1 Artificial Intelligence and Machine Learning

techmanga June 27, 2021 0 Comments



AI & Machine Learning


Value: $20 billionArtificial Intelligence is something people are both fascinated about andafraid of. On the one hand, it may make some roles in the economy useless, butat the same time, it should produce more jobs. Many top tech companies in thebay area make use of AI. As part of IoT or mobile applications AI will learnand make UX improving over time. For example, it can be incredibly helpful forpredictions. It can and is often used to detect possible fraud transactions.AI experts are among the most wanted tech specialists on the market. Lack ofthem is probably the only thing that can stop AI tech companies’ development.Founders: Prathamesh Juvatkar, Sarthak JainTotal funding amount: $120KThis is a SaaS product and machine learning API that helps developers inbuilding machine learning models for all kinds of solutions, including web appdevelopment. Software engineers who need to develop a model for identifyingobjects from sets can benefit a lot from Nanonets technology. The tool helpsto increase the adoption of machine learning technology among businesses.Founders: Jonathan Aizen, Paul KnegtenTotal funding amount: $19.5MThis tool uses artificial intelligence and machine learning to automate andoptimize the real estate industry. Among others, the company released anintelligent assistant for real estate transactions called Folio – itautomatically organizes essential transaction details for real estate agents.Founder: Karl MehtaTotal funding amount: $66.2MThis award-winning platform is used by Fortune 500 companies and governmentorganizations all over the world to solve problems around the discovery,curation, and recommendation of content across external, internal, and tacitknowledge sources. The solution includes a Learning Experience Platform (LXP),SalesU sales enablement suite, and GuideMe, a multi-language in-app contentauthoring tool.

Big Data


Value: $56 billionFor many years people have known that there are sets of data that are way toolarge and/or too complex to analyze them the traditional way. We hope thatthere are answers hidden somewhere in our databases. Data engineers struggleto make the most sense of the data we have. However, without big dataprocessing, the answers we seek are unavailable.Take weather forecasting. An equation of variables in which one small changecan completely alter the outcome. Thanks to the big data processing we finallycan count on reliable long term forecasts.Founders: David P. Mariani, James Lai, Matthew Baird, Sarah GerweckTotal funding amount: $95MA giant among the Bay area startups, this data warehouse virtualizationplatform connects Business Intelligence tools to data platforms to enablesmooth data migration without disrupting business users. It also helps toaccelerate business analysis and define business metrics in one place todeliver consistent operational reporting.Founders: Daniel BarberTotal funding amount: $9.2MThe age of privacy demands a new standard of transparency. DataGrail is thefirst dedicated privacy management platform that delivers ongoing compliancewith GDPR, CCPA and more. The privacy platform integrates directly with over150 business systems such as Salesforce, Adobe and Oracle, allowing businessesto discover and display personal data in seconds, rather than weeks or evenmonths.Based in San Francisco, California and founded in 2018 by Daniel Barber,DataGrail’s direct integration enables companies to deploy a privacy requestworkflow in minutes and unify email settings across all customer-facingapplications. Gartner named DataGrail Best Privacy Provider for 2020.Founders: Logan WelleyTotal funding amount: $163.1MFivetran fully automated connectors sync data from cloud applications,databases, event logs, and more to your data warehouse. Their integrations aredesigned for analysts who need centralized data but don’t want to spend timemaintaining their own pipelines or ETL systems. Its motto is “Focus on whatreally matters while driving analytics for your business.”

