WEKA

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WEKA is an AI-Native data platform designed to provide mindbending speed, seductive simplicity, infinite scaling, and effortless sustainability for cloud and AI workloads.

WEKA Image

WEKA Image

How WEKA can help you:

Eliminates data staging and storage silos, enhancing AI and Machine Learning efficiency.
Turbocharges cloud workloads, accelerating project timelines affordably.
Addresses complexities of parallel file systems in HPC environments on-premises or in the cloud.
Improves High-Performance Data Analytics (HPDA) outcomes with faster time-to-results regardless of scale.
Supports cloud-native application development with optimized persistent storage solutions.
Facilitates a seamless hybrid cloud experience, allowing for application deployment anywhere without compromising on performance.

Why choose WEKA: Key features

Uncompromising speed for high I/O, low latency, and mixed workload support with no tuning required.
Simplified data management that eliminates storage silos across on-premises and cloud environments.
Scalable architecture to handle tens of millions or even billions of files of all types and sizes.
Sustainability-focused with lower energy consumption and reduced carbon emissions through efficient data pipeline management.

Who should choose WEKA:

Enterprises and research organizations looking for a data platform to support demanding AI and Machine Learning workloads.
Cloud architects seeking to boost cloud workloads performance while maintaining affordability.
HPC and HPDA professionals in need of a solution to eliminate system complexities and achieve faster outcomes.
Development teams requiring persistent storage for cloud-native applications.
Organizations aiming for a hybrid cloud strategy without sacrificing speed, scale, simplicity, or sustainability.

About WEKA

Website

https://www.weka.io

Release Date

March 2024

Pricing

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Related fields

Categories

Anomaly detection, Deep learning, Computer vision, Engineering, Predictive analysis, Natural language processing, Cloud computing

