DataZenith

Contact for Pricing

Twitter

Facebook

Copy Link

Revolutionizing AI/ML data generation with VR and generative AI. Providing precise and customizable datasets for enhanced accuracy and innovation.

How DataZenith can help you:

Enable the development of AI algorithms that understand and interact with virtual environments.
Improve algorithm accuracy, enabling AI models to perform better in real-world scenarios.
Reduce development time and costs by prototyping and testing AI solutions up to 30x faster.
Generate data for rare and challenging scenarios, enhancing AI model's ability to handle edge cases.
Provide flexibility to create custom VR environments tailored to specific use cases.
Offer pixel-perfect annotations for VR-generated data, eliminating manual annotation errors.
Provide an intuitive, no-code interface for easy generation of VR training data.

Why choose DataZenith: Key features

User-friendly interface for non-technical users to generate VR training data.
Customizable VR environments for specific industries and applications.
Precise VR data annotations for accurate labeling and model training.
Enhanced AI model accuracy with realistic and immersive datasets.
Faster prototyping and development, enabling rapid iteration and innovation.

Who should choose DataZenith:

Industries looking to enhance AI model accuracy with realistic and immersive data.
Developers and businesses aiming to reduce development time and costs.
Organizations seeking to prototype and test AI solutions more efficiently.
Teams requiring customizable and precise datasets for AI and ML applications.

About DataZenith

Website

https://datazenith.in

Release Date

March 2024

Pricing

Contact for Pricing

Related fields

Related News

Transforming Data Engineering: How Generative AI is Shaping the Future

Generative AI can be leveraged to automate various data engineering processes, such as data integration, transformation, and pipeline creation. Generative AI is poised to completely transform data engineering and drive unprecedented innovation across industries. As the volume and complexity of data grows exponentially, generative AI offers powerful new capabilities to completely overhaul the way we collect, process, analyse and derive value from data. Today, the volume of data generated globally is growing at an unprecedented rate. According to IDC, the global datasphere is expected to reach 175 zettabytes by 2025, a staggering five-fold increase from 2018. This surge in data generation is driving significant growth in the data engineering market, particularly in India. AIM Research projects this market to expand at a CAGR of around 33.8% over the next five years, growing from $29.1 billion in 2023 to $124.7 billion in 2028. The traditional methods of data ingestion, transformation, and wrangling are labor-intensive, time-consuming, and often error-prone. It's like trying to navigate a raging river with a leaky rowboat - you might get there eventually, but it will be a slow and arduous journey. This is where GenAI steps in, offering a powerful motor to propel us forward. Migration and modernisation projects involve transferring data from one technology platform to another, such as moving from on-premises systems to the cloud. Data engineers are crucial in designing and implementing data pipelines that extract, transform, and load data into the target technology. Generative AI can be leveraged to automate various data engineering processes, such as data integration, transformation, and pipeline creation. This automation allows data engineers to concentrate on higher-value tasks. This is where DataSwitch excels. As a proven leader in data modernisation, especially in migrating from on-premise to the cloud, DataSwitch leverages its trio of tools -- DS Migrate, DS Integrate, and DS Democratize -- to automate the entire data transformation lifecycle. DS Migrate automates the migration of schemas, data, and processes from legacy databases to modern cloud-based database services. It supports the migration from legacy systems like Oracle, Teradata, Netezza, Informatica, SSIS, and DataStage to modern cloud platforms such as AWS RedShift, Snowflake, BigQuery, DataBricks, and Spark. On the other hand, DS Integrate is designed to consolidate and integrate data for domain-specific applications.Traditionally, data ingestion and transformation can be complex and time-consuming, requiring manual coding or scripting. DS Integrate offers a user-friendly interface with pre-built connectors and functionalities, allowing businesses to ingest data from various sources and transform it into a usable format without extensive coding expertise. It can ingest and structure previously unstructured data, and automatically generate code to create knowledge bases. It is designed to handle data arriving in various formats, including PDFs, images, text, ODBC, and JDBC. Meanwhile, DS Democraticise allows users to interact with data in a natural and intuitive way. Data quality is the foundation of all successful data-driven projects. Dirty data leads to dirty insights, and ultimately, bad decisions. GenAI can be leveraged to identify and address data anomalies, generate synthetic data to fill in missing values, and even predict potential data quality issues before they arise. According to estimates from The Data Warehousing Institute, inadequate data quality results in approximately $600 billion in annual costs for organisations in the United States. Generative AI is easing the process of data transformation and cleansing. Natural language interfaces allow data engineers to describe desired transformations in plain English, with AI automatically generating the necessary code. This dramatically accelerates development cycles. By automating routine tasks and providing intelligent suggestions, AI frees up data engineers to explore new ideas and tackle more complex challenges. We're seeing a shift from data engineers spending most of their time on data preparation to focusing on advanced analytics and developing novel data products. As we look to the future, generative AI will continue to push the boundaries of what's possible in data engineering. We'll likely see more sophisticated AI models capable of understanding complex business logic and generating entire data architectures based on high-level requirements. The line between data engineering and data science will blur further, with AI-assisted tools enabling seamless transitions between data preparation, analysis, and model deployment.