Drone Technology


Value: $10 billionDrone technology is something more than just Unmanned Aerial Vehicles. Thereare Remotely Operated Underwater Vehicles and Unmanned Ground Vehicles aswell. While the Drone Technology market is valued at about $10B, the globalmarket for services may be worth 12 times more, according to PwC analysis.This is because drones can change almost every industry we know today.Starting from the construction industry, to defense and security, to emergencyresponse and humanitarian aid, to healthcare. What’s more, developing DroneTechnology will bring also anti-drone technologies regarding security andprivacy.Founders: Colin Beighley, Fergus Noble, Timothy HarrisTotal funding amount: $48.8MSwift Navigation started as a GPS company. Today it not only provides low-costand high-accuracy GPS receiver that makes positioning more accurate andaffordable but also offers an ecosystem of positioning solutions that can beapplied to the development of autonomous vehicles. Their solutions can be usedfor aerial drones, automotive vehicles (also fully autonomous) and more.Founders: Keenan Wyrobek, Keller Rinaudo, Will HetzlerTotal funding amount: $233MZipline combines advanced drone technology with Medtech. They secure people inneed with on-demand delivery of vital medical supplies. Their technology workspretty much everywhere in the world complementing or even replacingtraditional means of transport. The company ensures that their end-to-endstorage and handling meets the highest medical standards. Battery-poweredunmanned aerial vehicles are quick and small, therefore they can reachdestinations directly. What’s more, Zipline hires talents locally helpingcommunities grow.Founders: Andrew BranaghTotal funding amount: N/AWhat if you could just use your phone to get something straight to you inminutes by air; faster, safer and with less carbon dioxide than if it weredelivered to the ground? Wing is changing the future of delivery. They are anon-demand drone delivery service that can deliver food, medicine or otheritems within minutes.They have also developed a drone motion control platform for safely movingdrones across the sky. Wing’s services are faster, safer and produce lesspollution than traditional shipping.Originally established in 2012 at Factory X, Moonshot Factory, Wing is now anAlphabet company.They are looking for curious, adventurous people who are highly motivated byunresolved problems. Wing come from different walks of life and share apassion for great ideas.

FoodTech


Value: $14 BillionIn the first half of 2020, the volume of investments in the global FoodTechmarket grew by 15.4% compared to the same period in 2019. These six monthshave shown that food delivery and super application platforms remain the mostattractive in terms of volume for investors. In total, the companies concluded331 deals for $8.6 billion.Founders: N/ATotal funding amount: $1.4BImpossible Foods is transforming the global food system by inventing the bestway to make the meats and cheeses we love without using animals.> “We start by understanding what we love about the amazingly challenging> experience of eating meat and dairy products, and then we explore the plant> world to find and piece together specific proteins and nutrients to recreate> the experience”,their official website says.Their mission is to provide food without compromise – tasty, healthy forpeople and healthy for the planet. The company was conceived and founded byrenowned Stanford University professors with the backing of leading venturecapitalists and visionaries.Founders: Sasan Amini, Mahni GhorashiTotal funding amount: $63.5MAt Clear Labs, they are implementing significantly stricter food safety andquality programs through comprehensive genomic testing and advanced dataresearch. The founders are confident that their product is a future offoodtech.

1. Artificial Intelligence and Machine Learning:


AI and ML are the two most in demand and promising IT skill to learn currentlyfor an all-round development in the IT industry. AI is a concept that makesmachines carry out tasks that human beings are able to perform, but in amanner that is “smart.” Whereas, Machine Learning is a part of AI that helpscomputers to know how to make use of all the available data to make a decisionor perform a task in a more efficient manner. ML finds its application inalmost every industry, including healthcare, education, finance, etc. Theaverage salary a Machine Learning Engineer and an AI Engineer earns is$143,000+ and $100,000 to $150,000 respectively. According to a surveyconducted in 2017, 83% of executives were of the opinion that AI was astrategic priority for their businesses. Since 2013, the number of jobsrequiring AI skills has multiplied by 4.5. Per an estimation, the demand forAI and Machine Learning specialists will witness a 60% increase in demand in2019.You can also learn: Difference Between Artificial Intelligence And MachineLearning

7. Data Engineering:


Data engineering is the key behind the existence of data science. Datascientists are dependent on data engineers as the latter developsinfrastructure and tools which data scientists use to conduct their own work.This skill is more closely related to software engineering than to other dataroles. A data engineer earns $90,839 on an average per year.