Related News

Vast Data is making itself essential to the AI ecosystem - SiliconANGLE

Vast Data aims to be guiding star for AI stack ahead of Cosmos event Data storage is all the rage again, with global data predicted to reach 200 zettabytes by 2025, half of which will be in the cloud. Only five years ago, that number was 41 zettabytes. One of the primary reasons for the rapid increase in data volume is the shift from text-based to multimodal models and the development of super intelligent and artificial intelligence systems. Many companies are battling for the AI crown, but Vast Data Inc., the fast-growing data computing platform, has positioned itself as the backbone for a data space shaped by machine learning. The company's prospects look bright -- the market for next-generation storage is anticipated to grow to $150 billion in 2032, at a compound annual growth rate of 10%, and Vast has lucrative partnerships with Nvidia Corp. and cloud service providers. One would do well to ask what Vast is bringing to data storage and how it fits into the AI ecosystem. "There's a huge data problem," said Jeff Denworth, co-founder of Vast, in an exclusive interview with theCUBE. "We sit in the center of that whole pipeline. Our system is both a distributed enterprise data warehouse and a distributed unstructured data store, so every single step of the pipeline can be solved with a unified high-performance and extremely affordable enterprise platform. It's way more than just training. There's a whole lot more that goes into it." Last year, Vast introduced its Data Platform on theCUBE, SiliconANGLE Media's livestreaming studio, selling it as a way for developers to interact with data regardless of file type or location. Now, the company is preparing to introduce its Data Engine, another layer of software that orchestrates events in the AI pipeline at its Cosmos event in October. TheCUBE will be providing exclusive coverage of the event. (* Disclosure below.) Vast has taken an unconventional approach to data, fusing the concepts of data storage and database in order to manage both unstructured and structured data for the era of AI. Users can interface with all of their data, on-premises and in the cloud, on Vast's distributed platform. "In my career, I've seen a lot of data companies start expanding beyond their core capabilities into the stack," said Sanjeev Mohan, industry analyst and principal at SanjMo, during Vast's Build Beyond event. "This is the first time I've seen a storage company come into the picture and start offering all of these new capabilities." The Vast Data Platform has four components: the DataStore, DataSpace, DataBase and DataEngine. The DataStore provides universal storage based on the clusters of their Disaggregated Shared Everything Architecture. DASE builds on Google's concept of shared-nothing systems by separating compute logic from the system state. This essentially allows clusters to scale up the platform's data capacity independent of the CPUs. Eliminating east-west traffic is one of the biggest features of the Vast DataBase, a transactional data warehouse that merges the capabilities of traditional databases, data warehouses and data lakes. It offers an alternative to Snowflake Inc. and Databricks, Inc.'s data lake or data lakehouse architecture, focusing on the unstructured data being processed by AI. "We started from the storage layer," Denworth said. "The reason that we wanted to do this is that we saw deep learning and the power of these really big AI supercomputers that Nvidia was going to be building, requiring a more scalable, more high-performance, real-time architecture. We think about [Vast DataBase] as an exabyte scale, transactional data warehouse, designed intentionally for the power of flash and when you put these two things together, now you have this unlimited scale information architecture that turns unstructured data into structured data, and those aren't the types of things we think Snowflake and Databricks are really working on." By streaming data into the platform at any level of scale, Vast claims to have closed the observability gap present in data lake and data lakehouse architecture. On top of that, you have the Vast DataSpace, which unifies multiple clouds and allows users to flow all of their tabular files and records into one pipeline. "What comes out of it is a product that is more efficient and more scalable than the products that [Snowflake and Databricks] built for their purposes but is so much more broad in its capability because it also supports these different data types that don't naturally fit in a data warehouse," Denworth said. Vast is all in on flash, with an architecture that makes flash more affordable and solves for the input/output (IO) bottlenecks that have previously plagued the storage market. The result is anywhere between a two and 20 times improvement in pipeline performance, according to Denworth. The final layer of simplification appears to be Vast's DataEngine, which builds on the company's collaboration with Nvidia and provides a server-level engine to orchestrate AI pipelines. It is based on event triggers and functions -- any I/O event in the DataStore can trigger a function for the DataEngine to execute without explicit instruction from the AI developer. "We [saw] the application stack, broadly speaking, on top of all those data problems ... and realized that that is equally complicated," Denworth said. "Considering that these are all data-driven challenges that customers are facing, what we wanted to do was pull the application stack into our systems to make it easier for organizations to go and implement enterprise AI across organizations and unlock the secrets that's in their data using AI tools." In March, Vast announced that it would be using Nvidia's BlueField-3 data processing unit technology to perform its data storage services at a massive scale, as well as a partnership with Super Micro Computer Inc., a global supplier of AI hardware solutions. Denworth hints at a flurry of announcements with key partners, including major leaders in the IT industry, at the Cosmos event in October. "The objective is to basically start to enable real-time, data-driven AI across every enterprise," he said. Another set of Vast collaborators are cloud GPU providers, which are growing in popularity, in part because they allow companies to rent expensive computer resources instead of buying them. "The thing that CSPs have more than anything else is power and the ability to build scalable and reliable infrastructure really quickly," said Denworth, in an earlier interview. "They become the destination for next-generation enterprise workloads that simply aren't able to be deployed in a customer's four walls." Vast has already announced partnerships with AI cloud providers CoreWeave and Lambda Labs, so we can look forward to seeing how its various collaborations have borne fruit in October, as well as details on the Vast DataEngine. "You're going to see us hang out the promises we made last year in terms of furthering a next-generation, game-changing systems architecture that solves a ton of customer problems," Denworth said. "Vast is in a leadership position as organizations start to reshape their infrastructure agenda in the world of AI. There is a movement happening around and within Vast Data that is just too big to be ignored anymore."

