Social media platforms are using AI to serve their users. Know what they are doing

Artificial intelligence holds the potential to change every industry around the world. We are witnessing mass digital transformations and the adoption of AI and machine learning technologies to accelerate the growth of the business and boost customer satisfaction.

We can train these technologies to leverage individual behavioral patterns, preferences, beliefs, and interests to personalize customer experiences. This data becomes fuel for the AI systems to draw insights to make increasingly relevant predictions and curate strategies to prevent impending business risks and financial losses.

The popularity of artificial intelligence has grown so much that currently, it has become a chief component of social media platforms. Top social media platforms like Facebook, Snapchat, and Twitter are reaping the benefits of artificial intelligence. Introducing voice bots, identifying visuals, enhanced security, social media platforms are using AI, entirely under the discretion of the company that owns the platform. Even social media marketers have started using AI, machine learning, and automation technologies to boost their audience reach.

Let us look at some ways how top social media platforms are using AI for their benefits and to serve the customers.

Facebook:

Facebook Artificial Intelligence Researchers, also known as FAIR, have been working to analyze and develop AI systems with the intelligence level of a human. It will not only help advance artificial intelligence technologies but also help monitor malicious activities on the platform.

Facebook uses an artificial intelligence tool called the Deep Text to monitors the comments, posts, and other data generated on Facebook to understand how people use different languages, slangs, abbreviations, and exclamation marks, to learn the context. The company is also applying ML algorithms to build its automatic AI-based translation system to enable users from different parts of the world to translate the posts appearing in their news feed.

Facebook has also introduced chatbots in its application. It has also introduced artificial intelligence-based systems to thwart suicides.

Twitter:

One of the many ways Twitter uses AI in its platform is to understand what tweets recommendations to suggest on the users’ timelines. It aims to recommend the most relevant tweets to the users for an increased personalized experience. Twitter also uses artificial intelligence to fight against racist, homophobic, islamophobic, and other inappropriate remarks. In UK and Germany, the company has started levying fines to prevent hate speeches, fake news, and illegal content on the platform.

Twitter uses IBM Watson and natural language processing (NLP) to track and remove abusive messages. Watson is not only capable of understanding the natural language but also interferes with the tones in the messages and the meanings of different visuals, therefore, it can analyze millions of obscene and inappropriate messages in seconds.

Instagram:

Millions of people use Instagram as a means to share images, videos, and statuses with friends and families. Facebook-owned Instagram has also started implementing big data and artificial intelligence to enhance user experience, filter spam, and boost the results of target advertising. With the help of tags and trending information, the platform users can find photos of a particular activity, place, event, restaurants, food, and discovery experiences.

Recently, in a study, Instagram has used over 100 million photos available on the platform to learn more about global clothing patterns. Like any other social media platform, Instagram uses AI to fight against hate speeches and cyberbullying. It uses Deep Text to identify these messages and posts and remove them from the platform.

Snapchat:

Snapchat is using machine learning models and augmented reality technology, to superimpose digital animation on videos, like windshield wipers on an individual’s glass or droplets of water falling, and other feats like that. Snapchat’s AI engineers are training deep learning models to do things like intercepting hand gestures. These hand gesture models can then be imported to create other features using augmented reality.

The goal behind implementing artificial intelligence in the platform is to serve its enormous user base and enable these users to access these technologies easily.

Pinterest:

Machine learning is crucial for Pinterest’s core business. The platform serves tailored content to enhance user engagement and retain more customers. Without machine learning models, it would be possible for Pinterest to generate such data.

The platform dedicates most of its operations to the continuous iteration of the ML models. It not uses ML for pin recommendations but also uses it in their daily business operation, to run the business efficiently. These ML models are only good as long the users are actively spending time on the platform. The more data generated, the better recommendations the users will gain.

The post HOW ARE TOP SOCIAL MEDIA PLATFORMS USING AI TO SERVE CUSTOMERS appeared first on Artificial Intelligence.

These are heady days in data science and machine learning (DSML) according to Gartner, which identified a “glut” of innovation occurring in the market for DSML platforms. From established companies chasing AutoML or model governance to startups focusing on MLops or explainable AI, a plethora of vendors are simultaneously moving in all directions with their products as they seek to differentiate themselves amid a very diverse audience.

“The DSML market is simultaneously more vibrant and messier than ever,” a gaggle of Gartner analysts led by Peter Krensky wrote in the Magic Quadrant for DSML Platforms, which was published earlier this month. “The definitions and parameters of data science and data scientists continue to evolve, and the market is dramatically different from how it was in 2014, when we published the first Magic Quadrant on it.”

The 2021 Magic Quadrant for DSML is heavily represented by companies to the right of the axis, which anybody who’s familiar with Gartner’s quadrant-based assessment method knows represents the “completeness of vision.” No fewer than 13 of the 20 vendors to make the quadrant’s cut landed on the right side, which indicates active innovation.