Analytics India Magazine

Wed, 31 Jul, 8:01 AM UTC

30+ Tech Leaders Convene To Share Insights On How GenAI Is Reshaping Startups & Industries

Google Cloud's Vertex AI, along with Llama and Mistral, are set to remain at the forefront of GenAI for R&D and practical applications Traditional AI/ML has been out there for years, promising mindblowing tech transformation across industries. But the whirlwind of innovations required more speed and depth and fewer technicalities for enterprise users. That did not happen at scale until GenAI (read the OpenAI ecosystem) made a global foray in 2022 and spread rapidly across economies and industries. GenAI adoption by startups and big enterprises is apparent. The country's booming startup ecosystem is also in a strong position to harness its advantage as India is now home to 100+ native GenAI startups, with more than $600 Mn of funding since 2019. In spite of these impressive estimates and worldwide hype, GenAI may not be growing as rapidly as we assume. Along with opportunities have come many paradoxes regarding implementation, deployment, data security and regulation/compliance. To decode all of this, Inc42 partnered with Google Cloud to organise the second edition of The Tech Horizon for a better understanding of the new frontier. Held in Delhi on June 26, the day-long event was split into three sessions and kicked off with a roundtable titled Elevating Customer Experiences Through AI and Cloud-Driven Analytics. The event featured 30+ C-suiters as attendees who delved deep into AI-GenAI and cloud solutions. The roundtable was followed by an intriguing fireside chat between InsuranceDekho's cofounder and CTO, Ish Babbar, and Google Cloud's sales lead, Dilip Mukkavalli. The chat provided insights into how startups can strategically use AI to scale businesses and amplify customer acquisition. Finally, there was a knowledge session on How Startups Can Build Faster and More Scalable AI Solutions with Google's Vertex AI. Similar to the first edition of The Tech Horizon hosted in Bengaluru in May, all attendees had the opportunity to network and collaborate on critical aspects such as GenAI's value propositions, adoption, use cases and concerns. Among the key participants of The Tech Horizon II were: In Pursuit Of Value: How Startups Are Implementing Excellence Via AI-GenAI, Cloud Solutions From gaming to policy room: Moderated by OfBusiness cofounder and CBO Nitin Jain, the roundtable started on a high note as Zupee's Sairam Mushyam unveiled how AI decodes customer behaviour, requirements and interactions to deliver personalised experiences on the casual gaming platform. According to him, developing advanced features, especially a gamer's persona/preferred environment, and helping build their [gaming] skills are complex tasks and require different ML models to ensure a high mix of AI analytics and creativity. On the other hand, Tagbin uses AI, VR and other tech stacks to create immersive digital worlds in physical space. These include experiential on-premises museums and LLM-based, prompt-engineered 3D models of cultural and political leaders intuitively interacting with and responding to visitors' queries. Tagbin's Ankit Sinha detailed another use case called the strategy room. "If a state has developed best practices in an area and another state wants to adopt those, AI can do a feasibility study and identify the requirements for successful implementation," he said. The fintech playbook: Fintechs like Clix Capital and Recur Club have integrated business learning with AI/ML for fast and risk-free credit underwriting. Also, Clix has moved its entire business to the cloud. "Overall, our turnaround time [TAT] has improved by 60% and IT costs are down 33% due to AI and cloud adoption," said CTO Vijaykumar Ramakrishna. Video intelligence for automating ops: The video AI platform Awiros uses deep learning-based CNNs and other core techs to process videos at scale and automate ops through video intelligence. "Our deployments are running across cameras in European warehouses and automating the operations. Instead of scanning and looking up where a parcel has to go, you can use a camera to know how many packages are unloaded, track which forklift has picked up which packet and get instructions about where the packet should be placed. This is a kind of in-process automation that significantly improves customer experience," said founder and CEO Vikram Gupta. Cloud security and sustainability: Given the large file sizes, Awiros uses hybrid technologies to process videos on and off the cloud ecosystem. This led us to the next critical question: How safe is the cloud in this era of hackers? Rahul Tyagi of Safe Security detailed the startup's key focus areas: Assessing the probability of a data breach, fixing and patching in case of a breach and auto-calculating the insurance cover to meet cybersecurity costs. The startup also uses a GenAI layer for easy communication to keep senior leadership in the loop. The conversation then shifted to AI and environmental