SiliconANGLE

Fri, 23 Aug, 10:00 PM UTC

NTT DATA in India launches Accelerated AI Platform to simplify complex projects

Platform eases AI adoption with full suite of AI services, cutting-edge GPU infrastructure and pay-as-you-go pricingDesigned to aid enterprises, startups, government bodies, and educational institutions in solving the challenges of AI deployment NTT DATA, a leading IT infrastructure and services company, today announced the launch of its new Accelerated AI Platform in India. The comprehensive solution is designed to empower businesses with advanced AI capabilities without the complexities of traditional implementations that include: Expert guidance from a team of AI specialists for end-to-end support, from conceptualization to deployment and optimizationFlexible and modular solutions to help clients match their requirements to the right combination of infrastructure, development components and servicesProprietary AI portfolio including accelerators, frameworks, and tools to enhance effectiveness and efficiencyCutting-edge GPUs (Graphics Processing Units, that are specialized hardware) designed to handle complex calculations quickly, making them ideal for demanding AI workloadsCost optimization with a pay-as-you-go model that helps clients and enables access to advanced infrastructure without the burden of capital expenditure. "AI initiatives are often complex, resource-intensive, and challenging to implement, which is why we've developed an intelligent platform that can address these challenges directly. The Accelerated AI Platform's launch comes at a time when there is a growing demand for high-performance computing resources, while India's current growth rate in AI adoption has reached an annual CAGR of 25-35%," said Avinash Joshi, Chief Executive Officer, India, NTT DATA. "Our platform, built on powerful infrastructure with advanced GPU resources, is a step forward in our commitment to ensure the success of our clients' AI projects. It offers expert guidance from conceptualization to deployment, with our own extensive IP portfolio and the choice of modular solutions along with the compute resources that can tackle even the most challenging AI workloads," he added. Designed primarily for Indian enterprises, startups, government bodies, and educational institutions that are adopting AI and high-performance computing (HPC) technologies, the platform provides on-demand access to cutting-edge technology and comprehensive services. Doing away with the need for heavy capital investment in hardware, the platform provides on-demand access to the latest GPU technology which are scalable and available for short-term and long-term requirements tailored to the application's requirement. With the comprehensive suite of AI services, organizations will be able to support application needs like training, creating foundational models, Retrieval-Augmented Generation (RAG), inference services, fine tuning and more. NTT DATA in India has partnered with Neysa, an AI cloud and platform-as-a-service (PaaS) startup, and an NTT DATA-funded company to fulfil the infrastructure requirements for this platform. This includes the high-end GPUs required to run cutting-edge LLMs (Language Model Models) on which the AI is built. Neysa provides the components necessary to deliver the infrastructure and associated areas in an as-a-service model. This encompasses identifying and delivering the right mix of cutting-edge GPUs needed for both training and inferencing workloads. NTT DATA has integrated these components into the Accelerated AI platform, which will be used to deliver end-to-end AI-related services to clients. To learn more about how NTT DATA's Accelerated AI Platform can help your business, please contact us at [email protected] About NTT DATA NTT DATA is a $30+ billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize, and transform for long-term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation, and management of applications, infrastructure, and connectivity. We are also one of the leading providers of digital and Al infrastructure in the world. NTT DATA is part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future.

CXOToday.com

Wed, 28 Aug, 10:04 AM UTC

Enterprises Embrace Cloud-Based Unified AI and Data Platforms

A new report from Information Services Group (ISG) reveals a growing trend among enterprises to adopt cloud-based unified AI and data platforms. This shift aims to enhance data management, improve AI capabilities, and drive digital transformation.

3 Sources

Sat, 7 Sept, 12:01 AM UTC

AIM Research launched VendorAI, A Comprehensive AI Vendor Database for Strategic Decision-Making

