DOCSLIB.ORG

HBSAAL Newsletter December 2019

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
HBSAAL Newsletter December 2019 HBSAAL Newsletter December 2019 INTRODUCTION Dear Friends and Members, It is my pleasure to share with you the first HBS Alumni Angels of London (HBSAAL) newsletter. With over 5,000 alumni in London we play an important role in helping foster an increasingly vibrant and economically important start-up ecosystem. Our local Alumni chapter was launched in 2011, and in that time our members have made investments totalling £7.2M (excluding secondary investments) in over 40 companies. We all know that angel investing requires patience and a portfolio mindset, so we are delighted with notable exits such as Stupeflix and DogBuddy along with significant up-rounds from recent investments such as FlatFair, Seldon, Urban and Pavegen. This past July five companies that received investment from our members were shortlisted for the UK British Angel Association (UKBAA) awards. HBS alum Romi Savova of PensionBee won Best High Growth Woman Founder. Our investors bring more than just cash they also bring a strong network and valuable business experience. We are all committed to life-long learning and our educational events have covered topics ranging from thematic investing in artificial intelligence and fintech to practical tips on angel investing. We had a successful pitch event on Nov 19th, and on Nov 27th we hosted a panel discussion with some of the most successful angel investors in the UK. This was hosted at McKinsey & Co’s new UK headquarters. We will also shortly be introducing you to the wider HBS Alumni Global organisation where you can gain access to syndicated deals from HBSAA Chapters, such as New York, Silicon Valley, Beijing, Paris and Brazil along with access to global educational webinars. We are a volunteer organisation and we are indebted to them for their time and commitment to our community. We are always looking for new volunteers, especially in marketing, investor outreach and deal flow. We also encourage you, our members, to continue investing. And please spread the word by inviting your network of sophisticated investors to attend our events. And of course, we are very open to ideas on how we can better serve you. We wish you all a happy festive season! Sincerely, Simon Greenman, MBA 1999 Co-Chair, Harvard Business School Alumni Angels of London Club RECENT EDUCATIONAL EVENT: ‘AI AND MACHINE LEARNING LEADERSHIP’ Two years after we first dove into Artificial Intelligence and Machine Learning Leadership, we revisited the topic in front of a vibrant full house on May 14th, kindly hosted by Taylor Wessing. READ MORE Kenneth Cuckier, the Senior Editor at The Economist and our Moderator for the night, kicked off the debate by engaging the audience and challenging the panellists on key questions such as are only the US and China leading the AI race? Do small companies stand a chance? How disruptive will AI be within the next 5 years? Will AI just rip through the labour market and create a job apocalypse? Sana Kharegnani (Head of the UK Government Office for Artificial Intelligence) gave us some critical insight about the UK government’s AI vision by placing it within its social context. Steve Crossan (Venture Partner at FirstMinute Capital) and Simon Greenman (a Founder and Partner in Best Practice AI) provided broad and balanced perspectives on AI while Dr Marc Weiner (CEO and co-founder of Faculty) explained how AI actually works from a technical standpoint. RECENT PITCH EVENT: 19th November 2019 On November 19th, the Harvard Business School Alumni Angels of London (HBSAAL) held the third and last pitch event of 2019. This pitch event was moderated by Rohan Pradhan, the COO of Deliveroo. During the event, four high profile startups presented their pitch: Lake Parime, Pepper, Sook, ZOA Robotics. Many thanks to Taylor Wessing for hosting us so graciously. READ MORE Rohan Pradhan, COO of Deliveroo Rohan is responsible for the company’s sales and account management across its 13 countries. He has been a member of Deliveroo's executive team since 2016. Prior to this, Rohan was a founding team member for Prime Now 3P, Amazon's 1-hour delivery service in the US and was the lead PM for Prime Now's European expansion. Many thanks to Taylor Wessing for hosting us so graciously in their event space. Pitch companies The four companies that presented were: (1) Lake Parime is involved in the design, manufacture, and operation of high-performance computing systems powered by clean energy. The company’s ‘Powerbox’ solution (a data-centre housed in a shipping container) enables wind and solar farms to generate an extra income by utilising power that cannot be economically stored or sold to the grid. Lake Parime offers a service that turns energy into money - the data centre and service to end-users is fully managed by Lake Parime. Contact Sath Ganesarajah - [email protected] (2) Pepper is the UK’s market leading B2B SaaS platform transforming the customer experience in hospitality