#predictionIO

[maks19770926]

PredictionIO - сервер машинного обучения с открытым исходным кодом - PredictionIO - это сервер машинного обучения с открытым исходным кодом, разработанный с использованием самых современных технологий, используемых специалистами по обработке данных, конечными пользователями и разработчиками для создания механизмов прогнозирования для любых задач машинного обучения. Основными компонентами PredictionIO являются; Платформа PredictionIO, сервер событий, галерея шаблонов. Платформа PredictionIO позволяет оценивать и развертывать движки с

[analyticsindiam]

11 Alternatives To Keras For Deep Learning Enthusiasts

Keras, one of the most popular frameworks in deep learning, is a high-level neural network library which runs on top of TensorFlow, CNTK and Theano. Written in Python, this framework allows for easy and fast prototyping as well as running seamlessly on CPU as well as GPU.   In this article, we are listing down the top 11 alternatives to Keras, the popular deep learning library: (The list is in alphabetical order) 1| CUDA CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It includes GPU-accelerated libraries, debugging and optimisation tools, a C/C++ compiler and a runtime library to deploy your application. With the help of the CUDA Toolkit, one can develop, optimise and deploy applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centres, cloud-based platforms, and HPC supercomputers. 2| Deeplearning4j Deeplearning4j is an open-sourced deep learning programming library which is written for Java and is compatible with any JVM language, such as Scala, Clojure or Kotlin. The underlying computations of this library are written in C, C++, and Cuda 3| DeepPy DeepPy is an MIT-licensed deep learning framework for designing models with complex architectures. Techniques like LSTM and Batch Normalisation are implemented inside this framework and it maintains a clean high-level interface. This framework allows for Pythonic programming based on NumPy, runs on CPU or Nvidia GPUs, implements various network architectures like Feedforward, Covnets, Autoencoders, among others.  4| Infer.NET Infer.NET is a machine learning framework for running Bayesian inference in graphical models. It provides state-of-the-art message-passing algorithms and statistical routines needed to perform inference for a wide variety of applications. There are various intuitive features in this framework such as rich modelling language, multiple inference algorithms, designed for large scale inference as well as user-extendable. With the help of this framework, various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented with ease. 5| ML Kit ML Kit is a mobile software development kit which provides convenient APIs that help to use custom TensorFlow Lite models in mobile apps. The ready to use APIs for common mobile use cases include recognizing text, detecting faces, identifying landmarks, scanning barcodes, labelling images, and identifying the language of the text. With just a few lines of codes, this framework can enable cloud-based processing, the real-time capabilities of mobile-optimised on-device models and much more. 6| NLTK Natural Language Toolkit (NLTK) is a platform for building Python programs to work with human language data. This toolkit is one of the most powerful NLP libraries which contains packages for machine learning. It provides easy-to-use interfaces along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.  7| PredictionIO PredictionIO is an open-source machine learning server which is built on top of state-of-the-art open source stack, Spark, MLlib, HDFS, and Elasticsearch. This framework includes a number of useful features such as it can respond to dynamic queries in real-time, choose from a wide variety of templates implementing important machine learning algorithms, deploy multiple engines to support multiple application features, efficiently use Huge Data or small data with flexible scaling and much more.   8| ScikitLearn  One of the most popular libraries of machine learning, ScikitLearn is a Python module for machine learning which is built on top of SciPy, NumPy and Matplotlib. The library features a number of classification, regression and clustering algorithms such as Support Vector Machines (SVM), Random Forest, Gradient Boosting, k-means clustering, among others. 9| TensorFlow Originally developed by the researchers at Google Brain, TensorFlow is one of the popular machine learning libraries. Often times, it is being compared with Keras. This is an end-to-end open-source platform for machine learning which has a comprehensive, flexible ecosystem of tools, libraries, and community resources in order to build and deploy machine learning applications.  10| Theano Theano is a popular Python library which allows a developer to define, optimise, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. There are various intuitive features in this library such as tight integration with NymPy, transparent use of a GPU, efficient symbolic differentiation, speed and stability optimisations, extensive unit-testing and self-verification, among others.  11| Torch Torch is an opensource machine learning library which supports a wide range of machine learning algorithms that puts GPUs first. This scientific computing framework includes an easy and fast scripting language known as LuaJIT, and an underlying C/CUDA implementation. Torch supports linear algebra routines, numeric optimization routines, neural network, energy-based models and much more. Read the full article

[speedysuitfun-blog]

Machine Learning Framework

[imadeit-davidjanes]

I guess the advantage of this is its integration with existing Apache Big Data services. But Google, Facebook and Microsoft have all open sourced some pretty good ML packages, and there’s only so much attention to go around.

