Google introduces the Professional Machine Learning Engineer certification to allow data scientists to differentiate themselves from the rest. Professionals can take the two-hour-long multiple choice and multiple select exam online or onsite-proctored exam to get certified. Over the years, organizations struggle to hire the right data scientists who can work on end-to-end on data-driven projects to generate business value. According to the IT Skills and Salary 2019 report, 52% of IT decision-makers are in need of professionals who can meet their organizational goals and close the skills gap.
Due to the current hype around artificial intelligence, many professionals pursue data science through various online courses. And since data science courses are devised to quickly complete in a few months, learners acquire similar knowledge. This, however, solves only a part of the talent gap problem in the ever-changing field. Data science is not limited to a few libraries and building predictive models that are being taught in online courses. Instead, data science is about framing ML problems with critical thinking, creating effective ML pipelines, optimizing solutions, managing infrastructure, monitoring models’ performance, and more.
Organizations do not need data scientists who can only write certain ML algorithms; today, recruiters focus on a wide range of skills in applicants, which can help companies in developing AI-based products to generate revenue.
Consequently, there was a need for a methodology to differentiate the best among the rest. Google Professional Machine Learning certification mitigates the challenge by evaluating professionals on a wide range of skills like statistics, ML models, preparing data processes, GCP, framing ML problems, and more.
Although there are no prerequisites to take this exam, Google recommends 3+ years of industry experience, including 1+ years of designing and managing solutions using GCP. To let you prepare, Google is also conducting a webinar and has created a learning path that includes courses to ensure you are well versed before attempting the exam that costs $200.
CAST AI, a multi-cloud company that helps developers deploy, manage, and cost-optimize applications in multiple clouds simultaneously, today announced its multi-cloud platform launch and closing of its $7.7M Series Seed round.
“We built CAST AI with a simple idea in mind – developers need tools to take advantage of everything that AWS, Azure, and other public cloud vendors have to offer. Until now, developers had to pick their preferred cloud partner. With CAST AI, our clients can deploy their infrastructure across all public cloud providers simultaneously,” said Yuri Frayman, Co-founder and CEO of CAST AI.
CAST AI enables businesses to deploy an application in a unified infrastructure that spans multiple public clouds. “We connected the clouds via a secure network mesh, allowing any cloud services of one cloud provider to be available on any other cloud. With autoscaling, we adjust compute resources in real-time, and, with the power of Kubernetes, we add workload replicas when the application needs it,” said Laurent Gil, Co-founder, and CPO of CAST AI. “This is our breakthrough, one application that spans many clouds on an infrastructure that is constantly resized to fit the application needs,” continued Laurent Gil.
The Covid-19 pandemic has massively accelerated cloud adoption. But what do cloud users find once they start scaling? Ballooning bills, growing complexity, and vendor lock-in. This means thousands of wasted DevOps hours and skyrocketing company budgets.
To combat these problems, many companies are now diversifying their infrastructure to multi-cloud. According to Gartner, more than 75% of mid-sized and large businesses will adopt a multi-cloud or hybrid cloud approach by 2021. Using multiple cloud services solves some problems, like vendor lock-in and cost optimization. But up to now, migrating to multi-cloud and managing such complicated infrastructure has been challenging and expensive.
“We went through all these struggles ourselves while trying to solve problems like stopping cloud bills from growing and saving the time of our teams. But it was only getting more complex and challenging. That was enough!” said Leon Kuperman, Co-founder, and CTO of CAST AI.
“We decided to solve this problem and finally let developers easily move their cloud infrastructures across all major providers and take advantage of everything that AWS, Azure, and Google Cloud have to offer. No more vendor lock-in, no more cloud waste. Giving the power back to developers is our mission, and we’re proud to share our platform with the world,” continued Leon Kuperman.
CAST AI recently raised $7.7 million from VC and angel investors for its Seed round, including a contribution from TA Ventures. “I believe that multi-cloud is the next big thing in the tech world. That’s why we invested in CAST AI. We support their mission of democratizing the cloud market and helping all developers to avoid vendor lock-in,” said Oleg Malenkov, Partner at TA Ventures.
CAST AI in its beta version attracted over 30 business clients. Launching out of stealth, the firm intends to use its Seed funding to expand sales efforts and continue investing in product development.
