideas outlining similar plans for developing a metaverse. In June, Shanghai also presented an industry metaverse roadmap for US$52m.
ImagenAI, a startup that uses artificial intelligence (AI) for photo-editing and automating media production processes, raises US$30m in an all-equity growth investment from Summit Partners. The investment will further the startup’s Saas (software-as-a-service) offering.
ImagenAI, not to be confused with Google’s Imagen, was founded by Yotam Gil, Ron Oren, and Yoav Chai in 2020. The idea rose from stealth after Chai’s wedding, wherein he had to wait months to get the wedding pictures and videos. After speaking with photographers, the founders identified a significant industry issue: post-production is “tedious and time-consuming.”
Imagen is available as a cloud plugin for Adobe Lightroom Classic and an independent app designed to learn the photographer’s style from their previous work. It uses machine learning (ML) to capture the editing process and make predictions of editing parameters within half a second at US$0.05 per photo.
Gil said, “Imagen profiles evolve and learn with the user over time, allowing better accuracy and consistency in applying each photographer’s style to new photos ingested into Imagen.” He added that the service would benefit photographers who edit at scale.
ImagenAI also provides pre-trained profiles based on industry experts. These profiles, called Talent AI Profiles, have pre-determined editing styles so that users can directly optimize their clicks without setting up editing parameters.
ImagenAI is making over US$10m in year-wise recurring revenue and will be profitable in the near future as the company plans to innovate using the all-equity investment for services like automatically picking the best set of pictures from a shoot and a lot more.
Helm.ai, a California-based startup developing software designed for advanced driver assistance systems, recently raised $31 million in a Series C funding round led by Freeman Group, only one year after it secured $26 million in venture funding.
Its partners, including Amplo and strategic investors Honda Motor, Goodyear Ventures, and Sungwoo Hitech, have pushed Helm.ai’s valuation to about $431 million.
Founder of the Freeman Group, Brandon Freeman, is joining Helm.ai’s board of directors as part of this financing. The company has raised $78 million to this date.
The six-year-old startup, Helm.ai, uses an unsupervised learning method to develop software that can train neural networks without the requirement for large-scale fleet data, simulation, or annotation.
Helm.ai provides its software to various Tier 1 suppliers and OEMs in the automotive industry to help them achieve software differentiation with high-end ADAS and L4 solutions.
The recent funding will add more employees to the 50-person workforce, R&D, and will help towards establishing several commercial partnerships.
Over the last few years, most enterprises have undergone a digital transformation and produced unimaginable volumes of data. This raw data is insufficient to push data science projects forward in production. As per Gartner, back in 2017, 85% of data projects failed because the data could not be trusted to facilitate business decisions. Gartner predicted these results because earlier data scientists were expected to work on the data before actually using it in the project. However, it has become apparent that “someone” needs to organize and transform this data to ensure quality, usability, and availability so that data scientists do not spend much time before the actual work begins. Data engineers are the ones who get this job done. You can opt for a data engineering course to learn more about data engineering and get one of the most in-demand jobs in the big data world.
What do data engineers do?
A data engineer’s primary objective is to transform the raw data into something valuable and understandable before presenting it to an enterprise. In addition, they must design, construct, test, mix, manage, and refine the data using various tools and sources. The goal is to build data pipelines that operate efficiently. Additionally, data engineers work closely with the infrastructure teams to automate several steps in the data engineering procedures. In addition to all of this, they create challenging queries to make the data available.
Top Data Engineering Courses
Several data engineering courses are available, and selecting the right one is challenging. This article has enlisted some knowledgeable courses for a data engineer. Have a look.
Professional Certificate in Data Engineering Fundamentals (IBM)
Professional Certificate in Data Engineering Fundamentals (IBM) is an excellent introductory data engineering course if you are interested in venturing into data engineering. Since data engineers are the core of a data science project as they create pipelines guiding the workflow, it becomes inevitable not to know the fundamentals. This course provides a comprehensive theoretical and practical introduction to building pipelines, managing data, and engineering work ecosystems to lifecycles.