with mobile. Creating merchant branded apps for hospitality companies, Pepper modernises the customer journey, while gathering actionable date for direct mobile marketing. Working with over 100 companies in the UK, US, Canada and Australia in over 600 locations, Pepper has recently signed partnership deals with Square and Coca Cola to expand rapidly in the US. Contact Simon Kelton - [email protected] (3) Sook offers a solution which maximises the utility of retail units allowing multiple different users to occupy them flexibly and affordably across a week. Sook does this by transforming empty shops into digitally enabled spaces that allows anyone to brand the space exactly how they want to at the touch of a button. This reduces the costs and waste of physical fit out and allows multiple tenants to make use of the space at different times of the day. Sook has been operational at its pilot site for six months and is launching its second site, supported by Legal and General, in January. Contact John Hoyle - [email protected] (4) ZOA Robotics develops commercially-affordable high-performance robots that enables Inspection as a Service for industrial customers. ZOA’s Inspection Service removes humans from hazards, increases the frequency and reliability of data acquired, helps plant managers schedule maintenance and alerts asset owners to impending failures before they occur. By solving stairs ZOA acquires multi-sensor data on all assets in an industrial site and provides asset health information through an intuitive Dashboard to plant managers on a subscription basis. Contact Thiago Azevedo - [email protected] --------------------------------------------------------------------------------------------------------------------------------- ---- HBSAAL SUCCESS STORIES We would like to congratulate a number of previous HBSAAL startup laureates for their continued success and for their nominations at the 2019 UK British Angel Association (UKBAA) Awards under the following categories: • PensionBee - Best Investment in Fintech, and Best High Growth Woman Founder • Dogbuddy - Best Exit of the Year • Flatfair - Best Investment in Fintech • Urban - Best Scale Up Team of the Year • Seldon - Best Investment in Deep Tech READ MORE PensionBee CEO Romi Savova was named ‘Best High Growth Woman Founder’ at the 2019 UKBAA Angel Investment Awards PensionBee is an award-winning online pension provider founded in 2014 by our CEO Romi Savova and CTO Jonathan Lister Parsons. They have transformed complicated pension transfer processes that typically take months to complete into a simple five minute process via a smartphone, and helped thousands of people save for a better retirement. PensionBee raised seed funding with the help of HBSAAL in March 2015, before launching to the public later that same year. They now count over 60,000 active customers with over £650 million in assets under administration. https://www.pensionbee.com --- DogBuddy, the leading UK dog sitting marketplace, was acquired by Rover in October 2018 DogBuddy, founded in 2013 by Richard Setterwall, raised seed funding through HBSAAL in February 2014 and went on to appoint one of the HBSAAL investors as Chair. The company went through a number of further successful raises through 2014 - 17 and a merger to grow to become Europe’s leading marketplace. They have facilitated over 1m dog nights through 25,000 vetted dog sitters. In October 2018 DogBuddy was acquired by Rover.com, the US-based global leader who has raised over $300m funding to date. https://uk.dogbuddy.com --- Flatfair, the leader in deposit free renting, raised $11m eighteen months after HBSAAL pitch Flatfair lets landlords offer "deposit-free" renting to tenants. Franz Doerr co-founded Flatfair in 2016 with “a focus on making renting fairer and more transparent for landlords and tenants". HBSAAL helped Flatfair raise its initial funding in November 2017. In August 2019, the London- based fintech raised $11m in a funding round led by Index Ventures. With this capital, Flatfair is hiring data scientists, business development specialists and product engineers, and is building new features and expanding its platform. Flatfair recently won Tech Startup Business of the year at the GoTech Awards and was named “exceptional” in the Best EA (Estate Agents) Supplier Guide 2020. www.flatfair.co.uk --- Urban, the European leading online wellness and massage marketplace, raised $10m in May 2019 Urban, the number one wellness app in Europe, operates in four cities and lets you book wellness services on demand, including massage, osteopathy, facial and nail services. Founded in 2014 by Jack Tang and Giles Williams, Urban benefits practitioners and customers alike. On Urban, practitioners take home 72% of bookings and customers are able to choose between trusted, pre-vetted practitioners. Urban raised its initial funding through HBSAAL in March 2015. In May 2019, Urban raised a $10m Series B with the ambition to add new services, expand to more cities, and become a “one-stop-shop for on-demand wellness services”.