sustainability. Ashish Airon, of CogniTensor, explained how the startup uses AI agents to help brands go green. The platform has developed Sustain 3P, which measures emissions for companies big and small, provides a thorough comprehension of global compliance standards and generates reports for clients Lohum's EIR Rayavarapu Narasimha Viswanadh discussed how AI is used to measure EV battery capacity, raise security alerts and estimate whether these EV cells can be recycled for further use in other industry segments. Also, sudden power cuts tend to harm the infrastructure, and optimisation algorithms are needed to control energy resources. Among other businesses present at the round table were martech platform Quickreply, HRtech startup Zimyo and ecommerce solution provider NuvoRetail. How InsuranceDekho's AI Leap Can Help Bridge The Chasm The following fireside chat saw Ish Babbar of InsuranceDekho, Dilip Mukkavalli of Google Cloud and Jain of OfBusiness engage in a lively conversation to give the audience a glimpse into the startup's business intelligence and forward-looking technology approach. Babbar set up the insurance marketplace in 2016, which currently features more than 45 insurers and 600+ policies covering every aspect (life, health, auto and more). It targets Tier II-IV cities and works with 1.5 Lakh agent partners. The founder cited the abject online penetration of insurance in India - around 4% - and the ambitious goal of getting every Indian insured by 2047, emphasising that the situation might improve if one could buy the right product every time. "Insurance, in itself, is a promise. You won't know whether you have purchased the right thing until you require it. And the service part is most crucial. There won't be much policy value until a buyer is serviced," he observed. The lack of customer awareness and inadequate tech support also hinder the non-metro customer base. People from Tier II and III still value personal hand-holding but also like the numerous options available on an insurtech platform. "Now, suppose one is looking for a health plan for a 36-year-old with diabetes and residing at a specific location. If you can solve that and recommend three or four plans, the customer will return and close the deal. All this is doable without GenAI, but it takes time and manual intervention. With AI, it will take only a second, and that can be a game changer," he added. Also, getting people to interact differently is the beginning of endless opportunities. The AI layer was only at the backend, but now, it is at the front end to enable smooth communications with virtual assistants. So, the way we buy air tickets may change two years from now. We may use a voice bot instead of culling data through filters. The only glitch could be the cost, but Mukkavalli disagreed. "Tech is an investment, not a cost. When discussing use cases, we only consider external things, such as customer-facing ones. But what about internal processes, team productivity and profitability balance?" Advantages Of Vertex AI The event ended with a knowledge session hosted by Ashish Arora, AI specialist and customer engineer at Google Cloud. As expected, he led the audience to the exciting world of open-source GenAI tools and technologies, revealing the latest from three standout models - Google's Vertex AI, Meta's Llama and Mistral AI. "We have all witnessed the dominance of predictive AI," Arora said. "But the past six months have seen a remarkable shift towards generative applications. We are now delving into chatbots, vector-based searches, semantic understanding and natural language processing that generates SQL queries based on user intent," he added. He also explained that Vertex AI would remain committed to realising GenAI's potential in real-world scenarios. For context, the Vertex AI programme helps users train and deploy AI applications and customises LLMs for integration with AI solutions. Elaborating on Vertex AI, Arora emphasised that the platform would go beyond simple content generation. Instead, startups can create intelligent agents capable of generating natural interactions. He would further focus on developing API-based conversational chatbots and unified search experiences. "The beauty lies in the fact that you don't need to be an algorithm or ML wizard to use these functionalities," he beamed. The Tech Horizon II served as a powerhouse for discussions around the pressing need to embrace AI solutions and their transformative use cases. This sentiment also aligns well with industry trends. A 2023 NASSCOM report on Indian tech startups also stated that 70% of founders planned to integrate AI into their solutions, with GenAI as a catalyst for this adoption. As tech companies abroad continue to plough billions of dollars in AI applications and the Wall Street is bankrolling the frenzy, the vision of a GenAI-powered technology era is rapidly rising. Goldman Sachs seems sceptical, though, and the big question is: After the dotcom bubble bust, has the 'next big what' finally arrived?