The certification includes three sub-courses:
Data Engineering Basics
Python Basics for Data Science
Relational Databases and SQL.
The course will span over 4 months and take an average of 4-6 hours per week.
Data Engineering with AWS Machine Learning (Pluralsight)
Storing data for complex machine learning projects is tedious because of varying data formats. This data engineering course focuses on how to store data and leverage machine learning on the AWS platform. In this course, Data Engineering with AWS Machine Learning by Pluralsight, you will learn how to select the appropriate AWS service for each data-related activity for any given scenario. Initially, you will investigate data storage options and the purposes of each type of storage. Finally, you will learn to transform raw data into usable formats.
The course will cover several topics that will introduce you to data engineering with AWS.
Typical Data Flow for ML on AWS
Database Storage Options for ML on AWS
Data Warehouses and Data Lakes
Batch Data Ingestion
Data-driven Workflow
It is a short course that you can finish within 3 hours and will bring you one step closer to using AWS machine learning services with ease.
Data Engineering Learning Path – Coursera
Data Engineering Learning Path is an excellent umbrella course offered by Coursera with which you can learn essential skills that a data engineer needs. Coursera suggests a combination of sub-courses that will aid you in moving towards a full-fledged data career. The following courses are recommended for a data engineering learning path:
Business Intelligence Analyst – Power BI, Tableau, SQL
Business Intelligence Developer – Software development, SQL, Javascript
Data Engineering – Python, Big Data, ETL
Coursera recommends a Coursera Plus subscription to guide you through multiple courses in a career learning path, with access to over 3000-course options.
Become a Data Engineer: Mastering the Concepts – LinkedIn Learning
If you are looking for a data engineer course online, LinkedIn Learning offers an extensive beginner-level course, Become a Data Engineer, for those who wish to learn the fundamentals of data engineering from scratch. You will study the core principles of data engineering, DevOps, trade-related tricks, and how to use them in platforms for project work. The course discusses Big Data, SQL, and NoSQL coding for analysis. Moving forward, you will understand how Apache Sparks work with Big Data technologies.
The course will cover
Data Science Foundations
NoSQL Essentials
Apache Spark Essential Training
Architecting Big Data Applications
Cloud SQL and SQL Essentials
Advanced NoSQL for Data Science and SQL Professionals
It will take approximately 13 hours to cover the entire material, and you will get a certificate on completion.
Data Engineering – ETL, Web Scraping, Big Data, SQL, Power BI (Udemy)
If you are looking for a big data engineer course, Data Engineering – ETL, Web Scraping, Big Data, SQL, Power BI is a beginner-level data engineering course that will teach you how to interact with data. It covers ETL, Web Scraping, SSIS, SQL, and Big Data.
The crash course is divided into twelve sections covering 134 video lectures covering the following topics:
ETL, or Extract, Transforms, and Load, a data pipeline using which people can extract data from several sources, transform it according to the requirements, and load it in a data store.
Secondly, you will also learn about SQL Server Integration Services for data integration, transformation, and solving business problems.
Big Data, including numbers, audio, images, text, and other kinds of data with high volume, variety, and velocity.
You will become familiar with SQL, a standard programming language for managing databases.
Lastly, you will learn Power BI, a robust business analytics solution that helps with data visualization and business insights.
The course content is about twelve hours long and can be completed flexibly. On completion, you will be able to implement ETL with SSIS, scrap web data with Python, Beautiful Soup, and Scrapy, connect web data with Power BI, and model with Power BI.
Professional Certificate in Data Engineering (IBM)
After learning data engineering fundamentals, proceeding with another course, like Professional Certificate in Data Engineering by IBM, will be a significant next step. This is one of the best data engineer course in India, designed for people who want to advance their interest and knowledge in the field. It advances the basics while teaching you application development, more complex pipelines, and data warehousing.