Apache PredictionIO (incubating) is an open source Machine Learning Server built on top of state-of-the-art open source stack for developers and data scientists create predictive engines for any machine learning task. It lets you:

quickly build and deploy an engine as a web service on production with customizable templates;

respond to dynamic queries in real-time once deployed as a web service;

evaluate and tune multiple engine variants systematically;

unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics;

speed up machine learning modeling with systematic processes and pre-built evaluation measures;

support machine learning and data processing libraries such as Spark MLLib and OpenNLP;

implement your own machine learning models and seamlessly incorporate them into your engine;

simplify data infrastructure management.

Apache PredictionIO (incubating) can be installed as a full machine learning stack, bundled with Apache Spark, MLlib, HBase, Spray and Elasticsearch, which simplifies and accelerates scalable machine learning infrastructure management.

[fazellnasiri-official]

21 Open Source Libraries/Tools for Artificial Intelligence. 1. Caffe 2. CNTK 3. Deeplearning4j 4. Distributed Machine Learning Toolkit 5. H2O 6. Keras 7. Mahout 8. MLlib 9. Mycroft 10. NuPIC 11. Neuroph 12. OpenNN 13. OpenCog 14. OpenCyc 15. ONNX- Open Neural Network Exchange 16. Oryx 2 17. PredictionIO 18. SystemML 19. TensorFlow 20. Theano 21. Torch -👉Do you know any other library for AI? - #artificialintelligence #machinelearning #ai #deeplearning #library #programming #development #tool #systemml #torch #caffe #deeplearning4j #mllib #opencog https://www.instagram.com/p/B_4ZWieHKej/?igshid=f3u8cz0affy5

[un-enfant-immature]

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’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 Vizion has done 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.”

[releaseteam]

via Twitter https://twitter.com/releaseteam

[nisbieee]

Machine Learning beyond Python and R !

Author : Chawat

Machine Learning , AI and Big Data are the top technologies today evolving with a rapid pace and almost all of these are implemented in Python or R.

Python is the most popular choice because of it’s simple syntax and large number of Libraries but Python isn’t the best choice because it is dynamically typed which slows it down.

So , what are the other languages available ?

1. Golang

Go was created at Google in 2009. Go is known for it’s Fast-Compilation , Multi Core functionality , Concurrency , C like syntax and Strong typing .

A number of Libraries are available :

These are some of the most popular libraries for general ML and Neural Nets:

     golearn  – https://github.com/sjwhitworth/golearn

     gago        – https://github.com/MaxHalford/gago

     GoNN      – https://github.com/fxsjy/gonn

Popular libraries for Natural Language Processing and Decision Forests:

     Word Embedding – https://github.com/ynqa/word-embedding

     CloudForest  – https://github.com/ryanbressler/CloudForest

     RF  – https://github.com/fxsjy/RF.go

Libraries for Image Processing and Data Visualisation:

     bimg            – https://github.com/h2non/bimg

     go-graph  – https://github.com/StepLg/go-graph

2. Scala

Libraries for general ML and Decision Trees:

   Conjecture         – https://github.com/etsy/Conjecture

   SwiftLearner  – https://github.com/valdanylchuk/swiftlearner/

   ganitha                – https://github.com/tresata/ganitha

   brushfire            – https://github.com/stripe/brushfire

Popular libraries for Data Visualisation:

   MLlib in Apache Spark  – http://spark.apache.org/docs/latest/mllib-guide.html

    Hydrosphere Mist  – https://github.com/Hydrospheredata/mist

    PredictionIO              – https://github.com/apache/incubator-predictionio

    Flink                                –  http://flink.apache.org/

    Spark Notebook     –  http://spark-notebook.io/

Natural Language Processing:

    Breeze          – https://github.com/scalanlp/breeze

    FACTORIE  –  https://github.com/factorie/factorie

    Chalk             – https://github.com/scalanlp/chalk

Just two ?