“As enterprises embrace digital transformation and move applications to the cloud, the centralized management of multi-cloud environments becomes critical,” said Alan Dumas, CEO of Red River, a partner of CAST AI. “CAST AI has the potential to be a game-changer for our customers by giving negotiating power back to cloud users, helping them avoid vendor lock-ins, reduce costs, and benefit from provider diversity.”
“We launched CAST AI because we believe that managing a multi-cloud setup should be as easy as buying anything online. With the backing of our investors and early clients, we are here to make the cloud a better place,” said Yuri Frayman.
Image processing in Python is comparatively easier than any other programming language because of numerous available libraries in the market. Python libraries for image processing simplify the process as anyone can import and run a few lines of code to quickly mould based on the requirements. Today, a colossal amount of data is generated due to the rapid increase in smartphones and CCTV cameras. The abundance of image data has let to many companies building data-driven products to streamline business processes. Consequently, being proficient with image processing libraries can differentiate you in the market.
Here are the top 9 image processing libraries in Python: –
1. OpenCV
OpenCV is arguably the best image processing library in the world due to its wide range of use cases in computer vision. Written in C++ and C programming, OpenCV delivers the necessary speed for real-time computer vision. Originally developed by Intel and later supported by Willow Garage and Itseez, the library has been helping machine learning practitioners since 2000.
On GitHub, the library has over 49k stars and 40.5k forks, implying the popularity among developers. Such demand for the library has also lead the project managers to devise a course to help learners master the library. Although the library is free and comes under the Apache 2 License, the course comes at a reasonable cost. However, you can also learn from several tutorials and documentation to quickly get started with the library.
Scikit-image is another widely used Python library for almost every image processing workflow. It is a collection of numerous algorithms for tasks like feature detection, color space manipulation, segmentation, transformations, and more. Created in 2009, scikit-image has gained traction from the developer community to simplify image processing workflows. On GitHub, it has more than 1.7 fork and 4k stars.
Written in Cython, the library is more Pythonic, thereby, easy to leverage and process images without clutters. For any machine learning enthusiast, learning scikit-Image is a must-know library.
Since scikit-image represents images as NumPy arrays, you should be familiar with the NumPy library. If you are exceptional with NumPy, you can implement several image processing without using other libraries. But, since you do not want to write custom codes for complex image processing, you will have to make use of scikit-image to write your code quickly.
Built on top of Python Image Library (PIL), Pillow is among the top three libraries for image processing. Especially used in batch processing, Pillow is commonly used within organizations. Another advantage of Pillow is that it supports a wide range of file format support, making it a one-stop-shop for all your image processing needs. Created in 2009, Pillow has gained over 1.5k forks, and 7.9k starts on GitHub.
Written in C and Python, you can effortlessly learn essential functions within a few days. The pillow library, in its documentation, has included tutorials to assist learners in getting hold of the library.
Mahotas is an open-source library for computer vision in Python, which handles all the image data types. Similar to scikit-image, Mohotas also represents images as NumPy array structures. With Mohotas image processing library, you can expect speed as it is implemented in C++. With Mohotas, you can use over 100 functions for image processing and computer vision.
Other advantages of the library include the ability to retain the functionality of the code even after iteration. In other words, the old code remains relevant for many years. Nevertheless, some interfaces do depreciate over time. To understand the implementation of the Mahotas, read the preprint paper here.
Although popular for scientific computation, SciPy is also used as image processing with scipy.ndimage submodule. Similar to scikit-image, SciPy works in tandem with NumPy to process images effortlessly. Due to the speed it offers, you can build several moderate-level workflows like feature extraction, face detection, image sharpening, denoising, geometrical transformations and more. But, SciPy cannot provide you with the flexibility to develop complex projects that require intensive workflows.
Matplotlib, along with visualization, can be used for manipulating images. The library uses Pillow library to load images data and can handle float32 and uint8, but is limited to uint8 for PNG files. While working with Matplotlib, you can use plt.imshow() to display the NumPy array representation of images. Matplotlib allows you to apply pseudocolor, display color scale reference, perform interpolation, and more. If you want to do basic image processing, then Matplotlib can come in handy while getting started with image analysis.