Inc42 Media

Fri, 2 Aug, 12:04 PM UTC

The Potential of Synthetic Data for AI Development - DZone

Data is the bane of existence for AI models, and the accuracy and effectiveness of the AI systems significantly depend upon the completeness of the data used during the training. Although real data undoubtedly makes AI systems more effective, there are certain challenges, as the real data can be imbalanced, biased, or incomplete. Hence, to cope with the shortages in real data, data scientists have to source synthetic data. Synthetic data is considerably more inexpensive than real data, but there are still some challenges, like ensuring demographic diversity, reliability, and sufficient volume accumulation, which data scientists must mitigate. As the name implies, synthetic data isn't collected from real-life occurrences. Instead, it mimics the characteristics of the original data and is sourced from various data generation techniques, algorithms, and models. Although synthetic data closely resembles real data, it never contains actual values from the original datasets. More precisely, it's made-up data. A more qualitative definition is that it is a type of data statistically identical to real-world data but algorithmically generated. Experts prefer synthetic data for three: it poses fewer privacy risks for organizations, decreases the turnaround time for model training and validation, and can significantly prove helpful for testing new products (as using production data is illegal). It can also help increase model explainability by eliminating bias and facilitating the stress-testing phase. Although synthetic data is a relatively new concept, its future importance can be estimated by Gartner's quote, "The most valuable data will be the data we create, not the data we collect." Furthermore, by 2030, most AI models will be trained on synthetic data. Synthetic data is rapidly becoming an industrial inclination. According to current statistics, about 60% of models are trained and validated via synthetic data sources. However, to decide which one will fit your needs, it is important to know the pros and cons of both. Synthetic data is sourced from various techniques; however, the most fundamental ones are mentioned below. Generative AI is one of the most popular means of creating synthetic data. These deep neural networks consist of GPTs, GANs, and VAEs, comprehending the underlying distribution in real data and trying to mimic it in the synthetic substitute. This approach is particularly useful when the real data for a specific domain is unavailable. However, the data analysts have a keen observation of real-world statistical distributions. By using their domain knowledge, they can produce a random sample of any distribution like Chi-square, t, lognormal uniform, etc. However, it is critical to note that the accuracy of the resultant data highly depends on the domain understanding of the expert directing the synthesis. Unlike the prior case, if there is real data available for the desired task, then businesses can use the Monte Carlo method to fit it into a known distribution. Although this method can help organizations find the best-fitting distribution, its compatibility with the industrial requirements is not always guaranteed. In such situations, Machine Learning models can be used to find the best-fit distribution. The deployment of deep learning models, especially GANs, is necessary because the general-purpose large language models could not provide the needed data accuracy. Deep Learning models solve most problems previously portrayed in synthetic data generation techniques. However, they are still prone to overfitting, require more computation overheads, and may lack the ability to create realistic patterns in the data. To mitigate these issues, experts recommend implementing regularization techniques to prevent overfitting and pre-training the model on a resembling dataset to improve generalization. Creating synthetic data based on statistical distribution poses challenges like mimicking the precise distribution and maintaining the correlations. To attenuate these complications, it is suggested that diverse statistical models be employed to capture complex relations. Monte Carlo is a popular method for finding the right statistical distributions, but since it uses ML algorithms, it is susceptible to overfitting. Here, the actionable strategy is to deploy hybrid synthetic data generation techniques. In this approach, one part of the data is generated from theoretical information, and the rest is derived from the available data. Synthetic data is rapidly becoming a viable source for training and testing AI models as it is more intelligent and scalable than real data. However, as this new discipline unfolds, many challenges and risks must be addressed. These include the absence of standardized tools, discrepancies between synthetic and real data, and the extent to which machine learning algorithms can effectively utilize imperfect synthetic data. However, this doesn't neutralize the importance of the real data because there always has to be a source that can be weaved into something reasonable and accurate.