The course is divided into 14 sub-courses that will give you an insight into cloud-based relational databases (RDBMS) and NoSQL databases. Some of these are:
Python for Data Engineering
SQL for Data Engineers
Building ETL and Data Pipelines
Big Data Engineering, Hadoop, and Spark Basics
Data Engineering Capstone Project
The course spans over one year and two months, with an average of 3-4 hours per week. On completion, you will have acquired skills in Hadoop, Big Data, PostgreSQL, Bash, Data Warehousing, and other related technologies.
Microsoft Azure Data Engineering Associate DP-203 Exam Prep Specialization
It is not a standard course like other data engineering courses. However, opting for Microsoft Azure Data Engineering Associate Exam Prep Specialization will give you a different insight into data engineering. It is a rewarding path to being an associate with Microsoft, where you will learn about basic theoretical concepts and get hands-on experience with real-world scenarios.
The specialization program will cover the following sub-courses:
Data Engineering with Microsoft Azure
Data Storage and Integration
Data Warehousing and Engineering
Preparation for Data Engineering on Microsoft Azure Exam
It will take approximately thirteen months to complete, with an average of two hours per week. On completion, you learn about Azure Synapse Analytics, Apache Spark, Modern Data Warehousing, Azure Data Lake Storage, and other related technologies.
A data engineer must know at least one cloud service provider and its services. Amazon Web Services (AWS) is an industry leader in cloud computing. Data engineers acquainted with an AWS Certified Solutions Architect – Associate (SAA) have better chances at career profiles and high earnings. In this intermediate-level course, AWS Solutions Architect Associate Certificate Prep, you will get expert guidance on how and what to prepare for the examination.
The first week talks about multi-tier data solutions and storage technologies. The following week talk about flexible and scalable computing solutions and database networks. In week three, you will learn how to secure your data and database network. Lastly, the fourth week will teach you computing and database services cost optimization.
The month-long course comes with flexible deadlines, sample certification questions, and skill-based hands-on exercises on data structures and architectures.
Taming Big Data with Apache Spark and Python
This Big Data engineering course, Taming Big Data with Apache Spark and Python on Udemy, focuses on Big Data analysis using Apache Spark and Python. With more than 20 hands-on examples with large data sets, you will learn to use DataFrames, structured streaming with Spark 3, and MLLib for ML-driven data mining and other related concepts. The course is divided into eight sections, covering 66 video lectures. These sections are structured to cover the following concepts:
Introduction to Spark and RDD interface
SparkSQL, DataFrames, and DataSets
Spark Clusters and Spark ML
Spark Streaming and Graph X
The course will take approximately seven hours, with access to a personal Windows/Linux computer and some prior scripting experience.
Data Structures and Algorithms Nanodegree (Udacity)
In this data engineering course, Data Structures and Algorithms Nanodegree from Udacity, you will be acquainted with more than 100 data structures. Data engineers should know their way around multiple data structures and algorithms to be proficient in managing and sorting data. Knowing about data structures also makes them capable of understanding patterns in data and deciding appropriate operations. During the course, industry experts will deliver online lectures on Udacity’s platform and provide personalized project reviews. Once you finish the course, your project will undergo a strict review process to get certified.
The certification will cover three sub-courses:
Data Structures
Basic Algorithms
Advanced Algorithms.
You need to have a basic knowledge of Python and Algebra to enroll in the course over 4 months, with an average of 10 hours per week.
Businesses have shifted to the digital world and are entirely driven by the digital data they accumulate. This data is of significant value as it depicts how consumers interact with their products and services. However, you need to have a robust analytical tool to collect and analyze it. Microsoft’s Power BI, a powerful business analytics tool, provides a platform for data collection, analysis, and visualization through appealing dashboards and interactive reports, enabling companies to boost profitability and unearth deeper insights. Power BI dashboards are an essential visualization technique that offers a 360-degree perspective for speedy insight-gathering.
Before proceeding with the examples, let’s look at what Power BI dashboards are.
What is a Power BI Dashboard?