Load more

Recommended publications
  • WORLD FINTECH REPORT 2018 Contents 4 Preface
    in collaboration with WORLD FINTECH REPORT 2018 Contents 4 Preface 8 Executive Summary FinTechs Are Redefining the Financial Services Customer Journey 13 Competition and Rising Expectations Spur 14 Customer-Centricity Push – Identify gaps left by traditional Financial Services providers and explore changing customer expectations. Emerging Technologies Enable Customer 19 Journey Transformation – Data and insights are reshaping personalized experiences through automation and distributed ledger technology. Alignment with Customer Goals, Creation of 27 Trust, and Delivery of Digital, Agile, and Efficient Processes Are Catalysts for Success – Firms are driving innovation and operational excellence through agile and digital teams. World FinTech Report 2018 The Symbiotic Relationship between FinTechs and Traditional Financial Institutions 35 FinTech and Incumbent Firms’ Respective 36 Competitive Advantages and Shortcomings Make Collaboration a Logical Fit – A partnership ecosystem of FinTechs, incumbents, and other vendors fosters a win-win for stakeholders. Finding the Right Partners for Collaboration 44 Is Essential – Maintaining and accelerating scale is a common FinTech firm struggle, so the right collaboration partner is critical. Successful Collaboration Requires Commitment 49 and Agility from FinTechs and Incumbents – Selection of the appropriate engagement model boosts FinTech scale-up efforts. The Path Forward: An Impending Role for BigTechs? 60 – BigTechs could have massive impact on the Financial Services industry. Preface Once rather homogenous and somewhat staid, the financial services marketplace has transformed into a dynamic milieu of bar-raising specialists. These new-age professionals are devoted to meeting and exceeding the expectations of consumers who have become accustomed to personalized services from industries such as retail, travel, and electronics. Financial services customers no longer rely on one or two firms.
    [Show full text]
  • Kubernetes & AI with Run:AI, Red Hat & Excelero
    Kubernetes & AI with Run:AI, Red Hat & Excelero AI WEBINAR Date/Time: Tuesday, June 9 | 9 am PST What’s next in technology and innovation? Kubernetes & AI with Run:AI, Red Hat & Excelero AI WEBINAR Your Host: Presenter: Presenter: Presenter: Tom Leyden Omri Geller Gil Vitzinger William Benton VP Marketing CEO & Co-Founder Software Developer Engineering Manager Kubernetes for AI Workloads Omri Geller, CEO and co-founder, Run:AI A Bit of History Needed flexibility Reproducibility and and better utilization portability Bare Metal Virtual Machines Containers Containers scale easily, they’re lightweight and efficient, they can run any workload, are flexible and can be isolated …But they need orchestration 2 Enter Kubernetes Track, Create Efficient Execute Across Schedule and Cluster Different Operationalize Utilization Hardware 3 Today, 60% of Those Who Deploy Containers Use K8s for Orchestration* *CNCF 4 Now let’s talk about AI Computing Power Fuels Development of AI Deep Learning Classical Machine Learning Manual Engineering 6 Artificial Intelligence is a Completely Different Ballgame New Distributed Experimentation accelerators computing R&D 7 Data Science Workflows and Hardware Accelerators are Highly Coupled Data Hardware scientists accelerators Constant Workflow Under-utilized hassles Limitations GPUs 8 This Leads to Frustration on Both Sides Data Scientists are IT leaders are frustrated – speed and frustrated – GPU productivity are low utilization is low 9 AI Workloads are Also Built on Containers NGC – Nvidia pre-trained models for
    [Show full text]
  • CNCF Webinar Taming Your AI/ML Workloads with Kubeflow
