Today HPE announced a container-based software solution, HPE ML Ops, to support the entire machine learning model lifecycle for on-premises, public cloud and hybrid cloud environments. The new solution introduces a DevOps-like process to standardize machine learning workflows and accelerate AI deployments from months to days. Only operational machine learning models deliver business value," said Kumar Sreekanti, SVP and CTO, Hybrid IT at HPE. "And with HPE ML Ops, we provide the only enterprise-class solution to operationalize the end-to-end machine learning lifecycle for on-premises and hybrid cloud deployments. The new HPE ML Ops solution extends the capabilities of the BlueData EPIC container software platform, providing data science teams with on-demand access to containerized environments for distributed AI / ML and analytics. BlueData was acquired by HPE in November 2018 to bolster its AI, analytics, and container offerings, and complements HPE's Hybrid IT solutions and HPE Pointnext Services for enterprise AI deployments.

IMAGE: Yoshua Bengio, Co-recipient of the ACM A.M. Turing Award, will present his Turing Lecture at the Heidelberg Laureate Forum on September 23, 2019. ACM, the Association for Computing Machinery, today announced that Yoshua Bengio, co-recipient of the 2018 ACM A.M. Turing Award, will present his Turing Award Lecture, "Deep Learning for AI," at the Heidelberg Laureate Forum on September 23 in Heidelberg, Germany. Bengio is a professor at the University of Montreal and Scientific Director at Mila, Quebec's Artificial Intelligence Institute. He received the 2018 ACM A.M. Turing Award with Geoffrey Hinton, VP and Engineering Fellow of Google, and Yann LeCun, VP and Chief AI Scientist at Facebook. Bengio, Hinton and LeCun were recognized for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

Interest in data science has risen remarkably in the last five years. And while there are many programming languages suited for data science and machine learning, Python is the most popular. Scikit-Learn is a Python module for machine learning built on top of SciPy and NumPy. David Cournapeau started it as a Google Summer of Code project. Since then, it's grown to over 20,000 commits and more than 90 releases.

As the driving force behind machine learning and Artificial Intelligence (AI), data science can be found at the heart of some of today's most exciting technological innovations--from self-driving cars to surgical robots and beyond. So it should come as no surprise that the best and most exciting careers of today and tomorrow belong to those who know how to work with large sets of data in a variety of environments. The Machine Learning & Data Science Certification Training Bundle will help you get certified in this increasingly important field, and it's currently available for over 95% off at just $25. With eight courses and 48 hours of in-depth content, this training will introduce you to both the fundamentals of data science along with its more advanced platforms and methodologies. After an introduction to the basics, you'll learn how to implement important deep learning frameworks using Python, integrate TensorFlow into your projects, gain valuable insights from complex sets of data, use R in order to build powerful networking systems, and much more.

Can Japan compete in the global battle for dominance in artificial intelligence and robotics that is under way? A long-standing strength in AI research gives the United States an advantage that is reinforced by the deep bench of AI talent at its numerous universities and tech giants like Apple, Amazon, Facebook, Google and Microsoft. China's government incentives and growing leadership in the mobile economy has led to a data advantage -- its e-commerce giants like Tencent, Alibaba, Baidu and DiDi have an unparalleled view into the minutiae of everyday economic activities across hundreds of millions of consumers, data that feeds into increasingly sophisticated deep learning systems that power AI-native applications ranging from news filtering to medical diagnostics. Japan does not have to be left behind as the U.S. and China race ahead of the rest of the world. But building dominance in this new generation of technologies will require change and planning.

A deepfake video presents a realistic AI-generated image of a real person saying or doing fictional things. Perhaps the most famous such video to date portrayed Barack Obama calling Donald Trump a "dipshit." Facebook is creating a "Deepfake Detection Challenge," which will offer grants and awards in excess of $10 million to people developing promising detection tools. The social network is teaming up with Microsoft and the Partnership on AI (which includes Amazon, Google, DeepMind, and IBM), as well as academics from MIT, Oxford, Cornell Tech, UC Berkeley, and others on the effort. The tech companies will contribute cash and technology and will help with judging detection tools, a Facebook spokesperson told me.

As artificial intelligence (AI) is becoming mature, every industry wants to adopt it. Enterprises want to use it to unlock the hidden insight from data and use that to make strategic choices for companies. Many enterprises are continuously evaluating different use cases and experimenting with data using different AI frameworks. Having an infrastructure that can support different machine learning and deep learning (MLDL) frameworks is one of the challenges for enterprises in experimenting with AI. In many cases, it is helpful to be closer to data where you want to perform AI.

Hewlett Packard Enterprise (HPE) has announced a container-based software solution - HPE ML Ops, to support the entire machine learning model lifecycle for on-premises, public cloud and hybrid cloud environments. The new solution introduces a DevOps-like process to standardize machine learning workflows and accelerate AI deployments from months to days. The new HPE ML Ops solution extends the capabilities of the BlueData EPIC container software platform, providing data science teams with on-demand access to containerized environments for distributed AI / ML and analytics. BlueData was acquired by HPE in November 2018 to bolster its AI, analytics, and container offerings, and complements HPE's Hybrid IT solutions and HPE Pointnext Services for enterprise AI deployments. HPE ML Ops transforms AI initiatives from experimentation and pilot projects to enterprise-grade operations and production by addressing the entire machine learning lifecycle from data preparation and model building, to training, deployment, monitoring, and collaboration.

Also: For data pros only - An SQL Query walks into a bar and sees two tables; Deep Learning for NLP: Creating a Chatbot with Keras!; 12 NLP Researchers, Practitioners & Innovators You Should Be Following; Wanting to be even more marketable as a data scientist?

Before we start this article on machine learning basics, let us take an example to understand the impact of machine learning in the world. We can safely assume that machine learning has been a dominant force in today's world and has accelerated our progress in all fields. No matter which industry you look at, machine learning has dramatically altered it. Let's take an example from the world of trading. Man Group's AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less. Machine learning has become a hot topic today, with professionals all over the world signing up for ML or AI courses for fear of being left behind. But exactly what is machine learning? It will be clear to you when you have reached the end of this article. Machine Learning, as the name suggests, provides machines with the ability to learn autonomously based on experiences, observations and analysing patterns within a given data set without explicitly programming. When we write a program or a code for some specific purpose, we are actually writing a definite set of instructions which the machine will follow. Whereas in machine learning, we input a data set through which the machine will learn by identifying and analysing the patterns in the data set and learn to take decisions autonomously based on its observations and learnings from the dataset.