Also commonly known as ITK–Insight Segmentation and Registration Toolkit–is a widely used image processing library. ITK is a powerful library to use but is very large and complex. However, you can use their detailed guide to understand the most important features of the library for image processing and segmentation. Built to handle advanced projects, the library keeps evolving with the help of contributors on GitHub, which has 756 stars and 441 forks on the platform.
SimpleCV is a very easy to use computer vision and image processing library, but it is not used for intensive projects. If you are new, you can leverage SimpleCV for computer vision tasks but will have to eventually move towards OpenCV. Although it has 2.4k stars and 769 forks on GitHub, there is no further development in the open-source project. Nevertheless, if you are a beginner, you can start with projects in just a few lines of code.
Only officially supported on macOS and Linux, Pgmagic is another image processing library that is most common among enthusiasts. However, a Windows user can rely on unofficial binary packages to play with images. Pgmagick is a very simple library that works with over 88 image formats for processing basic manipulations like resizing, sharpening, blur filtering, rotation, and more.
This week, we witnessed a few collaborations among top AI organizations and universities to simplify the adoption of AI. While PyTorch, OpenMined, and the University of Oxford partnered to offer free courses on -preserving-privacy in AI, IBM and AMD came together to build frameworks and AI solutions for hybrid cloud. Besides, this week has several announcements like 3D image datasets by Google and self-driving firm, Nuro, raising money in Series C round.
Here are the Top AI News of The Week (November 15, 2020): –
PyTorch Provided $600,000 To OpenMined For 4 Free Courses
OpenMined, along with PyTorch, Facebook AI, University of Oxford and more, will devise four free courses. The idea with the initiative is to bring awareness as well as educate people about preserving-privacy in AI technology. Starting from January 2, 2021, the courses will be released for anyone to enrol for free.
All the courses will be a part of The Private AI Series, which will be based on PyTorch. “New paradigms and skills are spread most effectively through education, so we’re building an entirely new learning platform starting with a series of courses on privacy-preserving machine learning,” notes OpenMined.
IBM And AMD Joined Forces For AI and Confidential Computing
As per the joint press release, the association between AI and confidential computing
IBM and AMD will develop open-source software, open standards and open system architectures to drive confidential computing in hybrid cloud environments. The idea is to enable organizations to deploy critical applications that require high performance computing on hybrid infrastructure.
“The commitment of AMD to technological innovation aligns with our mission to develop and accelerate the adoption of the hybrid cloud to help connect, secure and power our digital world,” said Dario Gil, Director of IBM Research. “IBM is focused on giving our clients choice, agility and security in our hybrid cloud offerings through advanced research, development and scaling of new technologies.”
Vatican City Will Use AI To Protect Its Library
Image source: History
Apostolic Vatican Library of Vatican City has been digitizing its library of more than 80,000 manuscripts, which consists of 40 million images. This has led to attacks on the library by hackers every month. Founded by Pope Nicholas in 1451, the library witness more than 100 cyberattacks every month. Consequently, there is a need for a robust AI-based system to protect in order to avoid any malicious practices by hackers.
“In the era of fake news, these collections play an important role in the fight against misinformation and so defending them against ‘trust attacks’ is critical,” said Manlio Miceli, chief information officer of the library, to The Guardian.
Nuro Raised $500M In Series C Round
Led by T. Rowe Price Associates, and participation from new investors–Fidelity Management & Research Company and Baillie Gifford–and existing investors, Nuro raised $500M in Series C round. Founded in 2016, the company has over 500 people who blaze the trail to make inroads into accomplishing self-driving cars. Currently, Nuro has developed two lightweight autonomous delivery vehicles. Its second-generation vehicle R2 was the only fully-autonomous (without safety driver) vehicle that drove on public roads in California, Texas, and Arizona.
Google Announced Objectron Dataset To Better Understand 3D Objects
Google released Objectron dataset, a collection of short video clips of common objects from different angles to further improve computer vision capabilities. With over 15k annotated video clips and 4M annotated images collected from a geo-diverse sample, the dataset will serve as the foundations for further research. “Each video clip is accompanied by AR session metadata that includes camera poses and sparse point-clouds. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions,” noted the author.