VendorAI by AIM Research is a comprehensive AI vendor database profiling over 200 vendors, providing deep insights for M&A, competitive intelligence, and vendor selection. The AI industry is growing rapidly, with vendors offering a range of solutions across various domains such as data science, data engineering, AI observability, generative AI services, computer vision, and data annotation. This expansion brings with it the challenge of selecting the right vendors, understanding the competitive landscape, and identifying potential acquisition targets. To address this need, AIM Research is proud to launch VendorAI, a comprehensive database that profiles over 200 AI vendors, offering in-depth insights into their capabilities, expertise, and performance. VendorAI is designed for businesses seeking to enhance their strategic decision-making, whether for mergers and acquisitions (M&A), competitive intelligence, or vendor selection. The platform brings together a wealth of data in one place, structured to help users make informed choices when navigating the AI vendor ecosystem. VendorAI features a detailed set of data columns that cover all aspects of an AI vendor's operations, leadership, technology, and competitive advantage. These columns include: In the world of M&A, finding the right target company for acquisition requires access to deep insights and structured data. VendorAI makes this process easier by offering a comprehensive view of AI vendors, allowing businesses to assess potential acquisition targets based on a range of critical metrics. For example, companies can evaluate vendors using the AIM PeMa Score, which aggregates performance metrics to provide a high-level understanding of a vendor's market strength. Furthermore, tracking employee growth over the past six months can reveal whether a vendor is on an upward trajectory or facing operational challenges. Insights into proprietary platforms and flagship technologies can help businesses identify vendors with unique intellectual property or technological innovations that could complement their own portfolios. VendorAI's leadership data is also invaluable for M&A activities, providing easy access to the backgrounds of key figures like CEOs and founders. Understanding the leadership team's experience and professional networks can help acquiring companies evaluate the strength of the management team, which is often critical to the success of a post-acquisition integration. Staying ahead of the competition in the rapidly evolving AI market requires constant monitoring of the competitive landscape. VendorAI offers businesses a structured way to gather competitive intelligence, allowing them to track the activities, innovations, and performance of key players in the industry. With information on each vendor's employee growth, delivery centers, client locations, and industry specialization, businesses can gain a granular understanding of where their competitors are investing resources and expanding their market presence. The platform's insights into proprietary platforms and flagship technologies allow users to identify which vendors are innovating and developing cutting-edge solutions. VendorAI's focus on tracking the number of Fortune 500 clients a vendor serves also provides a valuable indicator of market credibility. Vendors with significant Fortune 500 clients are often seen as more established and trustworthy, making them key players to watch in the competitive landscape. By using VendorAI, businesses can benchmark their competitors' strengths and weaknesses, identifying opportunities for differentiation or areas where they may need to catch up. The platform also enables businesses to keep an eye on emerging vendors, helping them stay ahead of market trends and potential disruptors. Choosing the right vendor in the AI space can be a daunting task, especially with so many options available. VendorAI simplifies this process by providing a structured and detailed view of over 200 vendors, allowing businesses to compare their offerings, industry expertise, and technology platforms. With detailed data on solution types, proprietary platforms, industry specialization, and years of operation, businesses can use VendorAI to match their specific needs with the capabilities of vendors. The platform's filtering options, such as applicable tags for company selection, make it easy to narrow down vendors based on specific criteria such as geographic location, industry focus, or technology expertise. In addition, VendorAI's insights into the number of Fortune 500 clients served by each vendor can be a key factor in decision-making. Vendors with significant Fortune 500 clients often have proven experience in handling large, complex projects, making them strong candidates for businesses with similar requirements. The USP/Competitive Advantage column provides further differentiation, helping businesses identify vendors with unique capabilities that set them apart from the competition. Whether a company is looking for a vendor with a specific technology platform or one with expertise in a niche industry, VendorAI provides the data necessary to make an informed choice. VendorAI joins AIM Research's existing portfolio of industry-leading tools designed to support businesses in strategic decision-making. Alongside VendorAI is GCC Explorer, AIM Research's comprehensive list of Global Capability Centers (GCCs) in India. GCC Explorer offers detailed insights into the GCC ecosystem, helping businesses understand the operational landscape and identify potential partners or acquisition targets. Together, VendorAI and GCC Explorer provide a holistic view of both the AI vendor landscape and the GCC ecosystem, enabling businesses to make informed decisions that align with their long-term strategic goals. With over 200 AI vendors profiled across key categories like data science, data engineering, AI observability, generative AI services, computer vision, and data annotation, VendorAI provides the insights needed to make strategic decisions with confidence. By leveraging the platform's rich data set, businesses can navigate the complex AI vendor landscape, identify potential acquisition targets, gain competitive intelligence, and choose the right vendors to meet their specific needs. Whether you're a company looking to expand through M&A or simply trying to stay ahead of the competition, VendorAI is the ultimate tool for strategic decision-making in the AI industry.

Analytics India Magazine

Sat, 14 Sept, 12:00 PM UTC

Oracle boosts AI development features for analytics and HeatWave managed service - SiliconANGLE