    CNCF Webinar Taming Your AI/ML Workloads with Kubeflow The Journey to Version 1.0 David Aronchick (Microsoft) - @aronchick Elvira Dzhuraeva (Cisco) - @elvirafortune Johnu George (Cisco) - @johnugeorge SNARC Maze Solver Minsky / Edmonds (1951) 2000 3 2006 4 2007 5 2008 6 2010 7 2013 8 2014 9 2015 10 Today 11 One More ML ???Solution??? 12 One More ML ???Solution??? 13 14 GitHub Natural Language Search Prototype MVP With Demo In Jupyter Demo with front-end mockup with Experiments.Github.Com: +3 Months Notebook: 2 Weeks blog post: +3 Days https://github.com/hamelsmu/code_search https://towardsdatascience.com/semantic-code-se https://experiments.github.com/ arch-3cd6d244a39c 15 Resource Provisioning Numpy Scheduling Experiment Tracking Jupyter Access Control Declarative Deployment TF.Transform Drivers HP Tuning TF.Estimator Orchestration Profiling Docker Lifecycle Management Validation Seldon Networking KubeCon 2017 17 Resource Provisioning Numpy Scheduling Experiment Tracking Jupyter Access Control Declarative Deployment TF.Transform Drivers HP Tuning TF.Estimator Orchestration Profiling Docker Lifecycle Management Validation Seldon Networking Mission (2017) Make it Easy for Everyone to Develop, Deploy and Manage Portable, Distributed ML on Kubernetes 20 Kubeflow 2018 Mission (2018) Make it Easy for Everyone to Develop, Deploy and Manage Portable, Distributed ML on Kubernetes 23 Kubeflow 2019 Mission (2019) Make it Easy for Everyone to Develop, Deploy and Manage Portable, Distributed ML on Kubernetes 26 Mission (2020) Make it Easy for Everyone
    [Show full text]
  • Kubeflow: End to End ML Platform Animesh Singh
    Kubeflow: End to End ML Platform Animesh Singh Jupyter Notebooks MXNet KFServing Tensorflow Seldon Core Serving Pytorch TFServing, + Training Operators XGBoost, + MPI Metadata Kale Workflow Building HP Tuning Fairing Tensorboard Tools Prometheus KF Pipelines Pipelines TFX Your Speaker Today: CODAIT 2 Animesh Singh Kubeflow STSM and Chief Architect - Data and AI Open Source github.com/kubeflow Platform o CTO, IBM RedHat Data and AI Open Source Alignment o IBM Kubeflow Engagement Lead, Kubeflow Committer o Chair, Linux Foundation AI - Trusted AI o Chair, CD Foundation MLOps Sig o Ambassador, CNCF o Member of IBM Academy of Technology (IBM AoT) © 2019 IBM Corporation Kubeflow: Current IBM Contributors Untrained Model Christian Kadner Weiqiang Zhuang Tommy Li Andrew Butler Prepared Preparedand Trained AnalyzedData Model Data Deployed Model Jin Chi He Feng Li Ke Zhu Kevin Yu IBM is the 2nd Largest Contributor IBM is the 2nd Largest Contributor IBMers contributing across projects in Kubeflow Kubeflow Services kubectl apply -f tfjob High Level Kubernetes API Server Services Notebooks Low Level APIs / Services Pipelines TFJob PyTorchJob Pipelines CR KFServing Argo Jupyter CR MPIJob Katib Seldon CR Study Job Spark Job Istio Mesh and Gateway Kubebench TFX Developed By Kubeflow Developed Outside Kubeflow Adapted from Kubeflow Contributor Summit 2019 talk: Kubeflow and ML Landscape (Not all components are shown) Community is growing! 8 Multi-User Isolation ML Lifecycle: Build: Development, Training and HPO Untrained Model Prepared Preparedand Trained
    [Show full text]
  • Build an Event Driven Machine Learning Pipeline on Kubernetes
    Assign Hyperparameters Initial Model and Train Create Model PreparedPrepared andand Trained AnalyzedAnalyzed Model DataData Monitor DeployedDeployed Validate and Deploy ModelModel Build an Event Driven Machine Learning Pipeline on Kubernetes Yasushi Osonoi Animesh Singh Developer Advocate IBM STSM, IBM kubeflow kfserving maintainer osonoi animeshsingh Center for Open Source Improving Enterprise AI lifecycle in Open Source Data and AI Technologies (CODAIT) Code – Build and improve practical frameworks to enable more developers to realize immediate value. Content – Showcase solutions for complex and real-world AI problems. Community – Bring developers and data scientists to engage with IBM • Team contributes to over 10 open source projects • 17 committers and many contributors in Apache projects • Over 1100 JIRAs and 66,000 lines of code committed to Apache Spark itself; over 65,000 LoC into SystemML • Over 25 product lines within IBM leveraging Apache Spark • Speakers at over 100 conferences, meetups, unconferences and more CODAIT codait.org 3 DEVELOPER ADVOCATE in TOKYO Tokyo Team is a part of Worldwide Developer Advocate Teams! Developer Advocate City Leader WW Developer Advocate WW Developer Advocate Client Developer Advocate AKIRA ONISHI NORIKO KATO KYOKO NISHITO YASUSHI OSONOI Program Manager WW Developer Advocate WW Developer Advocate Digital Developer Advocate TOSHIO YAMASHITA TAIJI HAGINO AYA TOKURA JUNKI SAGAWA @taiponrock https://developer.ibm.com/patterns/ https://developer.ibm.com/jp/ Please follow me @osonoi IBM’s history