7 Cloud Computing


As more and more enterprises switch from traditional physical serverinfrastructures to cloud-based solutions, cloud computing skills are becominghighly marketable. Machine Learning and Artificial Intelligent services arenow hosted by cloud platforms, and job openings between 2017 and 2020 haveincreased by 107% in the US alone.The leading cloud-based service provider is AWS or Amazon Web Services, andgaining skills in this tech will improve your demand as a professional. Beingcertified in AWS will see you earn more than your non-qualified colleagues, ascloud computing skills will earn you an average of $130,272.AWS cloud solution architects have the highest US and Canada techcertifications, while other cloud computing skills include DevOps, Microsoft,Kubernetes, Docker, and Azure. Cloud engineering has long term demand in thetech sector as more IT solutions are being shifted to cloud-based platforms.

6 Artificial Intelligence and Machine Learning


AI or artificial intelligence, and its counterpart ML (or machine learning)are buzzwords synonymous with an increasingly innovative business environment.A significant number of tools and services are today based on ML and AI, andthe hiring rate for artificial intelligence specialists has grown by 74% inthe past couple of years.A 2019 study of the best tech jobs by Indeed found that Machine learningengineers openings had increased by 344% for the past 4 years. These ML and AIspecialists with skills such as python, natural language processing, java,TensorFlow, and R command an average salary of up to $140,000 annually in theUS.Mastering these tech skills will including building Chatbots, one of the mostwanted specializations under ML and AI. Several major organizations arerelying on AI operated customer service interactions for website queries, andbeing a master earns you the highest IT salaries in 2021 and beyond. As an AIspecialist, you’ll benefit from accessing a wide variety of tech jobs for yourportfolio, including data sciences, product management, and softwareengineering.Machine learning covers statistical pattern recognition, neural networks,unsupervised learning, deep learning, recommender systems, and anomalydetection.

3 IoT or Internet of Things and Big Data


Internet of Things (IoT) is a broad term. It covers everything that’sconnected to the internet and, more specifically, devices that communicatewith each other. Defined as objects that talk, IoT (or edge computing)includes smartphones, wearable tech, and other smart sensors.One of the major concerns with connected devices is data security, and that’swhy techies who’ve mastered IoT tech are taught after. The average salary ofan Internet of Things professional is $101,000, with the segment poised tobecome the next technology career boom.By 2025, IoT will impact the economy by up to $11 trillion according topredictions by the McKinsey Global Institute. Currently, 94% of business isinvesting in IoT preparedness initiatives, and mastering this tech requiresyou to identify solutions and network components with security risks and datamanagement for prototype production.