DZone

Wed, 14 Aug, 4:01 PM UTC

11 Promising GenAI Startups in India

Technologies like GenAI will continue to be a core business focus. India is home to over 100 GenAI startups that have raised more than $600 million since 2019. Spearheading this transition are names like Sarvam AI and Krutrim, which are focussed on building Indic LLMs, while others like Observe.AI, having secured $214 million, are utilising AI to offer customised customer and operational support to businesses. India's GenAI market is expected to see a major boom in the coming years and is projected to cross the $17-billion mark by 2030. Here are some Indian startups that are causing a stir in the rapidly evolving Indian GenAI space. TWO AI, founded by Pranav Mistry in 2023, focuses on building advanced conversational AI technologies. The platform is designed to provide enhanced voice AI solutions for businesses, including creating more human-like interactions. The startup raised $7.5 million in the seed funding round in 2023, led by prominent venture capital firms such as Nexus Venture Partners and Lightspeed Venture Partners. The funds are being used to scale their AI models and develop more sophisticated conversational tools. Founded in 2020 by Harikrishna Valiyath, Shubham A Mishra, and Vrushali Prasade, Pixis provides a codeless AI infrastructure platform for brands to monitor and orchestrate their marketing campaigns. Since its inception, the startup has raised $209 million in capital, raising $85 million in its last funding round in 2023. Pixis is backed by startups like Grupo Carso, General Atlantic, Celesta Capital and Chiratae Ventures. It competes with the likes of Utilidata, HeadSpin, and Navikenz in the larger AI-powered technology space. Founded in 2022 by Ola founder Bhavish Aggarwal, Krutrim is experimenting with GenAI to develop an India-specific LLM. The startup's family of LLMs is said to be capable of working with 10 Indian languages. The startup competes with the likes of Sarvam AI, Mistral AI, and Google DeepMind. Krutrim introduced its AI chatbot in December 2023 and launched its public beta version. It is a multilingual model capable of understanding over 20 Indian languages and generating text in 10 of them, including English, Hindi, Tamil, and Bengali. The startup made headlines in January 2024 when it became the first pure-play AI startup in India to hit a unicorn valuation after its $50 million funding round. So far, it has secured $74 million in funding from backers such as Matrix Partners India, becoming one of the most well-funded AI startups in the country. Founded in 2023 by Vivek Raghavan and Pratyush Kumar, SarvamAI aims to develop custom-made LLMs, designed specifically for India-centric use cases. Backed by names such as Peak XV Partners and Khosla Ventures, the Bengaluru-based GenAI startup raised a Series A funding of $41 million led by Lightspeed Venture Partners in December 2023. Recently, the startup announced India's first open-source LLM, named Sarvam 2B. It is designed to support 10 Indian languages and is trained on an extensive dataset of 4 trillion tokens. Founded in 2021 by Mansoor Rahimat Khan and Siddharth Bhardwaj, this company has raised over $2.4 million in funding from investors including Capital2B (Info Edge), IvyCap Ventures, and Upsparks Capital. The company makes use of generative AI to create background music tailored for video, podcast, and game creators. https://youtu.be/uepqVSb0Ofg?si=vGX9HjOG8DS3g7MA Founded in 2014 by Ram Menon and Sriram Chakravarthy, Avaamo is a