No , the above two are my favourite choice. There are other languages like Julia , Lua , Java & C++ which have some great ML libraries. So , you might check them as well.

My views on Python

Python is a very powerful language with a lot of libraries and it’s simple syntax. It is thus  very Addictive but it is that simple and easy syntax of the language that makes it slow (dynamic typing).

It’s time to move onto newer technologies.

Disclaimer: I don’t mean to hurt anyone , I have just conveyed my views here.

[tak4hir0]

「Salesforce」は、Amazonから着想を得て生み出された 1986年から1998年の13年間、Oracleに在籍していたマーク・ベニオフ氏は不満を持っていた。顧客管理システム（CRM）の主流だったオンプレミスモデルは、コストや機能面で、顧客ニーズに沿っていないと感じていたのだ。 「ハードウェアを購入することなく、必要な機能を選び取り、使った分だけ支払えるシステムを作ろう」 そう考えたベニオフ氏は、1999年にクラウド型のCRM「Salesforce」を生み出した。 Salesforceはリリース直後から飛躍的に契約数を伸ばし続ける。ただし新規契約数だけ伸ばし続けても、既存顧客の解約を防がなければ、事業成長は果たせない。 どうすれば解約率を下げられるのか。ベニオフ氏は考え抜いた末に、「顧客を成功させる」という答えにたどり着く──「カスタマーサクセス」の重要性に気づいた瞬間だ。 その後は顧客に寄り添い、彼らの要望を聞くことに徹した。ユーザーコミュニティから挙がった意見をもとに機能開発、サービス改善に取り組んだ。 現在、全世界における同社のCRMシェアは約16.8％。2位のオラクル（5.7％）を大きく引き離している。2019年1月期の決算では、売上高が前年同期比26％増の132億8,200万ドル（約1兆4,900億円）に達するなど、さらなる好調ぶりが窺える。 過去5年間で集中投資した、3つの分野 Salesforce.comの躍進を支えるのは、徹底されたカスタマーサクセス戦略だけではない。積極的なM&Aも、成功要因といえるだろう。 過去5年間の買収傾向を見ていると、同社が「機械学習」「データ統合・分析」「コミュニケーション」の3分野を強化しているようだ。 以下、M&Aした企業の一覧だ。金額の記載が無いものは買収額非公表。日本円の表記は全て、執筆時点のレートを元に1ドル＝108円で換算している。 機械学習 Tempo AI：2015年5月に買収。人工知能型カレンダーアプリ事業を手がける。 MinHash：2015年12月に買収。AIマーケティング支援を手がける。 PredictionIO：2016年2月に買収。データサイエンス（機械学習）事業を手がける。 MetaMind：2016年4月、＄32.8M（約35億円）にて買収。AI（画像認識技術）テクノロジーの開発を手がける。 データ統合・分析 Implisit：2016年5月に買収。データオートメーション（予測解析）開発を手がける。 BeyondCore：2016年8月、$110M（約119億円）にて買収。BIツールやデータ・アナリティクスの開発を手がける。 Krux：2016年10月、$800M（約864億円）にて買収。顧客データ管理ツールの開発を手がける。 Attic Labs：2018年1月に買収。分散データベース開発を手がける。 MuleSoft：2018年3月、$6.5B（約7,000億円）にて買収。データ統合プラットフォームの開発を手がける。 Datorama：2018年7月、$800M（約864億円）にて買収。MI（Marketing Intelligence）ツール開発を手がける。 Griddable：2019年1月買収。データ統合クラウド開発を手がける。 Tableau：2019年6月、$15.7B（約1兆7,000億円）にて買収。BIツール開発を手がける。 コミュニケーション強化 Quip：2016年8月、＄750M（約810億円）にて買収。コンテンツコラボレーションプラットフォーム開発を手がける。 HeyWire：2016年9月買収。メッセージングアプリの開発を手がける。 Rebel：2018年10月買収。インタラクティブEmailサービス開発を手がける。 Bonobo AI：2019年5月買収。会話インテ���ジェンスツール開発を手がける。 その他 Toopher：2015年4月買収。2要素認証技術の開発を手がける。 Kerensen Consulting：2015年6月買収。クラウドコンサルティング事業を手がける。 AKTA：2015年9月買収。ユーザーエクスペリエンス（UX）デザイン事業を手がける。 SteelBrick：2015年12月、$360M （約389億円）にて買収。見積・請求アプリ開発を手がける。 YOUR SL：2016年1月買収。ITコンサルティング事業を手がける。 Demandware：2016年6月、$2.8B（約3,000億円）にて買収。ECソリューション事業を手がける。 Coolan：2016年7月買収。データセンターのパフォーマンス分析ツール開発を手がける。 gravitytank：2016年9月買収。