    [Show full text]
  • The AI Growth Capital of Europe 2 London: the AI Growth Capital of Europe London: the AI Growth Capital of Europe 3
    London: The AI Growth Capital of Europe 2 London: The AI Growth Capital of Europe London: The AI Growth Capital of Europe 3 About CognitionX CognitionX is the AI advice platform that connects every organisation with a global on-demand network of AI experts. The platform allows experts in AI from around the world to share invaluable expertise with organisations of any size and sector who want to tap into that knowledge. Through the platform, organisations can find an answer to their AI question very quickly, regardless of where they are. AI experts can either share knowledge for free or charge a fee. Founded in 2015, CognitionX’s mission is to drive innovation in, and deployment of, AI by making expertise universally accessible, levelling the playing field for the millions of organisations who don’t have access to strategy consultants or advisory boards, while also giving AI experts a new outlet for, and a way to monetise, their expertise. CognitionX.com Report authors Stephen Allott James Kingston Mick Endsor Matthew Miller Tabitha Goldstaub Milan Sachania Ed Janvrin 4 London: The AI Growth Capital of Europe London: The AI Growth Capital of Europe 5 Mayor’s Foreword There are few areas of innovation that have the power to define our future economy and society more than artificial intelligence. As Mayor of London, it is my goal to ensure both that London is at the forefront of developing and capitalising on these new technologies, and that all Londoners can benefit from the opportunities they will create. I commissioned CognitionX to undertake this research in order to discover the facts behind what many already know to be true - that London is the AI capital of Europe, and that our supportive and welcoming tech ecosystem is the best place to build and scale an AI company.
    [Show full text]
  • From Notebook to Kubeflow Pipelines with Minikf & Kale
    From Notebook to Kubeflow Pipelines with MiniKF & Kale Stefano Fioravanzo, Software Engineer, Arrikto Vangelis Koukis, Founder & CTO, Arrikto The Problem ● Setting up an ML stack/pipeline is incredibly hard ● Setting up a production ML stack/pipeline is even harder ● Setting up an ML stack/pipeline that works across the 81% of enterprises that use multi-cloud* environments is EVEN HARDER * Note: For the purposes of this presentation, “local” is a specific type of “multi-cloud” Source: “Building an ML stack with Kubeflow” by Abhishek Gupta, Google AI Huddle - Bay Area Perception: ML Products are mostly about ML Data Verification Machine Resource Management Data Collection Serving Infrastructure ML Code Configuration Monitoring Feature Extraction Process Analysis Tools Management Tools Credit: Hidden Technical Debt of Machine Learning Systems, D. Sculley, et al. Reality: ML Requires DevOps; lots of it Data Machine Monitoring Verification Resource Management Data Collection Configuration Serving ML Infrastructure Code Analysis Tools Feature Extraction Process Management Tools Credit: Hidden Technical Debt of Machine Learning Systems, D. Sculley, et al. Why Kubeflow ● End-to-end solution for ML on Kubernetes ● Containerized workload ● Experiment exploration with state-of-art AI technologies ● Easy on-boarding ● Outstanding community and industry support Community! 6 Just a SMALL sample of community contributions Arrikto IBM ● Jupyter manager UI ● Pipeline components for spark, ffdl ● Pipelines volume support ● Katib ● MiniKF ● KFServing ●
