Nowadays, machines, robots, droids, and computers are helping a lot of businesses to succeed. Because of these advanced technologies, it reduces working hours, saves expenditures, and expands business quickly. At Lotus Labs Inc, we help companies transform into AI-driven enterprises. Our AI platform Padmé provides a comprehensive solution to deploy machine learning models at the edge with a click of a button. We are a team of innovative professionals passionate about emerging technology.

This article was published as a part of the Data Science Blogathon. Often we have heard the term Automated Machine Learning or AutoML being used when it comes to machine learning, but what is AutoML, and is the hype around it real? The article is a deep-dive on AutoML, how it can make the power of machine learning available to everyone, and how certain AutoML tools can be tailored to your business needs. Today, companies are incorporating AI into their internal processes to improve the quality of their product pipeline, sales forecasting, predicting customer needs, designing new products/ inventory, and even optimizing their workplaces. As AI/ ML continues to become important for businesses, its challenges have also become more recognizable, particularly when it comes to accessibility of AI and data science.

Juniper CTO Raj Yavatkar joins the Light Reading podcast to discuss Juniper's strategy to use machine learning and AI to help customers build "self-driving" enterprise networks. The acquisition of Mist Systems brought that capability to Wi-Fi access points, he explains. Once Juniper completes its acquisition of 128 Technology, it can improve on its AI-driven approach to its SD-WAN and WAN assurance offerings, too. You can find all of Light Reading's editorial and custom audio programs on Apple Podcasts, Google, SoundCloud or Spotify.

As many as 57% of chief data and analytics officers have said that Covid-19 has increased demand for AI, digital products and tools in enterprise. The findings were published in the report Building AI-driven enterprises in a disrupted environment, by global analytics software firm FICO and market intelligence firm Corinium. Together they surveyed more than 100 C-level analytic and data executives. The study found that uncertainties caused by the pandemic have forced many organisations to adopt a more committed, disciplined approach to becoming an AI-driven enterprise. In addition to rates of take-up, the survey also questioned respondents on ethical and responsible AI, and competitive advantages AI could generate post-Covid.

All Indian Reprints of O'Reilly are printed in Grayscale While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs. Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You'll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.

The central premise of this book is that value at the enterprise is created by making decisions, not with data or predictive technologies alone. Nonetheless, we can piggyback on the big data and AI revolutions and start making better choices in a systematic and scalable way, by transforming our companies into modern AI- and data-driven decision-making enterprises. To make better decisions, we first need to ask the right questions, forcing us to move from descriptive & predictive analyses to prescriptive courses of action. I devote the first few chapters to clarifying these concepts and explaining how to ask better business questions suitable for this type of analysis. I then delve into the anatomy of decision-making, starting with the consequences or outcomes we want to achieve, moving backward to the actions we can take, and discussing the problems and opportunities created by intervening uncertainty and causality.

For data-driven organizations, machine learning offers powerful techniques for unlocking the value of data and enabling artificial intelligence applications. But for many organizations, there are obstacles on the road to machine learning: a skills shortage and the ability to build and deploy models quickly. Without tools that automate and simplify machine learning processes, organizations can be stymied by a lack of data scientists who understand the complexities of building intelligent systems with the ability to learn from data and improve over time. These aren't skills that the typical IT shop has in abundance. . . This is where the DataRobot enterprise AI software platform enters the machine learning picture.

DataRobot, the creator of the automated machine learning category, has raised $100 million in Series D funding, bringing its total funding to $225 million. The round was led by Meritech and Sapphire Ventures, with participation from DFJ Growth and 16 previous investors including NEA, IA Ventures, and Intel Capital. Meritech's Rob Ward and Sapphire's Jai Das have joined the Board of Directors. DataRobot was founded by experienced applied data scientists in 2012, long before the current resurgence of AI. Their mission was to develop technology to automate the process of building and operationalizing machine learning models.

BOSTON--(BUSINESS WIRE)--DataRobot, the creator of the automated machine learning category, has raised $100 million in Series D funding, bringing its total funding to $225 million. The round was led by Meritech and Sapphire Ventures, with participation from DFJ Growth and 16 previous investors including NEA, IA Ventures, and Intel Capital. Meritech's Rob Ward and Sapphire's Jai Das have joined the Board of Directors. DataRobot was founded by experienced applied data scientists in 2012, long before the current resurgence of AI. Their mission was to develop technology to automate the process of building and operationalizing machine learning models.

London – May 23, 2018 – DataRobot, a pioneer in automated machine learning, announced today the release of a new version of the DataRobot Cloud platform for the European Union (EU). The DataRobot EU Cloud, available on the Amazon Web Services (AWS) EU (Ireland) Region, is able to support the unique needs of EU customers, whose data is tightly regulated, as well as help enterprises conform to the upcoming General Data Protection Regulation (GDPR) requirements. With growing sensitivities over data privacy and breaches, the need for organisations to keep and process customer data at the point of origin is vital. With the introduction of GDPR and its 25 May 2018 enforcement date, organisations are also required to develop a strategy to prove compliance or face hefty fines. The DataRobot EU Cloud ensures European organisations adhere to enhanced data sovereignty requirements without sacrificing any enterprise-grade capabilities.