LogMeIn公司. creates innovative, cloud-based SaaS services for Unified Communication & Collaboration, Identity and Access Management, and Customer Engagement and Support. 成立于2003年,总部设在波士顿, LogMeIn began by building web-based software that gave IT administrators access to remote desktops. They’ve been a catalyst for reimagining how and where people work, delivering security in a bring-your-own device world and providing customer support and engagement for the digital-first generation.

自2009年首次公开募股以来, LogMeIn has steadily diversified its offerings to include collaboration and remote meetings with GoToMeeting, as well as everything from remote support for smartphones, 平板电脑, and computers (with LogMeIn Rescue) to password and access management (with LastPass). Their Bold360 system delivers AI-powered engagement to customers, 呼叫中心和员工, bringing together the best of human and chatbot support under a single platform.



在以色列LogMeIn人工智能卓越中心, 该公司的团队要处理大量的文本, audio, and video that needs to get quickly processed and labeled for its data scientists to go to work delivering machine learning capabilities across their product lines.

“Our job at the AI hub is to bring the best-in-class ML models of, 在必赢网站网址的案例中, 语音识别和NLP,埃亚尔·赫尔登伯格说, Voice AI Product Manager at the LogMeIn AI Center of Excellence. “It became clearer that the ML cycle was not only training but also included lots of data preparation steps and iterations, 必赢网站网址经常改变准备逻辑! That lack of parallelization and scale really hurt our ability to get datasets to our researchers so they could get to the real work of testing, 培训和建立必赢网站网址的必赢网站网址模型.”

“例如, one of our steps is a heavy processing of audio for sort of specific cleaning,摩西·阿布拉莫维奇说, LogMeIn数据科学工程师. “To process only one iteration of all our training data would sum up to seven weeks on the biggest compute machine AWS has to offer — and this is only one step. That means lots of unproductive time for the research team.”

“We had started to look for a parallel compute solution that would be friendly with our technology stack and knowledge — Dockers and Kubernetes, Abramovitch继续. “We just wanted things to work without becoming experts in data pipelines.”




“厚皮类动物’s parallelism helps us run the transformer at scale. 基本上, there is no limit of how many datum transformers we can run at once because as 厚皮类动物 runs on Kubernetes, 必赢网站网址可以按必赢网站网址想要的规模扩大,”Abramovitch说.

LogMeIn did a small POC at first and realized that instead of taking seven to eight weeks to transform their data, 厚皮类动物 crunched that time down to an amazing seven to ten hours.

LogMeIn’s research and business teams immediately saw the impact of 厚皮类动物’s speed and scale. “必赢网站网址的模型更加准确, and they are getting to production and to the customer’s hands much faster,”Heldenberg说. 一旦你消除了时间浪费, 构建块式数据准备, 整个链条都会受到影响. If we can go from weeks to hours processing data, it greatly affects everyone. 这样必赢网站网址就可以专注于有趣的事情:研究, manipulating the models and making greater models and better models.”

与厚皮类动物, LogMeIn has scaled their pipelines tremendously because it can do so much of the work in parallel, without the team having to rewrite its software to take advantage of that parallelization. 厚皮动物为它们做缩放和分块.

“必赢网站网址运行过的最大的管道大约是2,000 or 3,000个容器用于一条管道,”Abramovitch说. “它大约有15个节点,每个节点有96个cpu.”


厚皮类动物 also delivers tremendous flexibility because it’s agnostic to the tools data scientists need to get their work done right. LogMeIn uses different ML frameworks like TensorFlow and PyTorch, and also utilizes in-house and open-source toolkits like Kaldi. The LogMeIn team wrote its own pre-processing tools to adjust it to the different frameworks.

“你得快点,”赫尔登伯格说. “You need to work with your existing tools, your existing languages, your existing dependencies. You want to invest as little as possible in learning, right? 你只需要处理一些东西. And since 厚皮类动物 utilizes really flexible tools like Docker and Kubernetes, 它非常民主化.”

Instead of thinking about building a monstrous infrastructure that takes months and months to do, LogMeIn was up and running with 厚皮类动物 in a few days and delivering real impact on the business in weeks, as they reworked their pre-processing to take advantage of its capabilities.


When other teams and companies are running into data processing challenges, Heldenberg has some simple advice for them: “First of all, 我建议他们评估厚皮动物. 我已经把它推荐给我的朋友了.”

“Not everyone on the AI research team understands what 厚皮类动物 does, 他们只知道它速度快,能提供他们需要的东西, 当他们需要的时候,”他观察. “That’s a good thing because it lets the data science team focus on what it does best — doing research and training models — instead of focusing on the infrastructure. “Everyone knows that 厚皮类动物 is the processing framework, and it will just go fast.”

“The fact that we’re able to prepare our data so fast helped them to run a lot of training. Prior to using 厚皮类动物, we thought we’d never be able to execute those training sessions so fast. But because the data preparation process became so short, the research team was able to deliver much faster and create a lot of new models because of it.” 当LogMeIn的研究人员现在来找他们, the AI Center of Excellence team knows what to say: “We’ll just do it in 厚皮类动物.”