AI News, Kaskada Accelerates ML Workflow with Its Feature Store artificial intelligence
- On 20. februar 2020
- By Read More
“Above the Trend Line” – Your Industry Rumor Central for 2/4/2020
In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.
Kaskada delivers an end-to-end platform for feature engineering and feature serving, including a collaborative interface for data scientists and robust data infrastructure for computing, storing, and serving features in production.
Kaskada provides a unified platform for data scientists and data engineers to share features and eliminate inefficiencies, allowing teams to operate as high-functioning data science factories …
Companies can use the Directly platform to understand the thousands of things their customers want, provide automated answers or actions to customer questions, and tap community experts to help the customer when the virtual agent can’t.
Yellowbrick Data, the modern analytical data warehouse built for the hybrid cloud, announced a technology partnership with MicroStrategy® Incorporated (Nasdaq: MSTR), a global provider of enterprise analytics software and services, that provides the market with a new integration of a Yellowbrick Data warehouse and MicroStrategy 2020™, MicroStrategy’s flagship enterprise analytics platform.
The combination of Yellowbrick Data’s modern analytical database and MicroStrategy 2020 is designed to bring actionable insights to the workforce, while enabling significantly faster performance for complex queries and support for high user concurrency.
With nearly 30 years of experience, McNerney will focus on driving greater brand awareness, scaling a global demand-generation organization, and mobilizing a community of passionate customers and partners that rely on the IBI platform.
With clients that range from Fortune 100 enterprises and federal agencies to innovative data-driven companies of every size in every major industry, McNerney has the opportunity to make the IBI community a competitive advantage for the company.
“However, we’re seeing the real world reach of AI expand with more companies looking at ways to foster collaboration, gain economies of scale and accelerate their AI paths from concept to production with maturing tools.
The demand for these skills are also starting to shape higher-ed curriculums to contend with this new wave of expectations.” “AI is already used by many retailers for functions such as customer service (through chatbots), product recommendations, and targeted advertising,”
“Questions around the ethics of AI are not new, but 2020 will be the year of reckoning as the industry builds out the best practices and regulations required for ensuring that AI works in the best interest of people.” The demand for AIOps in the enterprise will continue to rise as AI and machine learning have taken the industry by the jugular,”
“Due to an expansion in the number of workloads – both in public cloud and on-premises – and an increase in application complexity, investment in AIOps will increase and ultimately lead to better business outcomes.
Today’s challenges place a premium on differentiated vendor solutions powered by AI/ML and big data analytics techniques that can help modern IT operations evolve from traditional monitoring to observability to actionability.
But savvy enterprises have figured out that cloud data warehouses are just a better implementation of a legacy architecture, and so they’re avoiding the detour and moving directly to a next-generation architecture built around cloud data lakes.
We predict 75 percent of the global 2000 will be in production or in pilot with a cloud data lake in 2020, using multiple best-of breed engines for different use cases across data science, data pipelines, BI, and interactive/ad-hoc analysis.”
“But, for organizations to increase their reliance on AI, storage vendors will need to make it easier for AI applications to access more data faster, in turn helping the systems learn faster and unlock the value of the data.
Data scientists across all industries currently spend about 80% of their time on lower-value activity such as ingesting data, incrementally updating data, organizing and managing data, optimizing pipelines and delivering data to applications.
Those who truly harness the power of data via new, automated approaches to data operations and orchestration will thrive, as this will enable them to focus their data science talent on creating business value.
The impact of digital transformation will be felt across all segments of the economy – in expected (technology, financial services, retail/etail, etc.) and unexpected places (agriculture, home improvement, public sector, etc.).”
Just like Apache Spark is considered a leader for data transformation jobs and Presto is emerging as the leading tech for interactive querying, 2020 will be the year we’ll see a frontrunner dominate the broader model training space with pyTorch or Tensorflow as leading contenders.”
According to research, only 38 percent of companies have created a data-driven organization, and 91 percent of companies cite people and process challenges as the biggest barriers to becoming data-driven.
We can also expect to see the “data science pendulum” start swinging back: Recently, machine learning experts have been focused on optimizing their models to be as predictive as possible using inscrutable algorithms like deep learning;
but as infamous mistakes have been publicized and ethical issues have been raised, I anticipate we will start to see renewed interest in algorithms that can “explain” their classifications, and business processes that creatively combine human and machine input, rather than delegating fully to computers.