OpenMined, an open-source community that ensures privacy-preserving artificial intelligence workflows, joined forces with PyTorch and others to make four free courses. PyTorch provided $600,000 to OpenMined to create and deliver free courses starting from January 2, 2021. The new courses by OpenMined will be under the banner The Private AI Series, which will be based on PyTorch.
“New paradigms and skills are spread most effectively through education, so we’re building an entirely new learning platform starting with a series of courses on privacy-preserving machine learning,” notes OpenMined.
The Private AI Series
The four courses — Privacy and Society, Foundations of Private Computation, Federated Learning Across Enterprises, Federated Learning on Mobile — will be of more than 146 hours, giving you a complete understanding of privacy and security while working with sensitive data.
Since the course will be created in association with Facebook AI, University of Oxford, PyTorch, Future of Humanity Institue (university of Oxford), BigData (UN Global Working Group), and OpenMined, the course will be delivered by experts, including guest experts from MIT, Harvard, and more.
Some of the core features of courses include real-world projects, technical mentorship, and more. The Private AI Series by OpenMined will not only offer free learning but also prepare you for upcoming private data analysis certification by the United Nations Global Working Group (GWG) on Big Data.
Privacy and bias have become the most significant barrier to the proliferation of artificial intelligence. Addressing one of the issues — privacy — OpenMined is working toward pushing the technology’s development for the greater good. Federated learning, a technique to train models without sharing the data, is a groundbreaking process to preserve privacy. This course highly focuses on the privacy technique to teach both beginners and practitioners with its upcoming four free courses.
You can sign up for the course today and start when it is available early next year.
The adoption of artificial Intelligence-based conversational systems is at an all-time high to maintain business continuity during the pandemic. According to Statista, the chatbots market is going to increase and reach 454.8 million in revenue by 2027, up from $40.9 million in 2018. Such a trend has also opened up opportunities for hackers to attack chatbots and disrupt customer services. The attacks, however, are not limited to dated practices; today, hackers are leveraging cost-efficient machine learning techniques to attack conversational AI systems at scale. One of the biggest challenges of AI attacks is the sophistication and ability to adjust behavior based on a systems’ defences. To address these problems, Scanta Inc, an AI-based cybersecurity firm, is assisting businesses in protecting virtual assistant chatbots against machine learning attacks.
Scanta provides security for conversational systems like Chatbots, robo-advisors, and virtual assistants against adversarial attacks. “By using AI against AI attacks, we hope to lessen the burden on security professionals. We provide a SaaS solution for integration, which acts as a proxy to provide inline defence,” says Anil Kaushik, CTO of Scanta. The company carries out significant research on machine learning attacks and collects several signals to identify the sophistication of malicious activities. By gathering metadata, Scanta is able to devise a solution to identify cyberattacks of AI systems that learn chatbots’ behavior and bypass the threshold to stay undetected.
Powered by machine learning algorithms, Scanta’s solution–VA Shield–builds deep models capturing features for all inputs and outputs in conversational systems to build unique unbreachable attributes in real-time. VA Shield also builds models for chatbots in addition to user inputs so it can be used to isolate any malicious insider activities prevalent in chatbots. The multiple layers in VA Shield allow it to build such unique DNA that protects conversational systems from within. To make a resilient solution, the multiple layers include capturing device information used for interaction, network characteristics, end-user input, and system output. “Both the input and output are extremely critical aspects of protecting conversational AI. VA Shield performs deep inspection of each conversation in systems to defend inputs and outputs efficiently,” explains Kaushik. The solution is specifically designed to collect multiple attributes and analyze the differences in the conversations. Identification of these differences enables VA Shield to isolate any malicious activities in the system.
Equipped with state-of-the-art machine learning techniques, Scanta is working closely with organizations that either offer or have integrated chatbots for providing a superior customer experience. Since this is the first foray for most companies in monitoring conversational systems at scale, Scanta simplifies the onboarding process for its clients by helping them implement VA Shield effectively. As one of the first companies to focus on cybersecurity for conversational platforms, Scanta offers enterprises the opportunity to protect conversational AI systems in ways that were not possible before. By having an AI-based system to uncover and block these attacks, Scanta hopes to automate the tasks of security professionals, leaving them more time to focus on higher-order security issues.