デザインコンサルティング事業を手がける。 Twin Prime：2016年12月買収。モバイルアプリのパフォーマンス最適化ソリューションを手がける。 Sequence：2017年1月買収。UXデザインサービス事業を手がける。 CloudCraze：2018年3月買収。BtoBコマースプラットフォーム事業を手がける。 Salesforce Foundation：2019年4月、$300M（約324億円）にて買収。Salesforceの慈善事業組織を統合。 MapAnything：2019年4月、$213M（約230億円）にて買収。ロケーションインテリジェンスプラットフォーム開発を手がける。 ClickSoftware：2019年8月、＄1.4B（約1,500億円）にて買収。フィールドサービス管理ソフト開発を手がける。 2016年中頃まで機械学習関連の技術を積極的に取り込んでいるのは、2016年9月にリリースしたSalesforceの独自AI「Einstein」への布石だろう。 2016年中旬以降は特にデータ分析ツールや統合プラットフォームに注力。メッセージングアプリやEmailサービス、会話インテリジェンスツールなど、顧客とのコミュニケーションをレベルアップさせるためのツールもたびたび買収している。その他、ECソリューションやITインフラ監視など、買収対象は多岐に渡る。 年間2兆円を「データ統合・分析」分野に投資。狙うのは「データ分析の民主化」 Salesforce.comが最も攻めの投資をしているのが、データ統合・分析分野だ。 2018年にはデータ統合プラットフォームMuleSoftを約7,000億円で、先日はBIツールのTableauを約1兆7,000億円と、わずか1年足らずの期間で計2兆4,000億円もの大型買収を実施した。 MuleSoftは、各プラットフォームに分散しているデータをつなげるためのツールだ。データをつなぎこむにはAPIが必要なため、発行されていない場合は統合できなかった。 だが、MuleSoftであればAPIの設計、開発からデータ統合までを行える。古くから使っているようなAPIなど存在しないレガシーな情報基盤でも、導入によりDMP（データマネジメントプラットフォーム）にデータを蓄積したり、他ツールと連携させたりできる。 Salesforce.comがMuleSoftを買収した狙いも、まさしく「データ分析・統合」に集約されている。レガシーなシステムを使っているばかりにデータがロックインされ、活用できない──そんな顧客の課題を解決するための手段として、選択した買収だったのだ。 そこからほとんど間を置かずにTableauを買収したのは、アナリティクス機能を強化することにより、MuleSoftの機能を補完するためだと推測できる。 MuleSoftにより膨大な量のデータを取り込み、統合できる環境を構築したら、次はそれらのデータを活用しなければいけない。だが、膨大なデータを自力で分析し、戦略に活かせるアナリストはそう多くはない。 そうした��ータ分析に関する課題の解決に最適だと判断されたのが、マルチプラットフォームに対応するBIツール、Tableauだったわけだ。MuleSoftは「Integration Cloud」へ、Tableauは「Customer 360」と「Einstein Analytics」に吸収され、Salesforceのプラットフォーム強化に活かされる。 また、昨年MIツールのDatoramaを買収した意図も、同様の文脈で理解できる。 Datoramaは、SNSや広告、メールシステム、気象データなど、マーケティングに活用できるあらゆるデータプラットフォームのAPIを揃え、各プラットフォームを選択するだけで自動統合、ダッシュボードに反映する。ダッシュボードはほぼリアルタイムで更新されるため、スピーディなPDCA運用が可能だ。同サービスは「Marketing Cloud」に取り込まれ、抽出できるデータをSalesforceに集約。マーケターはより深くユーザーを理解した上で戦略設計に取り組めるようになる。 Salesforce.comが目指しているのは、誰でも一定レベル以上のデータドリブンマーケティングが実践できる世界だ。データ活用の重要性が叫ばれて随分経つが、完全に実践できている企業は多くはない。Salesforce.comは、多くの企業を悩ませる課題を本気で解決しようとしているわけだ。 「カスタマーサクセスをわれわれ以上に重視している会社はない」 買収の意図を一つずつ紐解いていくと、全てに「顧客の課題解決」と「カスタマーサクセス」が通底していることがわかる。 たとえば、2011年に有償でアナリティクス機能をリリースしようとした際、ユーザーコミュニティから猛反発を受けた。意見を重く受け止めた同社は、コミュニティの代表者と話し合った末、「自分たちが間違っていた」と判断。結果、有償サービスが無償に変更されたのだ。 当時、Salesforce.comは既に世界有数の規模を誇る企業に成長していた。総導入企業数は15万社超、年間売上130億ドルを突破する超大手企業が、ユーザーの意見を聞き入れ、サービス提供の形式そのものを変更するのは、そう簡単ではなかっただろう。それでも顧客ファーストを貫き、断行できたところに、同社の強さがある。 「カスタマーサクセスをわれわれ以上に重視している会社はない」 2019年4月、Salesforce.com20周年特別イベントに登壇したベニオフ氏はそう語った。 Salesforce.comのビジネスモデルには、SaaS、サブスクリプションの成功に必要なもの全てが盛り込まれている。彼らの姿勢からは「顧客の成功にただただ向き合うことが唯一の勝ち筋」というメッセージが、強く伝わってくる。M&Aもあくまで、カスタマーサクセスを追求するための手段にすぎない。 SaaSビジネスの先駆者かつ、最大の成功者でもある同社の動向は、常に把握しておきたい。

