Data is the new gold Google Intel Apple buy into AI ML Big Data
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.
4 Analytics and Data Science
Data analytics and data science are two highly sought after tech skills thatare consistently hand-in-hand with big data, whose revenues are now projectedto grow by 14.1% in 2026. Big data initiatives and advanced analytics arebeing launched by 84% of tech enterprises to create greater accuracy andaccelerate decision-making processes.According to LinkedIn’s report on emerging jobs, data science emerges at thetop for the last three years. While the two tech careers are closely related,analysis is an entry-level tech skill. On the other hand, data science becomesmore advanced.Industries that require data specialists include finance, softwaredevelopment, health, education, and e-retail. Data scientists command anaverage salary of $101,000 and get voted as having the third-best jobs in theUS by Glassdoor’s annual Best Jobs in America report.Data analysis and science specialists help drive better decision making byproviding an overview of the organization, analysis, and interpretation of bigdata. On mastering this tech skill, you’ll be able to build projects with anunderstanding of neural networks, classifiers, and ML algorithms.
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.
Data is the new gold: Google, Intel, Apple buy into AI/ML, Big Data
Analytics startupsCategorization & design by the author. * Total of deals: 19–17 investments, 2 acquisitions * Most active investors: Google (7 deals) & Intel (6 deals) * Sector subcategories: Automated monitoring platforms, AI/ML, predictive analytics, NLP & voice tech, Data governance & visualizationOne of the consequences of digitalization is that we have more data than weknow what to with. Since the lockdown, even more so. Businesses now produceexabytes of data on everything from their performance, to their processes,products, and productivity that it becomes a Sisyphean feat to continuouslyderive any meaningful insights from it. FAGMIA have identified a growing needfor software companies who will take raw data, clean it, sort it, model it,process it, automate it, cluster it, visualize it, and package it for clientsin a neat, digestible format.Startup Spotlight: Inductiv * What it does: automated cleaning of AI training data * Big tech acquirer: Apple * Why we love it: AI & Machine Learning models are only as good as the quality of the data they’re trained on. Inductiv developed technology that uses artificial intelligence to automate the task of identifying and correcting errors in data. Founded in 2019 and based in Waterloo, Canada, the automated data cleaning tech company was acquiredon May 27, 2020 for an undisclosed amount. The Cupertino giant had also bought another AI startup just a month prior, Ireland-based speech recognition startup Voysis. Although in typical Apple fashion, the iPhone maker declined to comment on the motivation or plans for either acquisition, experts have guessed Inductiv & Voysis could be put to the task of improving Siri.
Intel, Google, & Microsoft take lead in powerful computing technologies
Categorization & design by the author. * Total of deals: 12–10 investments, 2 acquisitions * Most active investors: Intel (5 deals) & Google (4 deals) * Sector subcategories: Quantum, cloud, edge, AI chips, semiconductorsWith more connected devices generating more and more data for AI models andanalytics tools to process, computing can easily eat up a lot of time, energy,and costs. Lots of startups are coming up with innovative ways of acceleratingdata processing and reducing its resource demands, whether its edge-, cloud-,or quantum technology.Google & Amazon are both betting on the commercial applications of quantumcomputing with their recent participation in the $62m Series B round of IonQ,whose quantum machines can run at room temperature, unlike those of theircompetitors. Microsoft has also been active in the quantum space. In April,the company’s investment arm participated in the $215m Series C financing ofPsiQuantum, a company developing a quantum computer that uses photons asqubits., which is a fundamentally different approach from the other players inthe field.Startup Spotlight: MemVerge * What it does: memory-converged infrastructure system (MCI) * Big tech investor: Intel * Why we love it: In the history of computing, “memory” and “storage” have always been two different concepts. MemVerge, aims to combine the two concepts and make every application run in-memory, so they can handle more data workloads. The storage startup is delivering the world’s first Memory-Converged Infrastructure system, built with proprietary Distributed Memory Objects (DMO) technology. DMO technology provides a logical convergence layer that harnesses Intel’s Optane DC persistent memory to let data-centric workloads run flawlessly at memory speed.
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