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Market Update January 2020 a Machine Learning & Artificial Intelligence Market Update

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Market Update January 2020 a Machine Learning & Artificial Intelligence Market Update PRIVATE & CONFIDENTIAL Market Update January 2020 A Machine Learning & Artificial Intelligence Market Update B Canaccord Genuity Overview / Update Artificial Intelligence Machine Learning f (x) Deep Learning Page 1 Driven by your success. Machine Learning (“ML”) and Artificial Intelligence (“AI”) continue to generate strong levels of attention and excitement in the marketplace, based on the promise of self-correcting algorithms driving increased intelligence and automation across a number of mission critical applications and use cases When ML & AI were first introduced as concepts that would impact the IT landscape, most companies in the sector were limited to collections of data scientists or technologies in search of use cases – today there are defined categories emerging and companies with real traction in ML/AI, as well as a growing set of tangible use cases While there is real innovation and traction occurring in ML/AI, in some cases it is still difficult to understand where certain companies truly play in the ML ecosystem and the unique value that each brings to the table – this presentation aims to provide a framework to understand the ML landscape First we look to define and better understand ML/AI technology, both the underlying algorithms as well as data science platforms, operational frameworks and advanced analytics solutions which leverage and / or optimize core ML/AI technologies – these are also described as “AI Infrastructure” From a category perspective, we focus primarily on horizontal platforms which can provide data science frameworks and / or advanced analytics solutions across a number of verticals, as well as the underlying software platforms which ingest, store, manage, test and integrate data sources and models We highlight activity in the space by some of the largest platform players in the broader Cloud / IT platform sectors We also take a look at selected vertical application players that leverage ML/AI as a core source of differentiation – many of these businesses are gaining traction faster than horizontal platforms, as there can be a sharper value proposition and path to market as customers can more clearly understand and quickly leverage the benefits associated with these applications Page 2 Driven by your success. ▪ ML/AI at the highest level describes the ability for machines and algorithms to self-learn and think and act more like humans. Machine Learning and Deep Learning: Subsets ‒ Artificial Intelligence: the ability of machines to perform tasks that require of the Broader AI Opportunity human intelligence (e.g., visual perception, speech recognition, decision- making, translation) What Makes a Machine Intelligent? ‒ Cognitive Computing: the simulation of human thought processes in a While AI is the headliner, there are actually subsets of the technology that computerized model through self-learning systems that mimic the way the can be applied to solving human problems in different ways. human brain works (e.g., data mining, pattern recognition and natural language processing) ‒ Machine Learning: a subset of AI techniques which use statistical methods to automate the ability of a system to iteratively learn from data and extract insights without being explicitly programmed through algorithms Artificial Intelligence ‒ Deep Learning: a branch of Machine Learning that data scientists use to build models based on artificial neural networks (interconnected systems that learn to perform tasks by analyzing examples across the many systems without being programmed with task-specific rules and guidelines) Machine Learning ▪ Predictive/Advanced Analytics Solutions provide the platforms and tools to build and deploy predictive models and analytics applications using ML and other statistical algorithms. f (x) ▪ The increasing demand for Machine Learning is being driven by a number of Deep Learning trends, including the ongoing data explosion, the rapid adoption of cloud, Artificial Intelligence (AI) – Machine Learning (ML) – mobile & IoT technologies and strong need for deep and predictive A process where a Algorithms that allow intelligence. computer solves a task in a computers to learn from ‒ The exploding volume and increasing complexity of data that the world is now way that mimics human examples without being “swimming in” has quickly driven the need for ML/AI solutions behavior. Today, narrow AI – explicitly programmed. ‒ The movement of applications and infrastructure into the Cloud (where lots when a machine is trained to of data also resides) provides a strong platform for the development of ML/AI do one particular task – is frameworks and applications, while the proliferation of mobile & IoT devices becoming more widely used, Deep Learning (DL) – allows that data to be created, accessed and processed at the edge from virtual assistants to A subset of ML that uses ▪ As more than 50% of enterprise IT organizations are experimenting with ML/AI self-driving cars to deep artificial neural in various forms, the Global AI market is forecasted to reach over $51.3 billion automatically tagging your networks as models and in the next three years, growing at a CAGR of 49.6% from 2018 to 2022.