A Power BI Dashboard is a canvas that showcases essential data points in multiple forms within a single page. Several dashboards constitute the BI reports. Only the most significant parts of the data story are included in well-designed dashboards. A specific element can be clicked to see the main report. Dashboards are beneficial for tracking the progress of your company’s operations, sales, or other key metrics. It gives you a bird’s-eye view of your company and aids in developing data-supported action plans. Additionally, there are numerous Power BI dashboard designs from which you can choose.
Top 10 Power BI Dashboards Examples
This article presents the Top 10 Power BI Dashboard Examples that will help you comprehend how Power BI dashboard samples can be utilized to demonstrate various scenarios and provide insights through thoughtfully planned and selected KPIs.
E-Commerce Sales Power BI Dashboard
Online retailers can use this interactive Power BI template to evaluate the performance of various products from both a broad view and a granular one. It offers a summary of overall sales and has the option to display annual, quarterly, and monthly growth rates. Additionally, it enables retailers to explore and comprehend the best-performing goods, areas, and more.
This Power BI dashboard can filter sales by location, period, average order value, or other desired criteria. They can also be customized to display why certain products have been returned and summarize the product delivery status.
Benefits of using an E-commerce Dashboard:
Get a bird’s eye view of business performance.
Saves time by allowing real-time information analysis.
Eliminates analysis paralysis by facilitating faster decision-making.
Inventory Stock Analysis Power BI Dashboard Inspiration
Inventory management is essential to keep track of the supplies and materials you need to run a business effectively. Consequently, Inventory Power BI dashboard examples give you better access to your stock and enable you to undertake real-time inventory management.
This Power BI inventory dashboard sample is essential as it can be used by organizations of all sizes across multiple industries, including retail, FMCG, manufacturing, hospitality, education, and restaurants, to run efficiently and meet client demand.
In addition to the stock, this dashboard can display customer reviews, most viewed, least viewed, and unviewed products. Based on the Fulfillment Cycle and MarkDown variance, you may anticipate stock availability and restocking cycles.
Benefits of using an Inventory Stock Analysis Dashboard:
Prevents stockouts and project delays.
Better cash flows as the dashboard highlight the business’s lean and rush periods.
A standard business dashboard visualizes revenue, income, gross profits, etc. These metrics are used to compare ex-post and ex-ante business plans and forecasts using time references, geography, or product lines. However, a Price-Volume-Mix (PVM) variance analysis showcases how sales volume, product mix, and prices affect revenue.
In this Power BI dashboard example, the factors that contributed most to each category’s revenue growth are highlighted, including price, volume & product mix fluctuation, and new product releases and discontinued goods. Such dashboards are handy for product managers and their teams to pinpoint critical problems and opportunities.
Benefits of using a PVM Analysis Dashboard:
Helps to refine pricing policies to maximize profits.
Facilitates a more informed, data-driven understanding of the organization.
Pinpoints granular details due to its bottom-up approach.
COVID-19 Power BI Dashboard Example
Undeniably, the COVID-19 pandemic has been the talk of the town for the past two years. A Power BI dashboard representing the distribution and impact of the virus is an excellent example of depicting Power BI’s capabilities. This dashboard can compare and contrast mortality and recovery rates across different countries.
As seen above, this dashboard can also show the distribution of active and recovered cases in a country-wise distribution. It can also track the death toll and visualize the pattern to forecast future states.
Benefits of using a COVID-19 Dashboard:
Helps in tracking active and recovered Covid patients.
Based on the trends, people can plan their work/travel.
Sentiment Analysis Dashboards
Sentiment analysis is an artificial intelligence-driven technique that analyzes unstructured data to draw opinions and emotions. These outcomes can be represented in eye-catching charts and graphs with business intelligence software like Microsoft’s Power BI to get clear insights. These dashboards are extremely useful as sentiment analysis datasets are limited to research and used to develop rule-based models and advance artificial intelligence-driven techniques.
For instance, consider a social media business owner who posts about their products. This Sentiment Analysis Interactive Dashboard shows various analytical points in data accessible from online retailers. The dashboard also makes it possible to determine what customers think of a product and other details like customer satisfaction and discontent ratings for the same products.