deep-learning software company that specialises in conversational interfaces to solve specific, high-impact problems in the enterprise tech realm. Avaamo is building fundamental AI technology across a broad area of neural networks, speech synthesis and deep learning to make conversational computing for businesses a reality. It counts PolyAI, Zira, Odeza, and wrnchAI as its competitors. Over the years, the company has raised over $30 million from the likes of Intel Capital, Ericsson Ventures, Streamlined Ventures, WI Harper Group and Mahindra Partners. It raised 7 million in its last funding round in 2021. Founded in 2019 by Sanket Shah and later joined by Anshul Khandelwal, InVideo initially operated a web-based video editing platform that allowed users to convert existing pieces of static content into videos. However, it has come a long way since then. Currently, the startup operates a full-fledged AI-powered video editing platform that utilises GenAI to create videos with text prompts. Users just have to input the topic and the platform generates a script, adds scenes and voiceovers, among other things. The startup has raised capital of $52.5 million to date and is backed by names such as Peak XV Partners, Tiger Global, Hummingbird, RTP Global and Base. It competes with the likes of Kapwing, Synthesia, Veed, and Rephrase.ai, among others. As of 2024, InVideo has over 7 million users globally. The platform is popular for its user-friendly AI video creation tools, offering more than 5,000 templates for various video formats. Jivi AI was founded in 2023 by Ankur Jain, a former Chief Product Officer at BharatPe and GV Sanjay Reddy, Chairman, Reddy Ventures. The company recently completed a seed round and is currently in discussions to close another round of funding. It uses machine learning, and large language models to revolutionise primary healthcare by offering AI-driven healthcare solutions. The company partners with doctors and hospitals to enhance patient care. Its focus includes digital health innovations and generative AI technologies Founded in 2020 by IIT-Kharagpur graduates Sneha Roy, Ankur Edkie, and Divyanshu Pandey, Murf AI uses AI to create high-quality voice-overs without recording equipment for its users in minutes. The growth-stage startup has raised a total funding of $11.5 million. In its last funding round, it raised $10 million in 2022. It is backed by investors like Matrix Partners, and Elevation Capital and counts Imaginario AI, VideoDubber, and Rephrase AI as its competitors. Founded in 2021 by Vishal Virani, DhiWise is an AI-enabled programming platform where developers can convert their designs into developer-friendly code for mobile and web apps. It automates and expedites the application development lifecycle and instantly generates readable, modular, and reusable code. The startup has raised a total of $9 million since its inception, raising $7 million in 2022. DhiWise is supported by marquee investors like Accel, AngelList India, Storm Ventures, Abhishek Deo, and Pentathlon Ventures. It competes with the likes of Observe AI, Pixis, QpiAI, and Kombai. Founded in 2018 by Deepti Prasad and Sanjay Kumar, Spyne is helping businesses and marketplaces create and upgrade high-quality product images and videos at scale with AI. The company has so far raised $7.6 million from Accel Partners, Storm Ventures, and other investors. It raised $7 million in its last funding round in 2022. The Gurugram-based startup competes with companies like zapero.ai, Dresma, Ayna, Blend, and Orbo AI.

