It is often said that in this business environment, every organization has to be data-driven. This translates to the fact that most organizations have to make use of data to take business decisions across the enterprise, and this is not just confined to CxO only. Data is the core of digital transformation, and is pivotal in terms of improving a user's experience. We also hear about how data, which is being considered as the'new oil,' is fueling the economic growth of 21st century. In fact, there are many Billion dollar valuation companies that have been built on Data Foundation.

Machine learning on images is revolutionizing healthcare, manufacturing, retail, and many other sectors. Many previously difficult problems can now be solved by training machine learning (ML) models to identify objects in images. Our aim in this book is to provide intuitive explanations of the ML architectures that underpin this fast-advancing field, and to provide practical code to employ these ML models to solve problems involving classification, measurement, detection, segmentation, representation, generation, counting, and more. Image classification is the "hello world" of deep learning. Therefore, this book also provides a practical end-to-end introduction to deep learning. It can serve as a stepping stone to other deep learning domains, such as natural language processing.

Responsible AI is becoming critical as AI is widely used in our everyday lives. Many companies that deploy AI publicly state that when training a model, we not only need to improve its accuracy, but also need to guarantee that the model does not discriminate against users (fairness), is resilient to noisy or poisoned data (robustness), is explainable, and more. In addition, these objectives are not only relevant to model training, but to all steps of end-to-end machine learning, which include data collection, data cleaning and validation, model training, model evaluation, and model management and serving. Finally, responsible AI is conceptually challenging, and supporting all the objectives must be as easy as possible. We thus propose three key research directions towards this vision - depth, breadth, and usability - to measure progress and introduce our ongoing research. First, responsible AI must be deeply supported where multiple objectives like fairness and robust must be handled together. To this end, we propose FR-Train, a holistic framework for fair and robust model training in the presence of data bias and poisoning. Second, responsible AI must be broadly supported, preferably in all steps of machine learning. Currently we focus on the data pre-processing steps and propose Slice Tuner, a selective data acquisition framework for training fair and accurate models, and MLClean, a data cleaning framework that also improves fairness and robustness. Finally, responsible AI must be usable where the techniques must be easy to deploy and actionable. We propose FairBatch, a batch selection approach for fairness that is effective and simple to use, and Slice Finder, a model evaluation tool that automatically finds problematic slices. We believe we scratched the surface of responsible AI for end-to-end machine learning and suggest research challenges moving forward.

This is the third and final part of a three-part series. I suggest you read parts 1 and 2 first for better understanding. In the first part of the series, we got introduced to danfo.js, a new JavaScript package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. The second part dealt extensively with data pre-processing for model building, training, and evaluation with TensorFlow.js and danfo.js in an Observable notebook. In Pythonic data science end-to-end projects, notebooks are converted into scripts during deployment or package building.

This is the second part of a three-part series. In the first part of the series, we were introduced to danfo.js, a new JavaScript package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data easier and more intuitive. In part 1, we analyzed our data to spot trends that can be very useful for feature engineering, model building, and interpretation. If we check the dataset, we can see that the features have all been converted into numerical features, and there's little or no need for feature engineering without a base model. Therefore, we can start with the normalization of the dataset.

This video is part of an online course, End-to-End Machine Learning with Tensorflow from Google Cloud. About this course: In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned.

Video is a natural way for us to understand three dimensional and time varying information. It's how we navigate the world. Converting images to video is a great way to level up your data science results. It helps you to communicate more clearly and it gives you eye-catching GIFs in the process. You can go the other direction too and convert video to a sequence of images.

WIRE)--SnapLogic, provider of the #1 intelligent integration platform, today announced SnapLogic Data Science, a new self-service solution to accelerate the development and deployment of machine learning with minimal coding. Through SnapLogic's drag-and-drop interface, data engineers, data scientists, and IT/DevOps teams can use SnapLogic Data Science to manage and control the entire machine learning lifecycle – including data acquisition, data exploration and preparation, model training and validation, and model deployment – all from within the SnapLogic integration platform. SnapLogic Data Science breaks down traditional barriers that can undermine machine learning initiatives by providing a common platform for machine learning visibility and collaboration across teams including data engineering, data science, IT, DevOps, and development. According to our recent research with Vanson Bourne, 68% of IT decision-makers consider artificial intelligence and machine learning as vital to accelerating their transformation projects. At the same time, McKinsey Global Institute predicts that the U.S. alone will be short 250,000 data scientists by 2024.

WIRE)--Nov 14, 2018--SnapLogic, provider of the #1 intelligent integration platform, today announced SnapLogic Data Science, a new self-service solution to accelerate the development and deployment of machine learning with minimal coding. Through SnapLogic's drag-and-drop interface, data engineers, data scientists, and IT/DevOps teams can use SnapLogic Data Science to manage and control the entire machine learning lifecycle – including data acquisition, data exploration and preparation, model training and validation, and model deployment – all from within the SnapLogic integration platform. SnapLogic Data Science breaks down traditional barriers that can undermine machine learning initiatives by providing a common platform for machine learning visibility and collaboration across teams including data engineering, data science, IT, DevOps, and development. According to our recent research with Vanson Bourne, 68% of IT decision-makers consider artificial intelligence and machine learning as vital to accelerating their transformation projects. At the same time, McKinsey Global Institute predicts that the U.S. alone will be short 250,000 data scientists by 2024.

Lately, we've been talking a lot about containerization and how Kubernetes and MapR can pair up to enhance the productivity of your data science teams and decrease the time to insights. In this multi-part blog series, I will start with a high-level overview of why Kubernetes and containerization are appealing for data science environments. In a later iteration, I will provide an example of a framework that enables Kubernetized data science on your MapR cluster. Earlier this year, we released the MapR Volume Driver for Kubernetes, which enabled MapR customers to use Kubernetes clusters as extensions of their MapR computing space. This volume plugin provides the ability to mount directories from the MapR global namespace easily to Kubernetes pods, enabling stateful applications to run using your data in place.