Oracle boosts AI development features for analytics and HeatWave managed service Oracle Corp. will use its Oracle CloudWorld conference today to announce new artificial intelligence capabilities spanning cloud database, analytics and development platform, led by a raft of new features for its HeatWave managed MySQL database service. Saying some organizations are building generative AI applications with HeatWave and moving them into production in less than a month, Oracle said features such as in-database large language models reduce the need for AI expertise, manual integrations and complex security provisions. Customers using HeatWave on the Amazon Web Services Inc. cloud can replace up to six unique AWS services with a single HeatWave instance, the company said. They can automate vector store creation and vector embedding generation, use in-database LLMs on CPUs or with models from Amazon Bedrock, and converse with documents in Amazon S3 at speeds that Oracle claimed exceed alternatives from vendors such as Snowflake Inc., Databricks Inc. and Google LLC. A lakehouse -- which is a unified data architecture that combines the scalability and flexibility of a data lake with the reliability and performance features of a data warehouse -- built on top of HeatWave allows high-performance queries on structured, semistructured and unstructured data in Amazon S3 storage. It supports queries across hundreds of terabytes of data in object storage at database query speed without copying the data from the object store to the database. New features in the lakehouse enable users to share query results and store them in object storage, as well as to use HeatWave for MapReduce applications. Changes to data in object storage are automatically detected, and updates are propagated to the lakehouse. New native JavaScript support executes stored procedures and functions written in HeatWave's native scripting language for uses like querying data in object storage. Autopilot indexing enables AWS users to predict the optimal set of indexes needed for their transaction processing workloads. New generative AI features include support for documents in 27 languages within the vector store. Optical character recognition can be used to conduct similarity searches using scanned documents. LLM inference batch processing improves application throughput by executing multiple requests simultaneously across a cluster. Changes to documents in object storage automatically trigger updates to corresponding vector embeddings. AutoML, a no-cost feature that Oracle said includes everything needed to build, train, and explain machine learning models, is getting a richer training data set with four times the capacity of the previous version. Topic modeling can be used to discover insights in large textual data sets by interpreting themes in documents. Data drift controls helps detect differences between the data used for training and new data. Semisupervised log anomaly detection provides feedback on the results of unsupervised anomaly detection and uses the labeled data to improve subsequent predictions. Oracle also said HeatWave is now available in the OCI Always Free Service, which can be used to build and run small-scale applications. All OCI accounts have unlimited access to a standalone HeatWave instance in their home region with 50 gigabytes of storage and 50 gigabytes of backup capacity. Oracle also said it plans to make its Intelligent Data Lake a foundational component of its Data Intelligence Platform beginning in 2025. Announced a year ago, the Intelligent Data Lake is a platform for storing, managing and analyzing large volumes of structured and unstructured data. It provides unified data management, AI-powered data discovery and cataloging capabilities, built-in governance tools and scalable storage. The Data Intelligence Platform is integrated with Oracle's Autonomous Data Warehouse, Analytics Cloud, HeatWave and various third-party services. The addition of Intelligent Data Lake will provide a unified developer experience, including data cataloging capability, Apache Spark and Apache Flink compatibility and a Jupyter Notebook for data analysis and visualization, Oracle said. The combined services will enable customers to build a full-scale data lake, connect and extend analytical applications with real-time data from any source, inventory assets, transform data and fully orchestrate data with unified governance and security. The data lake will also offer zero copy, a technique that transfers data between different parts of a system without making multiple copies. In addition, Oracle announced the availability of the Oracle Analytics Cloud AI Assistant, which translates natural language into actions. The Analytics Cloud AI Assistant understands the context of a user's question using a built-in LLM optimized for analytics conversations and tasks that recognizes the Oracle Analytics workbook and datasets. Customers can optionally use other own LLMs. Oracle is also announcing generative development AI-centric application development infrastructure. It's intended to provide a set of development technologies for building applications that use AI-powered natural language interfaces and human-centric data. GenDev combines technologies in Oracle Database 23ai, including JavaScript Object Notation Relational Duality Views, AI vector search and Oracle's Apex low-code development platform. JSON Relational Duality Views is a hybrid data management approach that bridges relational databases with JSON data formats. It allows developers to work with structured relational and semistructured JSON with flexible access, manipulation and storage. AI Vector Search uses machine learning models to represent data -- such as text, images and audio recordings -- as multidimensional vectors. This allows for more nuanced searching capabilities compared to traditional keyword-based search methods, which are typically limited to exact pattern matches. Oracle said the combination helps developers build AI applications more quickly and at lower risk. Data complexity is handled at the data layer, and application data rules covering such areas as intent, confidentiality, validation and integrity are enforced by the data engine. Oracle Database 23ai supports all data types and workloads without sacrificing the data consistency, performance and availability. Users can interact with data and applications using natural language and find data based on its semantic content. Other features of Database 23ai that assist in AI development include precise responses to natural language questions using large language models and with enterprise data, a feature intended to reduce the risk of hallucinations. The database features built-in integration with 35 different LLMs across seven providers. Users can access Nvidia Corp. graphic processing units without needing to provision or manage GPU servers. Oracle also provides a set of machine learning notebooks that use GPU-enabled Python packages for resource-intensive workloads, such as generating vector embeddings using transformer models and building deep learning models. Users can prepare and load data using natural language and a visual drag-and-drop tool. They can also build operational property graph models without code with a built-in self-service tool. For developers working with the Autonomous Database in the cloud, these features are available at a flat hourly rate starting at 39 cents per hour. Autonomous Database for Developers is also available as a downloadable container image.

SiliconANGLE

Tue, 10 Sept, 2:04 PM UTC

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