    [Show full text]
  • Red Hat Openshift with Data Services Powered by Intel® Technology
    Solution Brief Hybrid-Multicloud | Enterprise Container Platform Cloud-Native, Hybrid-Multicloud Platform: Red Hat OpenShift with Data Services Powered by Intel® Technology Transform your business and get ready for hybrid-multicloud databases, AI, and machine-learning workloads with Red Hat OpenShift Container Platform and Red Hat Data Services on Intel® technology Executive Summary Hybrid-cloud-capable, cloud-native infrastructure is a major part of data center deployments, whether you’re talking about databases, artificial intelligence (AI), machine-learning, or telecommunications workloads. Today’s cloud-native applications use a distributed cloud approach, with some workloads running in private clouds and others running in public Solution Benefits clouds. Ninety-three percent of enterprises have a multicloud strategy.1 • Simple scalability from on- Infrastructure modernization, automation, and cloud-native containers premises to the hybrid cloud helps are important aspects of business transformation. The portability and enterprises easily accommodate repeatability of containers can create cost and resource savings, coupled with additional changes in workload faster time to market and rapid innovation. Containers have little overhead, demands. which helps to lower hardware, maintenance, and licensing costs. They can • High storage performance of be implemented quickly, and components can be shared among containers. Red Hat Data Services (OpenShift Organizations need high performance from their workloads to remain Container Storage) powered by competitive. They require flexible solutions that can run traditional Intel® Optane™ SSDs can help data analytics and AI applications. The solution described in this brief, enterprises get the most out of co-developed by Intel and Red Hat, helps enterprises modernize their data their Intel® Xeon® processors centers and start taking advantage of containers while lowering total costs.
    [Show full text]
  • Explainable. Repeatable. Scalable. Pachyderm: Enterprise
    Pachyderm: Enterprise Engineered to make data science Containerized Explainable. Data Pipelines Repeatable. Scalable. Data Versioning Data Lineage The unspoken truth about real-world data science. Instead of spending time innovating and us- ing data science as a force-multiplier to the business, most data science teams are stuck between disparate systems, juggling ad-hoc GOOGLE CLOUD scripts, and hacking their way through com- plex pipeline jungles. AWS AZURE How Pachyderm can help Pachyderm is an enterprise-grade, open source data science platform that makes ex- ON-PREM plainable, repeatable, and scalable Machine Learning (ML) and Artificial Intelligence (AI) HYBRID a reality. Data Versioning Containerized Data True Data Lineage Pipelines A robust data versioning engine Pachyderm enables you to build Pachyderm provides you with true powers the heart of the Pachyderm end-to-end data pipelines using data lineage. Our solution helps platform. Every change to your any language or framework you capture the entire journey data gets tracked and managed you want. Together with data of your data, code, models, and with ease. Adding, removing and versioning, Pacyderm Pipelines results in one unified system. With modifying files all get meticulously intelligently process only changed that power at your finger tips, your captured and recorded by the data, while also efficiently data team can reason about the Pachyderm engine automatically. distributing the computation complex relationships between With git-like familiarity, teams of across the cluster for faster results. them and drive business decisions data scientists can swiftly track the efficiently. dependencies and relationships between datasets, their models, and everything in between with “Pachyderm helped us confidence.