Generating new DSML features and exploring new DSML methods is the name of the game in this fast-moving business, Gartner says. “There remains a glut of compelling innovations and visionary roadmaps,” the analysts wrote. “…[V]endors are heavily focused on innovation and differentiation, rather than pure execution. Innovation remains key to survival and relevance.”

The Connecticut-based analyst firm did not sound surprised to conclude that the cloud biggies have moved strongly into the space. “The long-expected gigantic presence in this market of Google and Amazon is now easily felt as they compete with Microsoft for supremacy in terms of DSML capabilities in the cloud,” the analysts write.

However, that does not mean that they are sucking all the air out of the room, as smaller companies have found success in the market, with a few achieving what Gartner termed “hypergrowth.” A few well-established leaders from the previous generation of statistical tools, like SAS, MathWorks, and IBM (SPSS) are also doing well, Gartner notes. In fact, those three vendors are collectively doing better than AWS, Google, and Microsoft when it comes to ability to execute.

The DSML market is young and vibrant, and there is ample revenue and funding opportunities for companies that differentiate themselves on the product side, Gartner says. There is just a “moderate” level of M&A activity at this time, which indicates a growing market. With that said, the vendors who made Gartner’s cut had to prove themselves by meeting certain customer-count and financial performance criteria. And of course, they have to have a product that meets the definition of an DSML platform.

Which begs the question: Just what is an DSML platform? Gartner defines it as a place “to source data, build models and operationalize machine learning,” either by certified, card-carrying data scientists or people who are doing data science work, i.e. citizen data scientists, data engineer, or ML specialists.

Beyond that broad definition, Gartner identified 13 other capabilities that may (or may not) exist in a given DSML platform, including: data ingestion; data preparation; data exploration; feature engineering; model creation and training; model testing; deployment; monitoring; maintenance; data and model governance; explainable artificial intelligence (XAI); business value tracking; and collaboration.

Here’s a brief description of the pros and cons provided for each of the vendors listed in the Magic Quadrant, courtesy of Gartner:

Leaders Quadrant

Databricks Unified Data Platform

Pros: Scalable multi-cloud support; empowerment of data scientists; execution and expansion.

Cons: Lack of support for citizen data scientists; need for governance and responsible AI; growing cloud competition.

Dataiku Data Science Studio

Pros: Support for citizen data scientists; focus on business value; market traction.

Cons: Heavy use of extensions and plugins; emerging story around “XOps” (i.e. unified management of data, ML, models, and platforms); pricing for smaller teams.

IBM Watson Studio on IBM Cloud Pak for Data

Pros: support for multiple personas; composite AI vision; responsible AI and governance.

Cons: scope of auto AI features; doubts about Watson brand; lack of clarity in product-bundling.

MathWorks MATLAB

Pros: Robust composite AI capabilities; integrated domain knowledge; verifiable and reliable ML.

Cons: Interface lacks usability among non-engineers and non-scientists; interpretability of ML models; lack of augmented DSML capabilities.

SAS Viya

Pros: Market understanding and presence; cloud-native architecture and open source integration; automated feature engineering and modeling.

Cons: Perceived high cost; product bundling; marketing strategy.

TIBCO Software (various products)

Pros: Leading edge DSML capabilities; integration of DS and BI/analytics; support for collaboration and applied analytics.

Cons: Limited ModelOps capabilities; lack of support for citizen data science capabilities; financial growth in 2020.

Visionaries Quadrant

AWS (various products)

Pros: Breadth and depth of cloud platform; performance and scalability; data labeling and human-in-the-loop capabilities

Cons: Lack of attention on citizen data scientist; rapid rollout of products and maturity; maturity of on-prem, hybrid, and multi-cloud support

DataRobot Enterprise AI Platform

Pros: Sales strategy and execution; high-touch customer service; successful acquisitions.

Cons: Complexity of product portfolio; resource-heavy onboarding; capability gaps.

Google Cloud AI Platform

Pros: Responsible AI vision and capabilities; research contributions; cohesion and simplification of consolidated products.

Cons: Rapid pace of change; steep learning curve; lack of capabilities for on-prem, hybrid, and multi-cloud deployments.

KNIME Analytics Platform

Pros: Breadth and depth of DSML capabilities; commitment to open source; visual workflow coherence.

Cons: Limitations in enterprise deployments; responsible AI vision; low market traction.

Microsoft Azure Machine Learning

Pros: Strong support for enterprise DS; support for multiple personas; openness and partnerships.

Cons: Requirement of use of other Azure services; immaturity of on-prem, hybrid, and multi-cloud capabilities; lack of support for augmented DSML capabilities.