Benefits of using a Sentiment Analysis Dashboard
It can help you recognize the happiest customers.
These dashboards help in monitoring agent efficiency and performance.
Even website chatbots can benefit from a sentiment analysis dashboard to recognize customer moods.
Nowadays, most businesses utilize social media platforms to market their products and services. Keeping track of your business’s performance on social media platforms becomes vital to see whether your efforts are reaping any benefits. Using Power BI dashboard templates for social media analytics, business owners can develop better marketing strategies, alter branding, and improve consumer engagement.
For instance, this dashboard displays monthly information about many social media subjects, such as web sources, the volume of online discussions, online influencers, unique categories, sentiment analysis, quotes, geolocation, and many other topics, making it one of the most common Power BI dashboard examples.
Such Social Media Dashboards can be extended to obtain tags and mentions, view good/nasty remarks, locating influencers by geolocations. It also enables the users to track their mentions in the sentiment analysis by hourly/daily filtering.
Benefits of using a Social Media Dashboard
Enhanced productivity and engagement with followers.
It can help you understand collaborations and whether they have been effective or not.
This dashboard can help you in scheduling your posts.
Financial Analytics Dashboards
All organizations hold potentially valuable financial data. A Power BI Financial Analytics Dashboard is designed to study finances and infer trends for executive-level professionals.
The dashboard gives a broad overview of the company’s financial performance throughout. Professionals can also explore financial performance according to the area and product category and identify financial patterns and regions of over/underperformance in these areas. These preliminary insights can then be used to choose where to concentrate their efforts.
For instance, this financial dashboard displays revenue, liabilities, expenses, gross profit, and other financial assets. Similarly, a financial dashboard can display the profit and loss statement, aggregate revenues, and balances.
Benefits of using a Financial Analytics Dashboard
It summarizes your financial performance.
It demarcates expenses accruing to different activities and their corresponding returns.
Using the insights, the organization’s financial growth can be tracked over time.
Human Resource Analytics Dashboards
Human resource analytics is essential in employee management and reaching business goals. Given the available data, it is challenging to draw actionable insights quickly.
The next on our Power BI dashboard examples’ list provides several human resource analytics dashboard samples to make it easier, using which HR professionals can analyze employees and make better data-driven decisions.
It is a visual representation of key performance indicators (KPI) and HR data that provides an overview of the present situation and makes vital information easily accessible. For instance, this HR dashboard summarizes employee metrics by gender, age, absenteeism rate, contract types, terminations, and other related factors.
Benefits of using a Human Resource Analytics Dashboard:
These dashboards help identify effective measures and the ones that are not.
They help in tracking absentees and annual leaves taken by employees.
They also give insights into the hiring practices and trends in the organization.
Global Oil Production and Consumption Dashboards
Big data visualization is a challenge in tracking crucial parameters, output figures, costs, etc., concerning the oil and gas industry. Power BI dashboards provide a revitalizing visual analytical solution for the entire industry.
The dashboard on our list of some top Power BI dashboard examples is one that compiles all essential global oil production and consumption parameters to make it simpler for people to quickly and easily review massive amounts of oil production and consumption data in real-time. The top 3 oil metrics for all countries—reserve, output, and consumption—are shown in this dashboard’s country tab.
The production tab in this dashboard also highlights the most extensive oil reserves, the oil production timeline, and the total production by each country. On the other hand, the consumption tab highlights the consumption metrics.
Benefits of using a Global Oil Production and Consumption Dashboard:
It can assist employees in making more informed decisions.
It helps in tracking the oil production of different countries.
Oil is scarce, and such a Power BI dashboard development helps track and manage oil reserves efficiently.
Email Engagement Analytics Dashboard
These Power BI Dashboard designs are intended for businesses advertising their products or services via mass emails. These dashboards show the percentage of delivered, clicked, and opened emails. These dashboards typically use data from campaign management programs and show how these indicators changed during the relevant time frame.