[maks19770926]

PREDICTIONIO - сервер машинного обучения с открытым исходным кодом - КРАТКИЙ ОБЗОР PredictionIO - это сервер машинного обучения с открытым исходным кодом, разработанный с использованием самых современных технологий, используемых специалистами по обработке данных, конечными пользователями и разработчиками для создания механизмов прогнозирования для любых задач машинного обучения. КАТЕГОРИЯБесплатное программное обеспечение для прогнозной аналитикиХАРАКТЕРИСТИКИ• Интеграция с вашим приложением• Настройка движка• Сбор и анализ данных•

[mauricebigmoflynn]

Apache PredictionIO

What is Apache PredictionIO? Apache PredictionIO is the open-source platform that allows rebel users to build their own machine learning engines to launch at the web.

Is Apache PredictionIO for experts or beginners? Open source always has a tendency to frighten the uninitiated, who expect to see something like the lines from The Matrix when they boot up the system. It shouldn’t be this way,…

[toomanysinks]

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’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 Vizion has done 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.”

source https://techcrunch.com/2019/03/28/vizion-ai-launches-its-managed-elasticsearch-service/

[fmservers]

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’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 Vizion has done 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.”

Via Frederic Lardinois https://techcrunch.com

[jastewart]

via Buffer

[salesforceconsultancyservices]

WHY IS SALESFORCE LIGHTNING PLATFORM CRITICAL TO ITS SUCCESS

As Internet of Things has grown its roots in our day to day life, more and more of our data from our devices like phones, cars, home appliances etc. are exposed to the world. According to a recent study, it is found that more than six billion machines have come online forming a connected network (Internet of) Things that generates a daily data of more than 2 quintillion bytes. In simpler terms, this huge amount of data is enough to fill up approximately 58 billion of 32GB iPads daily. It is way beyond the human capacity to process this gigantic data and transform it to create a business impact.

Salesforce IoT cloud provided the companies with a place to store huge data collected from the connected devices. The application of Artificial intelligence to it provides it with a new dimension and many more capabilities. Leveraging Salesforce Einstein and IoT, Sales Cloud can collect data from various connected devices, suggest rules and predict next actions.

With the use of Sales Cloud Einstein, businesses are equipped with the capability to consume this gigantic amount of data and create meaningful customer insights in real-time. By combining Salesforce Einstein along with the IoT data, the businesses will be capable of experiencing a new rise in IoT innovation. Companies can leverage the advantages of Sales Cloud Einstein in CRM from best Salesforce Consulting Companies.