Scanta, being a pioneer in AI-based cybersecurity solutions for conversational systems, credits its team which is behind the ability of its solutions. To hire the right team, Scanta uses a referral system to maximize access to proficient cybersecurity talent in the market; but being independent of location helps Scanta source talent over a wide geographic footprint. “We have current executives with expertise in cybersecurity giving us a large pool to recruit from,” says Chaitanya Hiremath, CEO of Scanta. “We also provide an opportunity for engineers to work in the cutting-edge field of AI security and so we believe we are in a good position to build an industry-leading team to drive product innovation.”
In the future, Scanta is committed to protecting conversational systems like chatbots, robo-advisors, virtual assistants, social media, group chats, virtual agents, and email from bad actors.
Walmart collaborates with Cruise, a self-driving car provider, to deliver your orders from early 2021. The pilot program will allow Walmart to offer contactless delivery of orders to its customers to reduce the transfer of coronavirus. The association between the world’s largest retailer and Cruise is also a move toward Walmart’s zero-emission by 2040 to protect the planet from harmful pollutants.
Since the pandemic, Walmart has double down on autonomous delivery of grocery and health and wellness products through drones in the US. While a pilot program was launched in Fayetteville, North Carolina, on September 9, Walmart deployed more drones to deliver COVID-19 self-collection kits on September 22.
In late 2020, autonomous driving vehicle providers, especially self-driving car providers like Waymo, Tesla, Cruise, and others, have gained momentum due to the approval of offering robotaxi service without safety drivers. After years of delay in deploying self-driving cars, various companies are eventually being able to deliver on the promise of revamping the transport industry with complete autonomy.
However, for delivering your Walmart orders, Cruise will have a safety driver in place during its pilot phase. On November 14, 2018, Walmart also had a similar deal with Ford for the autonomous delivery of groceries in Miami. And in 2019, the retailer joined forces with Nuro to grocery delivery in Texas.
Several companies, including food delivery firms like DoorDash and Postmates, have adopted self-driving cars, but Walmart has been at the forefront of trying to revamp the delivery service for superior customer experience. “You’ve seen us test drive with self-driving cars in the past, and we’re continuing to learn a lot about how they can shape the future of retail. We’re excited to add Cruise to our lineup of autonomous vehicle pilots as we continue to chart a whole new roadmap for retail,” notes Tom Ward, SVP of Customer Product, Walmart US, in a press release.
IBM’s Digital Developer Conference Data & AI is going to be held on November 10 for Americas & Europe and November 24 for India & Asia Pacific, where you can get a free specialization or professional certification by completing a data science course on Coursera. The four-track free conference by IBM is focused on AI in production, Data & AI Essentials Course, 5 Hands-on labs, and Data Competitions & Open Source.
IBM Data & AI Conference
Digital Developer Conference Data & AI is ideal for both machine learning beginners and practitioners to learn from experts on a wide range of data science topics. Some of the most interesting sessions would be the deployment of models in production and overcoming challenges associated with productizing. The conference will also have sessions on best practices while developing machine learning models, covering design patterns used by developers, fairness in AI, bias detection and mitigation, building AutoAI pipeline for cyber threat detection, and more.
Unlike other conferences, what makes the IBM Digital Developer Conference Data & AI a must-attend is that the event does not binge you with a plethora of information. The sessions are very industry-relevant with topics like the future of open-source, optimizing models for accuracy, among others. Besides, the conference will host a data competition–Call for Code Spot Challenge on Wildfires. Further, IBM will also release a new geospatial dataset from the IBM Weather Operations Center, going back to 2005, for machine learning enthusiasts to blaze their trails.
You might have heard of robots replacing humans, but this week it was the other way around as Walmart removed robots to replace with humans. Nevertheless, other machine learning-based systems are penetrating into human workflows and setting them free from repetitive or non-creative tasks like driving cars, extracting values from documents; while Goole launched DocAI platform to process unstructured data, AutoX will expand its testing of autonomous vehicles to four more cities. This week in AI news has more exciting announcements and developments–read more.
