Mar
28
2019
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Vizion.ai launches its managed Elasticsearch service

Setting up Elasticsearch, the open-source system that many companies large and small use to power their distributed search and analytics engines, isn’t the hardest thing. What is very hard, though, is to provision the right amount of resources to run the service, especially when your users’ demand comes in spikes, without overpaying for unused capacity. Vizion.ai’s new Elasticsearch Service does away with all of this by essentially offering Elasticsearch as a service and only charging its customers for the infrastructure they use.

Vizion.ai’s service automatically scales up and down as needed. It’s a managed service and delivered as a SaaS platform that can support deployments on both private and public clouds, with full API compatibility with the standard Elastic stack that typically includes tools like Kibana for visualizing data, Beats for sending data to the service and Logstash for transforming the incoming data and setting up data pipelines. Users can easily create several stacks for testing and development, too, for example.

Vizion.ai GM and VP Geoff Tudor

“When you go into the AWS Elasticsearch service, you’re going to be looking at dozens or hundreds of permutations for trying to build your own cluster,” Vision.ai’s VP and GM Geoff Tudor told me. “Which instance size? How many instances? Do I want geographical redundancy? What’s my networking? What’s my security? And if you choose wrong, then that’s going to impact the overall performance. […] We do balancing dynamically behind that infrastructure layer.” To do this, the service looks at the utilization patterns of a given user and then allocates resources to optimize for the specific use case.

What VVizion.ai hasdone here is take some of the work from its parent company Panzura, a multi-cloud storage service for enterprises that has plenty of patents around data caching, and applied it to this new Elasticsearch service.

There are obviously other companies that offer commercial Elasticsearch platforms already. Tudor acknowledges this, but argues that his company’s platform is different. With other products, he argues, you have to decide on the size of your block storage for your metadata upfront, for example, and you typically want SSDs for better performance, which can quickly get expensive. Thanks to Panzura’s IP, Vizion.ai is able to bring down the cost by caching recent data on SSDs and keeping the rest in cheaper object storage pools.

He also noted that the company is positioning the overall Vizion.ai service, with the Elasticsearch service as one of the earliest components, as a platform for running AI and ML workloads. Support for TensorFlow, PredictionIO (which plays nicely with Elasticsearch) and other tools is also in the works. “We want to make this an easy serverless ML/AI consumption in a multi-cloud fashion, where not only can you leverage the compute, but you can also have your storage of record at a very cost-effective price point.”

Feb
19
2019
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Google acquires cloud migration platform Alooma

Google today announced its intention to acquire Alooma, a company that allows enterprises to combine all of their data sources into services like Google’s BigQuery, Amazon’s Redshift, Snowflake and Azure. The promise of Alooma is that it handles the data pipelines and manages them for its users. In addition to this data integration service, though, Alooma also helps with migrating to the cloud, cleaning up this data and then using it for AI and machine learning use cases.

“Here at Google Cloud, we’re committed to helping enterprise customers easily and securely migrate their data to our platform,” Google VP of engineering Amit Ganesh and Google Cloud Platform director of product management Dominic Preuss write today. “The addition of Alooma, subject to closing conditions, is a natural fit that allows us to offer customers a streamlined, automated migration experience to Google Cloud, and give them access to our full range of database services, from managed open source database offerings to solutions like Cloud Spanner and Cloud Bigtable.”

Before the acquisition, Alooma had raised about $15 million, including an $11.2 million Series A round led by Lightspeed Venture Partners and Sequoia Capital in early 2016. The two companies did not disclose the price of the acquisition, but chances are we are talking about a modest price, given how much Alooma had previously raised.

Neither Google nor Alooma said much about what will happen to the existing products and customers — and whether it will continue to support migrations to Google’s competitors. We’ve reached out to Google and will update this post once we hear more.

Update. Here is Google’s statement about the future of the platform:

For now, it’s business as usual for Alooma and Google Cloud as we await regulatory approvals and complete the deal. After close, the team will be joining us in our Tel Aviv and Sunnyvale offices, and we will be leveraging the Alooma technology and team to provide our Google Cloud customers with a great data migration service in the future.