(1) friends in your photos on does not require feature ▪ As many of these solutions will also reside in or be delivered from the Cloud, Facebook. engineering. the global market for Machine Learning as a Service (“MLaaS”) is estimated to grow to $5.4 billion by 2021, at a CAGR of 39.2%.(2) 1. Statista 2. Transparency Markets Research Page 3 Driven by your success. Path to Machine Learning Machine Learning Methods Two of the most widely adopted Machine Learning methods are supervised learning and unsupervised learning – while hybrid forms are also emerging Capable of creative, instinct driven tasks that require deep context. Ask questions • Supervised learning Human Only and hypothesize about abstract answers, ‒ The algorithm receives a set of inputs along with the but are not scalable and can only process a corresponding “correct“ outputs and continuously modifies Intelligence limited amount of data its model until the actual output equals the targeted outputs Can process massive amounts of data. ‒ Commonly used where historical data predicts future Human + Data Data visualization and analytics have events, e.g., predicting fraud Informed become key for web performance, business intelligence, stock analysis, etc • Unsupervised learning ‒ The algorithm must explore data and find some structure within; the system is not shown the “right answer” Leveraging AI, predictive analytics, Human + Machine ‒ Works well on transactional data, e.g., identifying similar and big data, applications go beyond segments of customers for marketing campaigns Reasoning just data visualization to provide Assisted targeted recommendations • Semi-supervised learning ‒ The algorithm receives some labeled data (i.e., correct answers) as training and a large amount of unlabeled data Fully automated algorithms but still ‒ Useful when the cost of fully-labeled data is too high, e.g., designed and monitored by humans. Machine Only facial recognition These solutions are highly scalable without the need of human touch • Reinforcement learning Algorithmic ‒ The algorithm uses trial and error to determine which actions yield the greatest rewards over a given amount of Manual Automatic Action time ‒ Often used for robotics, gaming, and navigation Sources: x.ai and SAS Page 4 Driven by your success. Data Science Platforms Advanced Analytics ML/AI Platforms • Data science platforms are generally frameworks and tools for • Predictive analytics and other categories of advanced analytics use bringing data pipelines / ML algorithms into production apps sophisticated quantitative methods to produce insights above and beyond traditional query and reporting • Leverage heavy ML expertise and IP but are generally agnostic to specific types of analytics and the resulting applications • Generally offer specific types of analytics solutions across a targeted range of verticals Open Source-Focused Vendors / Platforms Stream Processing / Real-Time Analytics Data Integration / Preparation / Governance • Vendors adding value or commercial support on • Analytics and data management • Data integration involves preparing, top of specific open source platforms platforms which ingest, analyze and normalizing and transforming data across take action on fast data streams disparate sources, which reside on-premise • Often developed in a collaborative and public or in the Cloud manner, which generates a more diverse design • Highly relevant for IoT use cases in perspective and evolution of the core platforms particular and other environments • These solutions allow ML/AI and analytics which involve real-time information solutions to be more effective out of the box Hadoop / NoSQL / Graph Datastores Next-Gen / New SQL Databases Data / Analytics Optimization • Hadoop is an open source data store w/ vendor • Vendors which are focused on • Technologies and associated vendors support now largely centered around one providing traditional relational / SQL DB focused on optimizing data access and vendor (Cloudera) functionality in cloud-native, scale out management through virtualization, platforms, often with analytics and caching or other optimization-oriented • NoSQL and graph DBs continue to address transactional capability as well techniques emerging real-time use cases Cloud / IT Platform Players Broader BI / Search / Data Analytics • Manage the infrastructure and platforms
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