RapidMiner (various products)

Pros: Support for multiple personas; “clear vision and delivery of aligned features”; expandability and governance.

Cons: Growth rate; average advanced analytics capabilities; academic perception of product.

H2O.ai (various products)

Pros: Vision for value creation; extensive automation; rich AI explainability features (XAI).

Cons: Lack of some data access and data prep features; OEM partner strategy; collaboration and cohesion.

Challengers Quadrant

Alteryx Analytics Process Automation

Pros: Support for multiple personas; product packaging and go-to-market strategy; customer support.

Cons: Changing product portfolio; high cost; lack of innovation.

Niche Players Quadrant

Alibaba Cloud’s Platform for AI (PAI) Studio and Data Science Workshop

Pros: Strong community in China; advanced use-case modeling; and seamless integration.

Cons: Focus on Asia; lack of product vision; narrow usage and focus on professional data scientists.

Altair Knowledge Studio and Knowledge Works

Pros: Ease of use; support for data pipelines; customer satisfaction

Cons: Functional gaps in lineup; limited rollouts in some industries; relatively slow growth.

Anaconda Enterprise

Pros: Trusted and flexible platform; based on open source; culture of collaboration.

Cons: Focus on technical audience; lack of model operationalization functions; runtime stability.

Cloudera Data Platform

Pros: Native Spark on Kubernetes; support for complex data workloads; metadata support for DataOps and MLOps.

Cons: No GUI for development; lack of coherence of products; domain-specific solutions.

Domino Data Lab Data Science Platform

Pros: Support for large, expert teams; mature MLOps capabilities; support for on-prem, hybrid, and multi-cloud.

Cons: Support for small, immature DS teams; low market visibility; open source vision;

Samsung SDS Brightics AI

Pros: Comprehensive ecosystem vision; data access, prep, and visualization; ease of use and collaboration.

Cons: Limited adoption outside of Asia; gaps in product vision; limited capabilities in ModelOps and explainability.

This is indeed a great time to be in the data science and machine learning business. Whether you’re a user of these tools or helping to develop them, the rapid pace of innovation is not only exciting but good for business as a whole.

The post A ‘Glut’ of Innovation Spotted in Data Science and ML Platforms appeared first on Artificial Intelligence.

Analyst house Gartner, Inc. has released its 2021 Magic Quadrant for Data Science and Machine Learning Platforms. The researcher defines a data science and machine learning platform as “a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner-sourced and open-source). Its primary users are data science professionals, including expert data scientists, citizen data scientists, data engineers, application developers and machine learning (ML) specialists.”

Data science and machine learning platforms can support tasks across the data science lifestyle like problem and business context understanding, data ingestion, data preparation, and data exploration. They can also include feature engineering, model creation and training, model testing, deployment, monitoring, maintenance, data and model governance, explainable AI, business value tracking, and collaboration tools. The expansion of capabilities among tools in this market makes it ‘more vibrant’ than ever according to Gartner.

Buyers should be aware that this market offers a broad array of products for different requirements and preferences, typically dependent on the user persona. This Magic Quadrant is aimed especially at expert data scientists, citizen data scientists, supporting roles, LOB data science teams, and corporate data science teams. While the eye may begin analyzing this report in the Leaders quadrant, Gartner notes that it is best to evaluate your specific needs when assessing vendors. They add “A vendor in the Leaders quadrant, for example, might not be the best choice for you. Equally, a Niche Player might be the perfect choice.”

Data science and machine learning vendors remain focused on innovation and product differentiation, and the market is made up of established and thought-leading providers as well as startups with emerging value propositions. The proliferation of augmented analytics has drawn this market to a near collision with the horizontal Analytics and Business Intelligence space, adding another layer. Gartner concludes “Traditionally these have been discrete markets with different buyers, but that situation is changing.”

While most vendors are aiming for a “sweet spot” with their platforms as it relates to adoption by expert and citizen data scientists, there are several key areas of differentiation. These include the user interface, augmented data science and machine learning, MLOps, performance and scalability, hybrid and multicloud support, and support for cutting-edge use cases and techniques.

Gartner adjusts its evaluation and inclusion criteria for Magic Quadrants as software markets evolve. As a result, Alibaba Cloud, Amazon Web Services (AWS), Cloudera, and Samsung SDS have been added to the 2021 report. No solution providers were removed from this edition.

In this Magic Quadrant, Gartner evaluates the strengths and weaknesses of 20 providers that it considers most significant in the marketplace, and provides readers with a graph (the Magic Quadrant) plotting the vendors based on their ability to execute and completeness of vision. The graph is divided into four quadrants: niche players, challengers, visionaries, and leaders. At Solutions Review, we read the report, available here, and pulled out the key takeaways.

