Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
When COVID hit the world a few months ago, an extended period of gloom seemed all but inevitable. Yet many companies in the data ecosystem have not just survived but in fact thrived. Perhaps most emblematic of this is the blockbuster IPO of data warehouse provider Snowflake that took place a couple of weeks ago and catapulted Snowflake to a $69 billion market cap at the time of writing – the biggest software IPO ever (see the S-1 teardown). And Palantir, an often controversial data analytics platform focused on the financial and government sector, became a public company via direct listing, reaching a market cap of $22 billion at the time of writing (see the S-1 teardown). Meanwhile, other recently IPO'ed data companies are performing very well in public markets. Datadog, for example, went public almost exactly a year ago (an interesting IPO in many ways, see my blog post here).
AI and automation are making it easier for users to find the data they need. Those of us beyond a certain age remember when school research projects began in front of the library card catalog: that now-antique set of wooden cabinets with the long drawers full of well-thumbed cards that adhered to a standard bibliographic system. If you understood that system (or had the help of a good librarian), you could perform a surprising amount of research at a metadata level before having to hunt through the library stacks for the actual books you needed. You could use the system to understand relationships between book topics and perhaps discover an unexpected book that was perfect for your report. Library catalogs, along with an increasing number of pre-digital-age storage systems, have changed.
Data Science has proven to be a boon to both the IT and the business. The innovation incorporates acquiring value from information, understanding the data and its patterns, and afterward anticipating or producing results from it. Data scientists play a fundamental job in this since they are responsible for organizing, evaluating, and studying data and its patterns. Not just having suitable qualifications and education, a successful data scientist must be skilled at a specific set of tools. He should be conversant in at least one of the tools from the lifecycle of a data science journey, in particular: data acquisition or capture, data cleaning, data warehousing, data exploration or analyzing, and finally, data visualization.
Do you remember a story about an irate father who marched into a Target to complain that his teenage daughter received maternity coupons, only to find out a few days later that she was pregnant? The story came from a 2012 New York times article and it signaled the arrival of predictive analytics. Despite reasonable skepticism over whether the story was real, it helped initiate an ethical debate over consumer privacy that has only intensified. Today, we live in a world with more powerful predictive capabilities and more personal data to be leveraged. We've reached an era in which AI can do more than out a teenage pregnancy.
Why does a secondary data store matter for AI? In my previous blog in this data store series, I discussed how the real selection criteria for an AI/ML data platform is how to obtain the best balance between capacity (cost per GB stored) and performance (cost per GB of throughput). Indeed, to support enterprise AI programs, the data architecture must support both high performance (needed for Ai training and validation) and high capacity (needed to store the huge amount of data that AI training requires). Even if these two capabilities can be hosted on the same systems (integrated data platform) or in large infrastructures, they are hosted in two separated specialized systems (two-tier architecture). This post continues the series of blogs dedicated to data stores for AI and advanced analytics.
With the rapid increase in software development and data science, Artificial Intelligence (AI) and Automated Machine Learning (AutoML) have been evolving at an impressive rate. AutoML is on its way to create a new wave of progress and provide developers with critical skills. With the rise of big data, advanced analytic, and predictive models since the beginning of lockdown, AI and ML are rapidly developing and thriving. AutoML is the process of automating the process of applying Machine Learning to real-world problems. AutoML covers the full pipeline from the raw dataset to the deployable ML model and was proposed as an AI-based solution to the ever-growing challenge of applying machine learning.
With the continuation of COVID disruption, businesses are still relying on webinars to carry out their marketing strategies. Although the concept of webinars has always been there, the pandemic has provided great importance to it while engaging audiences in the comforts of their home. Not only these webinars are a shorter form of conferences, saving an ample amount of time, but also turns out to be extremely convenient for attendees to get their hands-on industry insights, latest tools and technologies. Further to this, webinars have also proven to be a great learning resource for many enthusiasts as well as professionals. Alongside, with artificial intelligence gaining its massive momentum amid COVID, the number of webinars on AI is also rapidly increasing. About: Organised by Analytics India Magazine, in association with ISIMA, this webinar will cover the three generations of data architecture and what lies ahead.
Baskerville is a machine operating on the Deflect network that protects sites from hounding, malicious bots. It's also an open source project that, in time, will be able to reduce bad behaviour on your networks too. Baskerville responds to web traffic, analyzing requests in real-time, and challenging those acting suspiciously. A few months ago, Baskerville passed an important milestone – making its own decisions on traffic deemed anomalous. The quality of these decisions (recall) is high and Baskerville has already successfully mitigated many sophisticated real-life attacks.
The new solution is geared towards cutting through red tape and paperwork for borrowers and lenders. Google has announced the launch of Lending DocAI, a dedicated artificial intelligence (AI) service for the mortgage industry. On Monday, Google Product Manager Sudheera Vanguri said the new solution, now in preview, has been designed to transform unstructured datasets into accurate models able to speed up loan applications by accurately assessing a borrower's income and assets. To streamline the loan application process, dubbed "notoriously slow and complex" by Vanguri, Lending DocAI has been built with AI models that specialize in document types related to loans and is able to automate "routine" document reviews so mortgage providers don't have to. The executive says that in turn, this will speed up the mortgage and loan application workflows, including the processing of loan sources and mortgage services.
Companies often kiss more frogs than princes when it comes to artificial intelligence investments. Much has changed this year. The new normal brought about by the COVID-19 pandemic has affected everyone and everything. Globally, companies have been forced into crisis management mode: adapting, retooling and training employees to work remotely while adopting new technologies to stay productive. During all of this, corporate CEOs are trying to accomplish three primary objectives for their business: maximize growth, minimize risk, and protect margins. They have become even more challenging in manufacturing, considering the new work rules, the changing corporate remote work policies, and the use of conventional manufacturing technologies.