How Tech Giants Are Investing In The Indian AI Ecosystem


Google: Earlier in July, Google launched the Machine Learning Crash Coursewith an objective to train and upskill Indian developers. The Mountain Viewtech giant signed a Statement of Intent with NITI Aayog earlier in July towork towards building the AI ecosystem in India. This flagship course createdby Google engineers provides interactive visualisations, and instructionalvideos that anyone can use to learn and practice ML concepts. The coursecovers a bunch of machine learning fundamentals from basic concepts such asloss function and gradient descent, to advanced theories like classificationmodels and neural networks. There are various programming exercises whichinclude the basics of TensorFlow and also feature videos from Google machinelearning experts.Microsoft: The Microsoft Professional Program helps developers gain job-readyskills in AI and data science. The courses are helmed by Microsoft experts whoimpart training in areas like Python, Data Analysis, Azure Machine Learning,Computer Vision, Ethics and Law in Data and Analytics, Deep Learning Models,Reinforcement Learning Models and learn how to develop Applied AI Solutions.Intel: In an attempt to lower the entry barrier for developers and datascientists in India, the Intel AI Academy has educated over 15,000 scientists,developers, analysts, and engineers in technologies, including deep learningand ML. To this end, Intel India has forged deep industry collaborations withcompanies like Hewlett Packard Enterprise, Wipro, Julia Computing, and CalligoTechnologies, and at the same time acquire companies that can accelerate itsAI solution development capabilities.NVIDIA: NVIDIA has been at the forefront of fostering an AI developerecosystem in India and building a deep learning talent pool in India. In 2017,NVIDIA launched the Developer Connect in 2017, with the aim to provide aknowledge sharing platform for professionals, developers, academicians,students and the public sector working in the field of AI and deep learning.In an earlier interview, Vishal Dhupar, managing director, South Asia atNVIDIA Graphics stated that from a developer engagement standpoint, the NVIDIADeveloper Program is an important part of its commitment to the community. “Weare witnessing a constant rise in the sign-ups to the program, since itsinception. This program enables the delivery of an extensive range of NVIDIAsoftware and technologies to the developer community and two-way communicationabout issues, enhancements, usage and future needs,” he said.MathWorks: Another leading tech giant that has been at the forefront ofbuilding an AI ecosystem in India is MathWorks which is continuously strivingto make MATLAB easy for engineers and scientists to use for deep learning.MATLAB also offers specialised toolboxes for working with machine learning,neural networks, computer vision, and automated driving. With just a few linesof code, MATLAB helps to do deep learning without being an expert, allowingengineers and scientists to get started quickly, create and visualise models,and deploy models to servers and embedded devices.IBM: Tech giant IBM is also engaging with developers in India in areas likeAI, machine learning and IoT. According to news reports, the tech giant willengage with developers through roadshows in major hubs like Bengaluru, Mumbaiand build an ecosystem of developers who can leverage IBM Watson. Industryreports indicate India will have the largest developer ecosystem by 2020 andtech giants will play a pivotal role.Machine Learning Developers Summit 2019: Another attempt at catalyzing the AIand ML developer ecosystem in India, MLDS brought by Analytics India Magazinewill bring data science and machine learning developer community together. The2-day conference that will give attendees direct access top ML innovators fromleading tech companies across the country.

1. Big Data Engineer


Big Data Engineers are responsible for building, testing, and maintainingscalable Big Data ecosystems for the businesses so that the Data Scientistscan run their algorithms on stable and optimized data systems. Big DataEngineers usually work closely with Data Architects, Data Analysts, and DataScientists, all focused on one goal – to help organizations obtain meaningfulinsights from large and complex datasets that can be transformed intoactionable business decisions. Big data is one of the best career optionsafter computer engineering.As the name suggests, Big Data Engineers work primarily with Big Dataecosystems, tools, and technologies. They are required to upgrade,troubleshoot, and optimize Big Data systems and software to improve theefficiency of the databases. Usually, Big Data Engineers need a few years ofindustry experience in working with Big Data frameworks like Hadoop, SQL-baseddatabases, and also with popular data APIs and ETL tools. Click more if youwant to learn more about how to become a big data engineer.

2. Machine Learning Engineer


Machine Learning Engineers are sophisticated ML experts who specialize indesigning and building intelligent machines and systems that can learn fromexperience and perform human-like tasks with minimal or no human supervision.They create advanced ML algorithms that can teach computers how to performspecific tasks without being explicitly programmed for the same. Also, MachineLearning Engineers develop state-of-the-art ML and Deep Learning systems andrun various ML tests and experiments to innovate unique AI-powered machines.Machine Learning Engineers are one of the most sought after professionalstoday. However, the job profile demands a high-level of expertise inMathematics, Statistics, and Computer Science. You can see the demand formachine learning engineers by verifying the type of salary machine learningengineers receive. Machine Learning Engineers must be well-versed with thefundamentals of Computer Science, including data structures, algorithms, andcomputer architecture. Needless to say, they must be expert programmers whoare proficient in multiple languages like Python, R, Java, C, Ruby, Perl,Scala, etc.If you’re interested to learn more about machine learning, check out IIIT-B &upGrad’s PG Diploma in Machine Learning & AI which is designed for workingprofessionals and offers 450+ hours of rigorous training, 30+ case studies &assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects &job assistance with top firms.

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