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Most Learning Is Slow In The Field Of Machine Learning

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The first day of The Rising 2020 started with an informal session with Sara Hooker, a researcher at Google Brain where she shared some of her personal reflections on how to navigate in the field of machine learning and why we need to celebrate failures as well as success. Sara started her session with a simple story where she shared her childhood dream of being featured in the magazine, The Economist. In fact, she mentioned that "one of my goals was to eventually be an economist." However, when that happened in 2016, it wasn't a pleasing feeling for Sara; instead, it was a feeling of "unease" and seemed problematic. A lot of this could be attributed to the article that The Economist did, which profiled the efforts of fast.ai, a course that's run by Jeremy Howard and Rachel Thomas, and utilised Sara as an example of their success.


The 5 Components Towards Building Production-Ready Machine Learning System

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The biggest issue facing machine learning is how to put the system into production. To conceptualize this framework, there is a significant paper from Google called ML Test Score -- A Rubric for Production Readiness and Technical Debt Reduction -- which is an exhaustive framework/checklist from practitioners at Google. It is a follow-up to previous work from Google, such as (1) Hidden Technical Debt in ML Systems, (2) ML: The High-Interest Credit Card of Technical Debt, and (3) Rules of ML: Best Practices for ML Engineering. As seen in Figure 1 from the paper above, ML system testing is more complex a challenge than testing manually coded systems, since ML system behavior depends strongly on data and models that cannot be sharply specified a priori. One way to see this is to consider ML training as analogous to the compilation, where the source is both code and training data.


Natural Language Processing (NLP) with Python: 2020

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BESTSELLER Created by Ankit Mistry, Vijay Gadhave, Data Science & Machine Learning Academy English English [Auto] PREVIEW THIS COURSE - GET COUPON CODE Description Recent reviews: "Very practical and interesting, Loved the course material, organization and presentation. Thank you so much" "This is the best course to learn NLP from the basic. According to statista dot com which field of AI is predicted to reach $43 billion by 2025? If answer is'Natural Language Processing', You are at right place. How Android speech recognition recognize your voice with such high accuracy.


📈📉 Check Top 10 Digital Marketing Trends 2021 With Infographic

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Staying across digital marketing trends 2021 can shape the strategic positioning and communications of your business. Customers engaging with your brand on social media, via apps, and through your website can have their experience of your organisation enriched with the integration of these key 21st century marketing trends. Use the list below to get a rounded view of these trends, understand how these advancements are shaping the industry, and start reworking your marketing strategy so that you can move your business forward in the months ahead. Remember: "Social media users are now spending an average of 2 hours and 24 minutes per day multinetworking across an average of 8 social networks and messaging apps." There's a reason why digital marketing is essential -- your customers are most likely online in some capacity for a good proportion of their day.


Artificial intelligence: Enhancing the use of mobile apps in India

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Artificial Intelligence has transformed the face of the technological world. It is about to enhance everything from a toothbrush, television to the car and other vehicles, where mobile apps are no such exceptions. Earlier, the smartphone models were internet-dependent or cloud-based, but now AI has completely altered its features, thus enhancing it's quality and performance. Smartphones have become an integral and vital part of our lives, and AI is indeed taking the market by storm. With the emergence of AI and its growing inception, smartphone applications have come to the forefront.


Apple Data Science Interview Questions

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Apple Inc. is one of the biggest technology companies in the world that designs, develops, and sells consumer electronics, computer software, and online services. Apple is constantly in need of creative, passionate, and dedicated data scientists that can sit on any number of their teams. From its researched-based artificial intelligence development team at Siri to cloud-base architecture development team at iCloud, Apple has slowly but steadily been building data science teams to handle the avalanche of data accumulated on a daily basis. As with other big tech companies, the role of a data scientist at Apple varies a lot and is dependent on the teams you are assigned to. This means the job will require everything from analytics to machine learning software design to plain engineering.


Voice + AI Is Coming To The Workplace Loud And Clear

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Virtual assistants turn 16 this year and you don't have to look too hard – or speak too loudly – to find them. In fact, there will be around 8 billion voice-based devices by 2023 – more than the world's population today. From Amazon's Echo and Google's Assistant to Apple's Siri, Samsung's Bixby and Microsoft's Cortana, billions of people around the world are using their voices every day to schedule appointments, get directions, play music or get answers quickly-- all things that once required us to tediously type or write. Even Twitter recently announced that users can now audio tweet their inner musings. And yet, despite widespread adoption of voice-based devices in our personal lives, applications based on voice are nowhere as pervasive in our professional lives as they are in our homes.


Smart & Final Deploys Hypersonix's AI-Driven Analytics

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Smart & Final is rolling out Hypersonix's AI-driven analytics platform to support the company's enterprise analytics and digital transformation initiatives. The two companies started working together sixty days ago on a successful pilot program. With this announcement, Smart & Final officially joins a handful of early adopters in the grocery and consumer-commerce industries turning to the innovative company to help navigate the post-COVID-19 market. "Hypersonix is a key ingredient in leveraging actionable analytics that can be operationalized by our business teams as part of our on-going digital transformation," said Ed Wong, EVP and Chief Digital Officer at Smart & Final. "We established a great innovation-centric collaboration with Hypersonix where we are finding new ways to address our needs in key strategic areas for our business."


Enabling the Return To Work initiative using SAP Conversational AI & Qualtrics

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As many parts of the world continue to remain in lockdown due to the global pandemic, many countries have started to ease the restrictions. Particularly in Australia & New Zealand, schools have reopened, workers are heading back to their offices, and restaurants & retail stores are beginning to resume trade with new set of guidelines. These guidelines might also vary from one state to another and hence businesses that operate and have offices in different states, need to provide relevant updates to their employees to be able to comply with the new regulations. The most common practice from employers is to send out regular emails outlining the guidelines. Chatbots are beginning to play a vital role in providing real-time upto date information.


Deploy Machine Learning Pipeline on AWS Fargate - KDnuggets

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In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve it as a web application using Google Kubernetes Engine. If you haven't heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use the same machine learning pipeline and Flask app that we built and deployed previously. This time we will demonstrate how to containerize and deploy a machine learning pipeline serverless using AWS Fargate. This tutorial will cover the entire workflow starting from building a docker image locally, uploading it onto Amazon Elastic Container Registry, creating a cluster and then defining and executing task using AWS-managed infrastructure i.e.