If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Snowflake is a cloud data warehouse provided as a software-as-a-service (SaaS). It consists of unique architecture to handle multiple aspects of data and analytics. Snowflake sets itself apart from all other traditional data warehouse solutions with advanced capabilities like improved performance, simplicity, high concurrency and cost-effectiveness. Snowflake's shared data architecture physically separates the computation and storage which is not possible by the traditional offerings. It streamlines the process for businesses to store and analyze massive volumes of data using cloud-based tools.
Snowflake launched its Financial Services Data Cloud with more than 600 industry data partners as well as large customers including BlackRock, Capital One and Western Union. In February, Snowflake forged a partnership with BlackRock's Aladdin unit to launch the Aladdin Data Cloud. That partnership paved the way for a broader Financial Services Data Cloud launch. The general idea is that financial services firms can combine their data with third party data on Snowflake's platform to test and adjust models, market to clients and manage risk. At a high level, Snowflake's Financial Services Data Cloud combines the company's governance tools, industry-specific datasets and clients' first party data.
The latest winner of the growing interest in enterprise AI is Databricks, a startup that has just secured $1.6 billion in series H funding at an insane valuation of $38 billion. This latest round of investment comes only months after Databricks raised another $1 billion. Databricks is one of several companies that offer services and products for unifying, processing, and analyzing data stored in different sources and architectures. The category also includes Snowflake, which made a massive IPO last year and has a market cap of $90 billion, and C3.ai, another enterprise AI company that went public last year. Why are investors enamored with companies like Databricks?
Artificial intelligence, is the magic technology stimulating intelligent behavior in machines. The core concept of artificial intelligence is to train machines to mimic human activities in performing routine and labor-intensive tasks. Moving out of the confined box, today, artificial intelligence is also being trained to carry out intellectual works like difficult calculations, decision-making, coming up with solutions, etc. The combination of science and engineer, which emerged as artificial intelligence technology, has revolutionized the business industry as well. In the digital world, artificial intelligence companies are providing innovative solutions to almost all sectors.
This story is part of "The New Database," a Protocol Manual. It's a great time to be a database company. Money is flowing into the sector in historic amounts, creating a rush of new startups that command huge valuations. In 2020, startups developing traditional databases, which couple the processing engine with storage, took in $2.3 billion in funding across 54 deals, up from $849 million in 2019, according to data from CB Insights. That number doesn't even encompass the newer entities that are decoupling compute from the repositories.
Tech workers Jared Parker and Patrick Dougherty first met three years ago when they "took data scientists out for lunch and dinners and just heard them complain about their current world," Parker told Insider. The pain point they heard again and again, according to Parker, was "Why am I spending all my time extracting, exploring, cleaning, joining, transforming raw data into a set of features that can be consumed by my model?" Their answer to that frustration is Rasgo Intelligence, a startup they founded a year ago during the height of the pandemic, that helps data scientists prep their data, reuse code, and ultimately build machine learning models much more efficiently. On Thursday, the New York-based startup raised a $20 million Series A led by Insight Partners with participation from Unusual Ventures. This latest round brings its total funding to over $25 million; the startup declined to disclose its valuation.
C3 AI and Snowflake are partnering to give Snowflake customers access to C3 AI's development tools and enterprise applications, including AI-driven CRM, predictive maintenance, supply network optimization, and fraud detection apps, the companies announced. Billed as a way to "deliver next-generation enterprise AI applications at scale," the partnership will make C3 AI's suite of Integrated Development Studio (IDS) tools -- including C3 AI Data Studio, C3 AI ML Studio, C3 AI App Studio, C3 AI DevSecOps Studio, and C3 AI Marketplace -- available to Snowflake users. Customers using Snowflake's cloud-based data warehousing platform will also get access to C3 AI's AI Suite of operational and security apps based on the C3 AI model-driven architecture. Services provided by those apps include data persistence, batch and stream processing, time-series normalization, auto-scaling, data encryption, attribute and role-based access control, and AI/ML services. "The C3 AI Suite and C3 AI's prebuilt enterprise-grade models significantly speed and simplify the development of enterprise AI applications. As our customers deploy enterprise AI applications at scale, integration with C3 AI to Snowpark will accelerate the development and deployment of complex AI and machine learning use cases," Snowflake senior vice president Christian Kleinerman said in a statement.
C3.ai, the twelve-year-old Silicon Valley startup that is bringing machine learning forms of AI to various industries such as oil and gas, on Wednesday said it is partnering with data analytics upstart Snowflake, the cloud-based vendor of data warehouses and other wares. The duo promised to take customers from start to production deployment of apps in one month. The arrangement provides for Snowflake users to "be provided with access to the C3 AI Suite and pre-built C3 AI applications that address a range of industries and enterprise AI use cases," the two companies said. C3.ai's chief product officer, Houman Behzadi, said the partnership "will create significant time and operational efficiencies for Snowflake's customers and solidify Snowflake as the operational data platform of choice for enterprise AI applications." Snowflake's leader of its product efforts, Christian Kleinerman, commented that the collaboration "will accelerate the development and deployment of complex AI and machine learning use cases," adding that the "C3 AI Suite and C3 AI's pre-built enterprise-grade models significantly speed and simplify the development of enterprise AI applications."
Inside a 13th-floor boardroom in downtown San Francisco, the atmosphere was tense. It was November 2015, and Databricks, a two-year-old software company started by a group of seven Berkeley researchers, was long on buzz but short on revenue. The directors awkwardly broached subjects that had been rehashed time and again. The startup had been trying to raise funds for five months, but venture capitalists were keeping it at arm's length, wary of its paltry sales. Seeing no other option, NEA partner Pete Sonsini, an existing investor, raised his hand to save the company with an emergency $30 million injection. Founding CEO Ion Stoica had agreed to step aside and return to his professorship at the University of California, Berkeley. The obvious move was to bring in a seasoned Silicon Valley executive, which is exactly what Databricks' chief competitor Snowflake did twice on its way to a software-record $33 billion IPO in September 2020.
Data preparation remains a major challenge in the machine learning (ML) space. Data scientists and engineers need to write queries and code to get data from source data stores, and then write the queries to transform this data, to create features to be used in model development and training. All of this data pipeline development work doesn't really focus on the building of ML models, but focuses on the building of data pipelines necessary to make the data available to the models. Amazon SageMaker Data Wrangler makes it easier for data scientists and engineers to prepare data in the early phase of developing ML applications by using a visual interface. Data Wrangler comes with over 300 built-in data transformations to help normalize, transform, and combine features without writing any code.