SAS and IBM headline the Leaders quadrant for 2021. SAS remains the overall leader via its Visual Data Mining and Machine Learning product and thought leadership in composite AI, MLOps and decision intelligence. SAS touts an enterprise-grade solution and keen eye for market trends, as well as cloud-native architecture and integrations with popular open-source tools. IBM drastically improved its Completeness of Vision across Gartner’s horizontal axis to attain its 2021 position. IBM now offers a modern and complete product featuring multipersona support and a focus on responsible AI and governance.

Dataiku and TIBCO Software are clustered even closer together this year than last, though both remain major players. Dataiku is known to have an understanding of the citizen data scientist role, focuses on business value via performance metrics, and has what Gartner refers to as a “strong product roadmap” and vision. TIBCO Software offers a platform for Connected Intelligence and has a science and engineering focus. It has a leg up on the field when it comes to model deployment and production, and offers leading-edge functionality. TIBCO is also an excellent consideration for decentralized analytic professionals.

Databricks and MathWorks round out the Leaders quadrant for 2021, both maintaining positions in the bottom-third of the column. Databricks’ Unified Data Platform is available in multiple clouds and touts excellent scalability features. Its recent acquisition of Redash further expands the platform. Expert data scientists will find the most immediate value from the Databricks platform. MathWorks primarily services those in engineering and asset-centric organizations. The vendor offers advanced composite AI capabilities, deep domain expertise, and verifiable and reliable machine learning as well.

Alteryx is the only 2021 Challenger in this Magic Quadrant. The vendor is currently overhauling its focus, evident through the release of Alteryx Analytic Process Automation in May. It mainly serves clients in manufacturing, financial services, consumer packaged goods, retail, healthcare, and government. Alteryx touts ease of use for different user personas, a growing collection of partnerships, and strong customer sentiment regarding overall experience with the platform.

Microsoft, Google and AWS are competing for the top spot among cloud-based data science and machine learning vendors in the Visionaries column. Microsoft has the highest score for Ability to Execute in this class and offers capabilities for citizen and expert users. The mega-vendor also features strong MLOps functionality and security and governance for compute quota and cost management. Google currently offers its Cloud AI Platform but is soon set to release a unified product. Google touts thought leadership in ML research and responsible AI, and has recently made a major effort to reorganize its software release schedule.

Amazon’s data science and machine learning tools run through its SageMaker product line. AWS is natively integrated with many proprietary cloud data and analytics solutions as well. The mega-vendor touts perhaps the strongest performance and scalability of any product listed. Its data labeling and human-in-the-loop capabilities are also very popular. DataRobot retains its position between Microsoft and AWS just a stone’s throw away from the Leaders column. It features data science augmentation, prediction quality management via a Humble AI initiative, and high-touch customer service for providing business value.

KNIME, RapidMiner and H20.ai form a cluster on the right-hand side of the Visionaries quadrant to round it out. The open-source KNIME Analytics Platform and commercial KNIME Server products bridge the gap between development and production so data scientists and end-users can better collaborate. KNIME touts more than 4,000 nodes for connecting to data, as well as a visual workflow that can be broken down into individual components. RapoidMiner’s offerings reflect current trends like multipersona, collaboration, XAI, and model governance. New capabilities like FeatureMart and Feature Catalog are major value-adds.

H2O.ai earned the highest score for Completeness of Vision of any provider in this report. It is a thought leader in the automation and augmentation of data science and machine learning, especially for time-series analysis. H2O also offers several explainability capabilities for both modeling and feature engineering.

Alibaba Cloud, Cloudera, and Samsung SDS make their collective debuts as 2021 Niche Players. Alibaba is currently an Asia-only play for its retail, internet and data service customers. Cloudera Machine Learning is delivered as a service on top of the Cloudera Data Platform. The vendor mainly focuses on unifying machine learning workflows across data warehousing, data engineering, data science and machine learning, and operationalization. Cloudera’s strengths include native use of Spark on Kubernetes, complex data processing, and metadata management for DataOps and MLOps. Like Alibaba Cloud, Samsung SDS is currently concentrated in the Asian market.

Domino offers end-to-end capabilities on-prem or in the cloud. It released Domino Model Monitor last June which, according to Gartner, shows a “commitment to enterprise MLOps.” Domino is best-fit for code-first data science teams. Anaconda’s recent innovations around model governance and scalability are noteworthy, while Anaconda Enterprise remains a  trusted, flexible and recognized product. Anaconda enables the optimization of open-source tools as well. Altair is a strong consideration for service-centric industries, offers various simulation and high-performance computing tools, and touts excellent customer scores for deployment, service, and support.

The post What’s Changed: 2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms appeared first on Artificial Intelligence.

For those unaware, Artificial Intelligence alludes the re-enactment of human insight into machine so as to enable them to think like members of the human race. Thus, attributes like problem solving, learning and critical thinking are carried on by machines.