As you can see here, this is one of those Power BI dashboard examples that display several email metrics and a month-wise comparison. These boards can be customized to include other relevant metrics like email-driven sales by categories, recurring customers, etc.
Benefits of using an Email Engagement Analysis Dashboard:
It saves time as it displays all vital metrics on a single dashboard.
Visual insights are easier to draw conclusions and decide whether the emails are effective or not.
These dashboard insights can also be used to schedule emails for geolocations in different time zones.
Power BI Dashboards vs Reports
While these Power BI dashboards may seem like a report as they summarize performance or common metrics, they are fundamentally different. Here are a few differentiators:
A dashboard in Power BI may pin visuals after drawing insights from multiple datasets, but a report usually focuses and cater to a single dataset at once.
Dashboards are a single-page summary, whereas reports may take as many pages as required.
Visualizations on a dashboard in Power BI focus on building insights using attractive elements such as graphs, whereas reports do not concentrate on visualizing.
Dashboards are a great method to keep an eye on your company and quickly see all your key indicators. These visuals may be drawn from a single underlying dataset or several and even from underlying Report(s). A dashboard does more than just visualize; it updates automatically when the underlying data changes, making it highly interactive.
The Shriram Group has announced that the company will have a metaverse branch by the first quarter of 2023. By doing so, it will become the first Indian non-banking financial company (NBFC) to be in a metaverse.
Novac Technology Solutions, Shriram’s digital arm working on virtual reality (VR), mixed reality (MR), and augmented reality (AR), will put the group on a metaverse that will have solutions for employees and customers.
According to Pradeep B, associate vice-president of Novac, customers will be able to experience the solutions of the brand during the first phase. We are also trying to incorporate a bot-based system, in which the customer will receive a call back based on their virtual interest, he added.
In the second phase, the company will get a real-time support system, which will include avatar representations of brands/products. “Depending on regulatory approvals, the company will try to see what transactions can take place online through metaverse,” he said.
Novac said that it has over 60 customers, and out of that, about 20 use metaverse solutions. It has a tie-up with a European VR and AI soft skills trainer, Bodyswaps, to offer teaching modules to the staff of its clients.
Meta has announced that it is going to shut down its cameo-like app, Super, in February 2023. Developed by Meta in 2020, Super is a live-streaming platform for influencers.
According to TechCrunch, Meta wanted to create a virtual meet-and-greet experience similar to what users experience at real-life events like VidCon or Comic-Con through Super.
Although Super is not officially shutting down until February, users will not be able to create new events during this period. If users have a pre-scheduled event on Super, the company advises that the event be rescheduled on another platform.
Users who have participated in a Super event or have hosted one in the past can download their recorded media before the official decommissioning of its website in February.
Super has joined a long list of apps and experiments that have been shut down by Meta this year. The company recently shut down its Facebook live shopping feature on October 1 to shift its focus to Reels.
Dataiku, the well-known platform for Everyday AI, announced $200 million in Series F funding led by Wellington Management at a $3.7 billion valuation on 13th December. The investment will help to strengthen Dataiku’s leadership position and empower its capabilities.
Matt Witheiler, Consumer/Technology Sector Lead, Wellington Management, stated that Dataiku’s proven track record, growth trajectory, management team, and customer roaster help the company to scale AI to new heights. Wellington Management is pleased to partner with and contribute to Dataiku’s journey.
Dataiku is a popular Everyday AI platform, allowing data experts and domain experts to work together to build AI products to carry out daily operations. It helps businesses to systemize the use of data for exceptional business results. About 500 businesses use Dataiku for predictive maintenance, supply chain, marketing optimization, quality control, and more.
Florian Douetteau, co-founder and CEO of Dataiku, mentioned that Dataiku is glad to attract new, market-leading investors like Wellington in today’s challenging market to strengthen its solution and a world-class team. It has taken a leadership position in helping businesses to use massive datasets at scale and create a culture of AI-focused business results.