Analytics India Magazine

Wed, 11 Sept, 12:04 PM UTC

Securing the data pipeline, from blockchain to AI

Generative artificial intelligence is the talk of the town in the technology world today. Almost every tech company today is up to its neck in generative AI, with Google focused on enhancing search, Microsoft betting the house on business productivity gains with its family of copilots, and startups like Runway AI and Stability AI going all-in on video and image creation. It has become clear that generative AI is one of the most powerful and disruptive technologies of our age, but it should be noted that these systems are nothing without access to reliable, accurate and trusted data. AI models need data to learn patterns, perform tasks on behalf of users, find answers and make predictions. If the underlying data they're trained on is inaccurate, models will start outputting biased and unreliable responses, eroding trust in their transformational capabilities. As generative AI rapidly becomes a fixture in our lives, developers need to prioritize data integrity to ensure these systems can be relied on. Data integrity is what enables AI developers to avoid the damaging consequences of AI bias and hallucinations. By maintaining the integrity of their data, developers can rest assured that their AI models are accurate and reliable, and can make the best decisions for their users. The result will be better user experiences, more revenue and reduced risk. On the other hand, if bad quality data is fed into AI models, developers will have a hard time achieving any of the above. Accurate and secure data can help to streamline software engineering processes and lead to the creation of more powerful AI tools, but it has become a challenge to maintain the quality of the expansive volumes of data needed by the most advanced AI models. These challenges are primarily due to how data is collected, stored, moved and analyzed. Throughout the data lifecycle, information must move through a number of data pipelines and be transformed multiple times, and there's a lot of potential for it to be mishandled along the way. With most AI models, their training data will come from hundreds of different sources, any one of which could present problems. Some of the challenges include discrepancies in the data, inaccurate data, corrupted data and security vulnerabilities. Adding to these headaches, it can be tricky for developers to identify the source of their inaccurate or corrupted data, which complicates efforts to maintain data quality. When inaccurate or unreliable data is fed into an AI application, it undermines both the performance and the security of that system, with negative impacts for end users and possible compliance risks for businesses. Luckily for developers, they can tap into an array of new tools and technologies designed to help ensure the integrity of their AI training data and reinforce trust in their applications. One of the most promising tools in this area is Space and Time's verifiable compute layer, which provides multiple components for creating next-generation data pipelines for applications that combine AI with blockchain. Space and Time's creator SxT Labs has created three technologies that underpin its verifiable compute layer, including a blockchain indexer, a distributed data warehouse and a zero-knowledge coprocessor. These come together to create a reliable infrastructure that allows AI applications to leverage data from leading blockchains such as Bitcoin, Ethereum and Polygon. With Space and Time's data warehouse, it's possible for AI applications to access insights from blockchain data using the familiar Structured Query Language. To safeguard this process, Space and Time uses a novel protocol called Proof-of-SQL that's powered by cryptographic zero-knowledge proofs, ensuring that each database query was computed in a verifiable way on untampered data. In addition to these kinds of proactive safeguards, developers can also take advantage of data monitoring tools such as Splunk, which make it easy to observe and track data to verify its quality and accuracy. Splunk enables the continuous monitoring of data, enabling developers to catch errors and other issues such as unauthorized changes the instant they happen. The software can be set up to issue alerts, so the developer is made aware of any challenges to their data integrity in real time. As an alternative, developers can make use of integrated, fully-managed data pipelines such as Talend, which offers features for data integration, preparation, transformation and quality. Its comprehensive data transformation capabilities extend to filtering, flattening and normalizing, anonymizing, aggregating and replicating data. It also provides tools for developers to quickly build individual data pipelines for each source that's fed into their AI applications. The adoption of generative AI is accelerating by the day, and its rapid uptake means that the challenges around data quality must be urgently addressed. After all, the performance of AI applications is directly linked to the quality of the data they rely on. That's why maintaining a robust and reliable data pipeline has become an imperative for every business. If AI lacks a strong data foundation, it cannot live up to its promises of transforming the way we live and work. Fortunately, these challenges can be overcome using a combination of tools to verify data accuracy, monitor it for errors and streamline the creation of data pipelines.

Dataconomy

Tue, 8 Oct, 2:58 PM UTC

Similar products

Avanzai

Create synthetic data that mirrors real-world financial datasets, streamlining the fine-tuning of large language models with precision and ease.

Contact for Pricing

YData

Generate synthetic data, manage data, improve data quality, and build the best datasets for your AI projects with the YData Fabric platform.

Contact for Pricing

Syntho

Explore the self-service AI generated synthetic data platform now to accelerate your data-driven tech solutions!

Contact for Pricing

3DFY

3DFY.ai uses artificial intelligence to create high-quality 3D models from just a text prompt or as little as a single image, enabling quick creation of 3D assets at scale.

Contact for Pricing

Datature

Datature is a complete AI vision platform that streamlines the dataset management, annotation, training, and deployment of computer vision models.

Contact for Pricing

Your Daily Dose of Curated AI News

Don’t drown in AI news. We cut through the noise - filtering, ranking and summarizing the most important AI news, breakthroughs and research daily. Spend less time searching for the latest in AI and get straight to action.

The Outpost

Top stories

News

About