Artificial intelligence brings along a colossal potential to the table which is ultimately sculpturing the fate of technology in future.

Thus, its no surprise that business industry is investing more and more in this platform that holds the promise of changing the world as we know it. Thus as per estimates, given the investments that artificial intelligence is witnessing, it will easily cross the value of trillions of dollar in the future.

AI also houses gigantic potential when it comes to development of software and its further improvement. It is not a replacement for human intelligence but it eliminates the error that humans might make and does the task with complete accuracy and precision.

With the mankind being largely dependent on artificial intelligence, here is a list of AI platforms that are pulling the strings in the industry

Google Cloud AI Platform:

This platforms allows developers to effortlessly manufacture artificial intelligence models. It incorporates Google Cloud Dataflow. It allows pre-handling and permits the users to get data from Google Cloud Storage, Google BigQuery etc.

Tasks like NLP, Speech, Vision Capabilities and deep learning are carried out through the help of Google Cloud AI platform. These incorporate-

Machine Learning

By providing a toolchain, the platform lets developers without much difficulty create AI models and assist in the procedure of development. 

Deep Learning

Deep learning applications are developed with ease due to pre-configured Virtual Machines (VMs) offered by this platform. Deep Learning containers also provide flexibility by being compatible with TenserFlow, PyTorch.

Natural Language Processing (NLP)

Google provides Natural Language Processing capacities to the developers so as to discover the composition and significance of a text. It is of utmost importance to break down content with Google NLP API that is accessible via RESTful API.

Vision

Vision is also a part of Google Cloud AI platform. It gives users the oppotutnity to detect images and get astute bits of knowledge from them. For this purpose, RPC and REST APIs are used and their integration with ML models helps identifying items, faces and texts.

Speech

In order to convert speech to text and text to speech, Google Speech API is used. It utilizes neural network models. Speech to text API bolsters 120 languages.

All thanks to voice recognition, it lets developers and engineers empower their applications and software with voice command features. Speech is very beneficial when it comes to tasks like transforming texts to audio for example in mp3 format.

Microsoft Azure AI Platform:

By providing developers with AI competence to do speech recognition, Machine learning (ML), object recognition, machine translation and Knowledge mining; Microsoft Azure AI Platform has established itself as one of the most popular platforms worldwide. Tools like Bot Framework, Cognitive Services, Azure Machine Learning to create new exposure for the users.

It also incorporates Python based ML service known as Azure Databricks which amalgamates ONNX and Azure ML.

IBM Watson:

IBM Watson is an open AI that has made performing in several fields like financial services, Internet of Things (IoT), media, healthcare, oil & gas sector and advertising possible and an advanced mean.

It lets engineers speeding up the turn of events and enlargement of AI application models.

Apparatuses like SDKs are provided by IBM Watson. Watson Assistant can also be used to emulate a conversation between a user and the machine.

IBM watson is capable of converting speech to text through Natural Language Processing (NLP) which is also known as Watson Natural Language Understanding (NLU). Thus, software makers are greatly benefitted by this.

IBM Watson also offers its developers SDKs for Java, Python, Swift, Ruby,

and Node.js which empowers the engineers and developers to locate an appropriate SDK for their tasks.

It is used widely in the medical field as it lets doctors read Xray and MRI scans precisely.

BigML:

As the name suggests, BigML provides users with ML calculations. Most notable programming languages like Java, Python, Ruby, Swift, Node.js etc as per the requirement of the task and the developers.

Features like loading of data, an expansive room of no cost models to utilise and flexible, elastic pricing make BigML a widely used Artificial intelligence platform throughout the world.

Infosys Nia:

With its advanced technology, Infosys Nia permits clients to manufacture custom encounters to match the industrial requirement and models. It includes features like–

Machine Learning

BigML offers a massively expansive scope of Machine Learning calculations which speeds the very process and makes it uncomplicated for the developers to create software.

Nia Chatbot

Assembling Artificial intelligence based chatbots helps the developers enable technologies to converse with human beings and complete actions that they are told to do. 

Contracts Analysis

By incorporating Deep Learning, ML, and semantic modelling provides developers with a wide scope for contracts analysis 

Data nia

Data Nia is a data driven tool which helps a firm receive insights and therefore forecast the plans.

The post Must try Artificial Intelligence Platforms appeared first on Artificial Intelligence.

For those unaware, Artificial Intelligence alludes the re-enactment of human insight into machine so as to enable them to think like members of the human race. Thus, attributes like problem solving, learning and critical thinking are carried on by machines.

Artificial intelligence brings along a colossal potential to the table which is ultimately sculpturing the fate of technology in future.

Thus, its no surprise that business industry is investing more and more in this platform that holds the promise of changing the world as we know it. Thus as per estimates, given the investments that artificial intelligence is witnessing, it will easily cross the value of trillions of dollar in the future.