Here are the top AI news of the week: –
Google Introduces Document AI Platform
Google introduced a DocAI platform to automatically extract information from documents. Unstructured data have valuable information, but organizations fail to streamline the process of gaining value from it, as processing unstructured data is a strenuous task. Addressing this challenge, Google is offering DocAI platform through GCP to quickly collect information like address, name, date, and more. To collect data, you can either use default parsers or make a customized template. You can currently use generalized parser like OCR, form parser, and document splitter but will have to request access to leverage specialized parsers.
Walmart Replaces Robots With Humans
At the time of termination of the contract with Bossa Nova Robotics, Walmart had 500 robots in 4,700 stores to check stocks of products on shelves. The robots used to wander in stores and notified in case of probable out-of-stock circumstances. However, Walmart believes that humans can be more efficient than robots in scanning shelves while being cost-effective. In a recent interview with Squawk Box, Doug McMillion, CEO of Walmart, noted that one of the challenging tasks is to ensure products are in-stock.
Intel Acquires
Intel acquired cnvrg.io, an end-to-end machine learning platform, to help data scientists deploy models at scale. cnvrg.io is an Israel-based company that assists organizations in quickly bringing AI-based products to the market. As per a spokesperson of Intel, cnvrg.io will remain an Intel independent firm and continue to serve its clients. Intel, with cnvrg.io, will now compete with the likes of DataRobot, Databricks, Dataiku, and others, with an end-to-end data science platform. cmvrg.io is now Intel’s second accusation in two weeks after SigOpt.
Self-Driving Cars In AutoX
AutoX tested its autonomous vehicle in California in July without a safety driver, becoming the second firm after Waymo. Backed by Alibaba Group Holding Ltd, the company is about to test its vehicle in four more cities. The company is already offering robotaxi service in Shanghai and is about to test level 4 autonomous vehicles in China. This is a year of self-driving cars as numerous companies like Cruise, Waymo, and Tesla, have received the approval for offering raid-hailing with autonomous vehicles.
AWS Announces The Expansion Of Its Cloud Service In India
AWS will add another AWS Region in Hyderabad, Telangana, by 2022. This will be the second region in India after Mumbai, which was opened on June 27, 2016. Currently, AWS has 24 regions across the globe to ensure the speed and reliability of its services. The Hyderabad region will have 3 Availability Zone (AZ), making it a total of 6 AZ in India. AWS has plans to add another 15 AZ across India, Indonesia, Japan, Spain, and Switzerland. Google is also working on commissioning its second cloud region in India, which will be launched next year.
AWS announced the expansion of its cloud region in India as a part of its Global Infrastructure initiative. The new AWS data center will be commissioned in Hyderabad, Telangana, the second AWS Region in India after its infrastructure region in Mumbai, which was opened on June 27, 2016, and later expanded in 2019 with a third Availability Zone (AZ). The AWS Hyderabad region will allow organizations and developers to leverage cloud computing with low latency to make superior products.
The second region will consist of 3 zones, joining the other three regions in Mumbai. AWS has a total of 24 infrastructure regions across the globe and has a plan of 15 more AZ in India, Indonesia, Japan, Sapin, and Switzerland.
“Businesses in India are embracing cloud computing to reduce costs, increase agility, and enable rapid innovation to meet the needs of billions of customers in India and abroad. Together with our AWS Asia Pacific (Mumbai) Region, we’re providing customers with more flexibility and choice, while allowing them to architect their infrastructure for even greater fault tolerance, resiliency, and availability across geographic locations,” said Peter DeSantis, Senior Vice President of Global Infrastructure and Customer Support, Amazon Web Services.
In a continuous attempt to deliver the best cloud computing platform to businesses, AWS has also expanded its service through edge locations in Bengaluru, Chennai, Hyderabad, Mumbai, New Delhi, and Kolkata. These edge locations are used for cache copies to reduce the delay in data delivery.
Prominent startups and big tech companies like Aditya Birla Capital, Axis Bank, Mahindra Electric, Ola, OYO, Swiggy, Tata Sky, Zerodha, and more use AWS for performance, availability, security, scalability, and flexibility of their products and services.
Earlier this year in July, Google Cloud Platform had also announced the launch of its second cloud region in 2021, after its first launch in Mumbai in 2017.