Regarding supporting competitors, yes, the existing Alooma product will continue to support other cloud providers. We will only be accepting new customers that are migrating data to Google Cloud Platform, but existing customers will continue to have access to other cloud providers.   
So going forward, Alooma will not accept any new customers who want to migrate data to any competitors. That’s not necessarily unsurprising and it’s good to see that Google will continue to support Alooma’s existing users. Those who use Alooma in combination with AWS, Azure and other non-Google services will likely start looking for other solutions soon, though, as this also means that Google isn’t likely to develop the service for them beyond its current state.

Alooma’s co-founders do stress, though, that “the journey is not over. Alooma has always aimed to provide the simplest and most efficient path toward standardizing enterprise data from every source and transforming it into actionable intelligence,” they write. “Joining Google Cloud will bring us one step closer to delivering a full self-service database migration experience bolstered by the power of their cloud technology, including analytics, security, AI, and machine learning.”

Sep
19
2017
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Google Cloud’s Natural Language API gets content classification and more granular sentiment analysis

 Google Cloud announced two updates this morning to its Natural Language API. Specifically users will now have access to content classification and entity sentiment analysis. These features are particularly valuable for brands and media companies For starters, GCP users will now be able to tag content as corresponding with common topics like health, entertainment and law (cc: Henry).… Read More

Sep
15
2017
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A typical day for researchers on Google’s Brain Team

 What do you and researchers on Google’s Brain Team have most in common? You both probably spend a lot of time triaging email. In a Reddit AMA, 11 Google AI researchers took time to share the activities that consume the greatest chunks of their days. Email was a frequent topic of conversation, in addition to less banal activities like skimming academic papers and brainstorming with… Read More

Sep
14
2017
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Facebook is the latest tech giant to hunt for AI talent in Canada

 Facebook is turning its attention to Canada with a new AI research office in Montreal. Google and Microsoft already have outposts in the city and countless other tech companies, including Uber, have researchers based in Canada. McGill University’s Joelle Pineau will be leading Facebook’s AI efforts in Montreal. Pineau’s research focus tends to lean heavily on robotics and… Read More

Sep
13
2017
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The director of Baidu’s Silicon Valley AI Lab has departed

 Adam Coates, director of Baidu’s Silicon Valley AI Lab, has left the company. A source confirmed the exit and Coates has updated his LinkedIn reflecting the change. Coates joined the Chinese search giant back in May of 2014 to lead a California-based team of 50 machine learning developers. Baidu has built up an established proficiency in natural language processing. While at Baidu,… Read More

Jul
13
2017
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Here are the winners of the Google Cloud machine learning pitch-off

 Back in March at Google’s Cloud Next conference, the company announced plans to run its own machine learning startup competition side-by-side with Data Collective and Emergence Capital. Four months later, 10 startups, pulled from a pool of 350+ applicants, presented onstage at Google’s Launchpad Space in San Francisco. The startups vied for three prizes; here are the winners. Read More

May
23
2017
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Google’s Unique Reach tells marketers when you’ve seen the same ad a gazillion times

 We have all suffered the pain of seeing the same ad for a BBQ grill we can’t fit in our apartment on our phone, tablet, laptop and work desktop. The duplication is not only annoying, it’s wasteful for advertisers.
At Google’s Marketing Next conference in San Francisco, the company announced Unique Reach, a new measurement tool that captures the number of times the same… Read More

May
23
2017
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Google Attribution is a free and easy way to evaluate marketing efforts

 At Google’s Marketing Next conference, the company is announcing a new beta for Google Attribution, a free tool for examining the role that different marketing strategies play in customer purchasing decisions.
Regardless of device or marketing channel, Google wants Attribution to be a home for evaluating marketing campaigns. By creating a tight loop between strategy, ad spend and… Read More

May
23
2017
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Google is now using deep learning to measure store visits

 Google is announcing a major update to its store visits measurement tool today at its Google Marketing Next conference. Google has used anonymized location and contextual data since 2014 to estimate brick and mortar store visits spurred by online ads. The company is augmenting its existing models with deep learning to bring insights to even more customers. Omnichannel marketing is as big of… Read More

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