AI also houses gigantic potential when it comes to development of software and its further improvement. It is not a replacement for human intelligence but it eliminates the error that humans might make and does the task with complete accuracy and precision.

With the mankind being largely dependent on artificial intelligence, here is a list of AI platforms that are pulling the strings in the industry

Google Cloud AI Platform:

This platforms allows developers to effortlessly manufacture artificial intelligence models. It incorporates Google Cloud Dataflow. It allows pre-handling and permits the users to get data from Google Cloud Storage, Google BigQuery etc.

Tasks like NLP, Speech, Vision Capabilities and deep learning are carried out through the help of Google Cloud AI platform. These incorporate-

Machine Learning

By providing a toolchain, the platform lets developers without much difficulty create AI models and assist in the procedure of development. 

Deep Learning

Deep learning applications are developed with ease due to pre-configured Virtual Machines (VMs) offered by this platform. Deep Learning containers also provide flexibility by being compatible with TenserFlow, PyTorch.

Natural Language Processing (NLP)

Google provides Natural Language Processing capacities to the developers so as to discover the composition and significance of a text. It is of utmost importance to break down content with Google NLP API that is accessible via RESTful API.

Vision

Vision is also a part of Google Cloud AI platform. It gives users the oppotutnity to detect images and get astute bits of knowledge from them. For this purpose, RPC and REST APIs are used and their integration with ML models helps identifying items, faces and texts.

Speech

In order to convert speech to text and text to speech, Google Speech API is used. It utilizes neural network models. Speech to text API bolsters 120 languages.

All thanks to voice recognition, it lets developers and engineers empower their applications and software with voice command features. Speech is very beneficial when it comes to tasks like transforming texts to audio for example in mp3 format.

Microsoft Azure AI Platform:

By providing developers with AI competence to do speech recognition, Machine learning (ML), object recognition, machine translation and Knowledge mining; Microsoft Azure AI Platform has established itself as one of the most popular platforms worldwide. Tools like Bot Framework, Cognitive Services, Azure Machine Learning to create new exposure for the users.

It also incorporates Python based ML service known as Azure Databricks which amalgamates ONNX and Azure ML.

IBM Watson:

IBM Watson is an open AI that has made performing in several fields like financial services, Internet of Things (IoT), media, healthcare, oil & gas sector and advertising possible and an advanced mean.

It lets engineers speeding up the turn of events and enlargement of AI application models.

Apparatuses like SDKs are provided by IBM Watson. Watson Assistant can also be used to emulate a conversation between a user and the machine.

IBM watson is capable of converting speech to text through Natural Language Processing (NLP) which is also known as Watson Natural Language Understanding (NLU). Thus, software makers are greatly benefitted by this.

IBM Watson also offers its developers SDKs for Java, Python, Swift, Ruby,

and Node.js which empowers the engineers and developers to locate an appropriate SDK for their tasks.

It is used widely in the medical field as it lets doctors read Xray and MRI scans precisely.

BigML:

As the name suggests, BigML provides users with ML calculations. Most notable programming languages like Java, Python, Ruby, Swift, Node.js etc as per the requirement of the task and the developers.

Features like loading of data, an expansive room of no cost models to utilise and flexible, elastic pricing make BigML a widely used Artificial intelligence platform throughout the world.

Infosys Nia:

With its advanced technology, Infosys Nia permits clients to manufacture custom encounters to match the industrial requirement and models. It includes features like–

Machine Learning

BigML offers a massively expansive scope of Machine Learning calculations which speeds the very process and makes it uncomplicated for the developers to create software.

Nia Chatbot

Assembling Artificial intelligence based chatbots helps the developers enable technologies to converse with human beings and complete actions that they are told to do. 

Contracts Analysis

By incorporating Deep Learning, ML, and semantic modelling provides developers with a wide scope for contracts analysis 

Data nia

Data Nia is a data driven tool which helps a firm receive insights and therefore forecast the plans.

The post Best of Artificial Intelligence Platforms in the world appeared first on Artificial Intelligence.

Machine Learning has been serving several industries for the past many years. It has enabled businesses to work easily with data. Moreover, the acceleration in the adoption of ML tools has evolved with time making it even easier to use today. Using AutoML tools, the act of gathering data and turning it into actionable insights has become much convenient. People with even less knowledge of data science and machine learning can work with these automated tools.

DataRobot

In 2013, DataRobot invented automated machine learning — and an entirely new category of software as a result. Unlike other tools that provide limited automation for the complex journey from raw data to return on investment, the company’s Automated Machine Learning product supports all of the steps needed to prepare, build, deploy, monitor, and maintain powerful AI applications at enterprise scale.