    [Show full text]
  • 2021 Mlops Platforms Vendor Analysis Report
    2021 MLOps Platforms Vendor Analysis Report Executive guidance on selecting the optimal technology solution for supporting your MLOps journey 1 Intro Defined as a separate discipline only recently, the MLOps movement has already moved from the сonceptualization stage to corporate boardroom discussions on implementation. But while nearly every industry has now entered the AI race, adoption of operations best practices and thus competitiveness remains unequal. AI and ML leaders today already have a better understanding of the MLOps lifecycle and the procedures and technology required for deploying new models into production (and subsequently scaling them). The State of AI 2021 report by McKinsey notes that 51% of AI high performers have standard tool frameworks and development processes in place for developing AI models. However, laggards can still catch up and even overtake the leaders if they get strategic with process standardization and automation, which in fact is what most MLOps platforms promise to facilitate. An MLOps platform is a centralized infrastructure hub featuring resources, tools, frameworks, and process automation scenarios for managing all steps of the machine learning lifecycle. Machine Learning Lifecycle Datasets e Tr as s a ele re in R tu in D a g e p Fe l oy e t u p Experiment ML Ops m o C g Te in g s a tin k g c r Code Pa Monito 2 Back in the day, IDEs revolutionized the speed and quality of software development. MLOps vendors are now attempting to create a similar level of speed, convenience, and automation for data scientists with the ultimate promise of reducing model development and deployment timelines by 2X-10X.
    [Show full text]
  • Introduction 2 ML on Kubernetes 3 Tooling Availability 3 Effective
    Introduction 2 ML on Kubernetes 3 Tooling availability 3 Effective Resources Utilization 4 Separation of Concerns 4 Popular ML tools with Kubernetes 5 AI, Machine Learning and Deep Learning 5 Machine Learning Phases & Tools 7 Experimental Phase 8 Data Preparation, Cleanup, and Visualization 9 ML Framework and Model Algorithm 10 Model Training 11 ​ Validation and Tuning 13 ​ Production Phase 14 ​ Model Training 15 ​ Model Serving 16 ​ Model Performance and Tuning 16 ​ Higher-level Machine Learning Tools 17 ​ Jupyter Notebook 17 ​ Pipelines 19 ​ KubeFlow 19 ​ How Spectro Cloud Can Help 20 Introduction With businesses and enterprises continuing to transform themselves into technology companies, applications are the new life-blood of business. Applications are defining the way most companies in many industries compete, to provide the best customer experiences and drive competitive advantage. As these companies look to further drive positive business outcomes, they’re increasingly relying on insights from the massive amounts of data they’ve generated and collected over the years. However, with traditional algorithms finding it difficult to process and provide any meaningful value from the data, organizations are turning to data science and machine learning. These new technologies not only help analyze the data, but provide real-time business decisions and intelligence, without human intervention. From human-like voice assistants, to fraud detection or even automatic mortgage approvals - the possibilities of machine learning applications are limitless. Increasingly conventional and machine-learning applications alike are built and deployed as containers, running on the popular orchestration platform Kubernetes. Enterprises are adopting Kubernetes to drive down operational costs, increase business agility, and provide faster time to market for their applications.
    [Show full text]
  • Kubeflow Shows Promise in Standardizing the AI Devops Pipeline
    Kubeflow Shows Promise in Standardizing the AI DevOps Pipeline Kubeflow Shows Promise in Standardizing the AI DevOps Pipeline by, James Kobielus November 7th, 2018 Developing applications for the cloud increasingly requires building and deployment of this functionality as containerized microservices. Increasingly, artificial intelligence (AI) is at the core of cloud applications. Addressing the need to build and deploy containerized AI models within cloud applications, more vendors of application development tools are building support for containerization into their data-science workbenches […] © 2018 Wikibon Research | Page 1 Kubeflow Shows Promise in Standardizing the AI DevOps Pipeline Developing applications for the cloud increasingly requires building and deployment of this functionality as containerized microservices. Increasingly, artificial intelligence (AI) is at the core of cloud applications. Addressing the need to build and deploy containerized AI models within cloud applications, more vendors of application development tools are building support for containerization into their data-science workbenches and for programming of these applications using languages such as Python, Java, and R. Data science workbenches are the focus of much AI application development. The latest generation of these tools is leveraging cloud-native interfaces to a steady stream of containerized machine learning models all the way to the edge. Productionization of the AI DevOps pipeline is no easy feat. It requires a wide range of tooling and infrastructure capabilities, ranging from the workbenches that provide access to such popular AI modeling frameworks as TensorFlow and PyTorch to big data analytics, data governance, and workflow management platforms. In a cloud-native context, it also requires the ability to deploy containerized machine learning and other AI microservices over Kubernetes orchestration backbones in public, private, hybrid, multi-cloud, and even edge environments.
    [Show full text]