IoT Cloud with PredictionIO

The Salesforce IoT cloud and PredictionIO, both play on the Apache open-source Frameworks like Spark, Storm or Kafka. Salesforce Apps developers will be able to build customized intelligent applications connecting logic with IoT data, all thanks to the Salesforce innovation that brought both these services together.

Analytical Score for IoT Event Data

By bringing the services like IoT cloud and PredictionIO closer, the customers can transfer data at a high speed to AI algorithms, which is trained to arrange for scoring/recording data and  indicating the behaviour of the connected device that needs a service update.

Next Suggested Action

As the IoT Cloud is directly integrated with the primary CRM products, it is connected in such a way that it not only collects IoT data but also detects the conditions where the data from connected devices requires action at employees or customer’s end. By making use of Einstein, the IoT cloud is able to mention the next vital action depending on the situation like suggesting the launch of a particular type of marketing journey based on the observed patterns it has come across, or intelligently bringing an event to a particular representative’s notice who has successfully resolved such cases in the past, & delivered high customer satisfaction.

Optimize the Journey of IoT Device Automatically

Salesforce is working towards using Einstein’s technology to enable customers to optimize their device journey. In future, the customers will be competent to automatically update the rules governing connected device interactions by providing the device journey data into IoT Cloud Einstein. This will be beneficial for the customers as they will be able to restate IoT use cases in order to quickly get to business value.

 HOW WILL INTEGRATION OF SALESFORCE EINSTEIN AND THE IoT TRANSFORM BUSINESS-

In the near future, the SaaS companies are going to completely transform the way we do business. This can be explained by understanding a simple example. Suppose your iPhone suddenly gets the hang. The factory reset is unresponsive and the screen freezes. Your daily job will be affected by this glitch and you will have no option but to wait for a week for the phone to be replaced. The replaced phone might not even include your existing contacts and other phone's data. This will all create a frustrating scenario.

Now imagine that your iPhone has a pre-installed sensor to auto-detect any error. This sensor detects your phone’s glitch and sends your case file in the Apple’s CRM platform. This sensor even directs your case automatically to a customer service agent and provides all your customer information and purchase history to them. Imagine that the agent is equipped with a contact centre solution integrated with its Salesforce Service Cloud with Salesforce’s AI, Einstein.

In this case:

1. The customer service gets notified automatically through your phone without any action taken by you.

2. The Customer Service agent receives your customer information and purchase history that enables him to trigger a replacement order with express shipping.

3. The agent notifies you on a call about the replacement being shipped. The Salesforce Service cloud has speech analytics that can record your call, capture your sentiments and stores information in Salesforce Cloud.

4. Speech Analytics program is able to detect any emotional changes in your voice while you express any concern during the call.

5. Salesforce Einstein can study your current sentiment and compare it with past purchase information and other customer service cases to suggest a relevant action. (eg: if the customer was concerned about the shipment time, the agent can prioritize his shipment for immediate delivery)

6. You receive your replaced phone on the same day- just moments after you notice the glitch.

7. The agent ports all your old phone’s data and contacts into your new phone by accessing your data that was stored in the cloud.

In this example, Salesforce AI Einstein and IOT work together to create an environment in which the customer receives the help from an agent before even noticing the problem. This way the company is able to gain complete customer satisfaction and emerge with the reputation of a Customer First Company. The customer wait time decreases from months to just a few hours- no hostile calls from the customers, no phone hold time, no scouring the customer’s information from their files.

This is just the scenario of the service front of the business. Other departments like Sales reps will also be benefited with this integration of AI with IOT. The CRM files can travel between sales and Service.  IoT could study the customer trends and trigger opportunities for upselling. With the help of AI, the leads can be prioritized based on the probability of success. This way the company’s sales will also increase drastically.

This is the future we are entering into and we are almost here.

Don’t you think it’s time to get the most out of your Salesforce solutions with Einstein! Contact us today for your Salesforce Einstein Integration Services.

[ulyss]

This is a most popular repository list for Scala sorted by number of stars STARS FORKS ISSUES NAME DESCRIPTION 17228 15780 549 spark Mirror of Apache Spark 11255 1820 71 predictionio PredictionIO, a machine learning server for developers and ML engineers. Built on Apache Spark, HBase and Spray.