DataRobot’s AutoML product accelerates the productivity of your data science team while increasing your capacity for AI by empowering existing analysts to become citizen data scientists. This enables your organization to open the floodgates to innovation and start your intelligence revolution today.

Google Cloud AutoML

Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology.

Cloud AutoML leverages more than 10 years of proprietary Google Research technology to help your machine learning models achieve faster performance and more accurate predictions.

dotData

dotData was born out of the radical idea, unique among machine learning companies, that the data science process could be made simple enough for just about anyone to benefit from it. Led by Dr. Ryohei Fujimaki, a world-renowned data scientist, and the youngest research fellow ever appointed in the 119-year history of NEC, dotData was created to accomplish this mission. The company values its clients and works hard to provide the highest value possible in Automated Machine Learning (AutoML).

dotData was first among machine learning companies to deliver full-cycle data science automation for the enterprise. Its data science automation platform speeds time to value by accelerating, democratizing, and operationalizing the entire data science process through automation.

Splunk

Splunk’s original version started off as a tool for searching through the voluminous log files created by modern web applications. Since then it has grown to analyze all forms of data, especially time-series and others produced in sequence. The latest newest versions of Splunk includes apps that integrate the data sources with machine learning tools like TensorFlow and some of the best Python open-source tools. Such modern tools offer quick solutions for detecting outliers, flagging anomalies, and generating predictions for future values.

H2O

H2O has made it easy for non-experts to experiment with machine learning. In order for machine learning software to truly be accessible to non-experts, the company has designed an easy-to-use interface that automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-pre-processing, feature engineering and model deployment. It can be employed for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.

The post TOP AUTOML PLATFORMS TO LOOK OUT FOR IN 2020 appeared first on Artificial Intelligence.

Data is not only “big,” it’s also unruly. It populates every pocket of the enterprise. Every information system, every cloud, is dripping with it. And not unlike Jed Clampett’s “bubbling crude (oil that is),” it takes a lot of machinery, a lot of refining, to make it useful.

It took the 2010s to build out the infrastructure of big data, the constellation of platforms and applications that store, tag, govern, manage and deliver it. In that regard, I see the past decade as a test lab with the focus on developing, implementing and integrating a large swath of heterogenous solutions to streamline turning data into real business intelligence. 

That period of testing has paid off. In 2020, we’ve not only turned the page on a new decade, we’ve turned the corner on making data the true currency of value creation. And, the good news for every enterprise — whether large or small, brimming with IT staff or manned (or womanned) by a hearty few — is that turning data into insights at speed and scale is available now for everyone. (And it doesn’t cost a lot or take years to implement.)

The rise of DataOps

Two complementary developments have delivered this transformation. One is the evolution of an Agile mindset, a framework for approaching, implementing, and demanding more from data management solutions, called DataOps.

The other development is a series of technological breakthroughs, perhaps not obvious amid the sheer volume of data management solutions in the market, that make end-to-end data management not only possible, but safer, faster and more useful than ever before. 

First up is DataOps, which Gartner calls “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.” 

I can say from firsthand experience that the rise of DataOps, modeled after the success of DevOps (an Agile engineering framework for enterprise IT that streamlined application development, deployment and continuous improvement), is the result of reorienting data management around value creation. It’s a get-to-value-first, get-to-value-fast philosophy that enables the enterprise to either fail or succeed quickly and rapidly build on what works.

A lot is riding on this shift. In Getting DataOps Right, O’Reilly’s authors summarized: 

“The necessity of DataOps has emerged as individuals in large traditional enterprises realize that they should be using all the data generated in their company as a strategic asset to make better decisions every day.” They concluded, “Just like the internet companies needed DevOps to provide a high-quality, consistent framework for feature development, enterprises need a high-quality, consistent framework for rapid data engineering and analytic development.”

For the purposes of this article, suffice it to say that a DataOps mentality, one that emphasizes cross-functional collaboration in data management, learning by doing, rapid deployment and building on what works, is beginning to sweep the enterprise, and early adopters have strong results to show for it.

While the rise of DataOps may prove to be the tip of the spear in 2020’s data management, the heft behind it, which is making it so effective, is a new generation of great technology.

DataOps technology drivers

Without the best tools, great teams can only go so far. We now have at our disposal a new generation of platforms and applications that 1) make all the data management solutions amassed by the enterprise over the last decade work better together and 2) offer a quick-to- implement, low-cost alternative for smaller enterprises looking to play and win the data management game at scale.

Here are three key technology drivers enabling DataOps excellence and the pursuit and attainments of rapid time-to-value:

Extensible platforms. Enterprise data can live anywhere — on premises, in the cloud and, as is often the case, among multiple clouds. For many enterprises, this data sprawl across siloed systems has seemed insurmountable. However, it’s not. Extensible platforms, which can easily pull data from myriad sources and align them in a metadata catalog, solve sprawl without requiring the building of a data lake. A win for agility, internet-native technology and for all enterprise users.

Augmented data catalog. Just as an extensible platform enables companies to leverage data regardless of where it resides, next-generation metadata catalogs, where data is easily accessed, tagged, annotated, enriched and shared, allows companies to orchestrate their current data management systems and turbocharge their performance. As Gartner urges companies to evolve from “storage-centric” to “streaming-centric” data management solutions to speed time to value, metadata catalogs, which greatly reduce administrative costs through machine learning, hold the key. Consider this the new cockpit for end-to-end data visibility and management. 

Self-service. Drawing on the advances of extensible platforms and augmented metadata catalogs, today’s data management systems provide a breakthrough capability once only dreamed of by most enterprises — true self-service data provisioning. In the past, analysts might have to wait weeks or months to have IT find, pull, and perform jujitsu on required data sets to empower better decision making. Now, analysts, data scientists and business intelligence users can “shop” for the data they need at data marts, sparing valuable IT resources. As an added bonus, that data will be pre-commissioned, quality-checked, tokenized and enriched by DataOps collaborative efforts through the platform. Today, through self-service, the Amazonification of data is upon us, and everyone is invited.

How to succeed with DataOps

The best part of these developments is that they are happening now. I am not describing a “future state.” Peer companies are taking advantage of these tools today as part of successful DataOps initiatives that are delivering business value as we speak, more quickly and at less cost than ever before.

One such success, recognizable to any business with loyalty rewards programs, is occurring at a regional financial services company. Leveraging an extensible data management platform, the firm integrated data that had been siloed across five lines of business and third-party sources to create a 360-degree view or “golden record” of its customers. It then made that data available to users through a metadata catalog, providing self-service access to its data scientists that resulted in eight hours per day of saved data engineering work and the ultimate prize of increased revenues through personalized, golden-record-driven sales.

Oh, and the firm started seeing great results in less than six months.

Democratizing data gains with DataOps

Data, measured in petabytes across the enterprise, the cloud and third-party sources, has the potential to be one of every company’s most valuable resources. The Agile approach of DataOps, turbocharged by a powerful new generation of data management platforms and tools, is flipping the script on who gets to benefit most from data. 

Big, small or anywhere in between, enterprises today have at their disposal the methodology and technology needed to tap data at scale, turn on their data pipelines and deliver their people and businesses a game-changing intelligence advantage.

The post Using DataOps to create business value from big data appeared first on Artificial Intelligence.

Various organizations keep floating data science platforms to simplify machine learning workflows. However, in the ever-changing data science landscape, only a few draw the attention of practitioners. Besides, due to fierce competition in the market, oftentimes platforms keep replacing one another as and when it brings new capabilities to improve organizations’ productivity. 

Here are the top 7 data science platforms that are widely adopted by organizations in 2020:-

Databricks

Built by the founder of Apache Spark, Databricks provides a unified analytics platform that allows data scientists to manage end-to-end machine learning workflows. The one-size-fits-all platform not only enables practitioners to explore, visualize and build superior machine learning models, but also allows them to scale it quickly with the help of collaboration.

The platforms support a wide range of languages, IDEs and notebooks. Data scientists do not have to worry about adopting new technologies as it integrates with different popular platforms like Alteryx, Azure, DataRobot, AWS SageMaker, and Dataiku. Such capabilities have helped the platform to gain a place in Gartner’s magic chart for data science as a leader.

DataRobot

DataRobot is a unicorn in data science that helps companies automate the workflows of machine learning through its feature-rich solutions. The company continuously strives to enhance its platform by either acquiring various companies, or by developing in-house solutions.

Apart from assisting the regular analytics workflows, DataRobot is among the best in the AutoML space. More recently, it equipped the platform with Visual AI to simplify the incorporation of image data into ML models alongside tabular and text-based data types.

Apache Spark

Apache Spark is an open-source unified analytics engine for large-scale data processing and analyzing. It is similar to Hadoop MapReduce; it works on cluster computing, but due to exceptional speed – which is believed to be 100x faster in memory and 10x faster on disk than Hadoop – it has become popular among data scientists.

Launched in 2009, Apache Spark has emerged as a big data platform due to its superior performance. In the last ten years, the platform has been evolving by integrating with other tools to ensure better user experience.

Dataiku

This is another famous enterprise AI and machine learning platform that helps businesses in minimizing data processes to expedite the development of machine learning-based solutions. With Dataiku, companies can bring data analysts, engineers, and scientists together to achieve shared goals through collaboration. It also provides instant visual and statistical feedback on model performance to manage models’ lifecycle effectively.

IBM Cloud Pak for Data

Built on Red Hat OpneShift container platform, IBM Cloud Pak for Data is a fully-integrated AI platform to meet the changing needs of enterprises. It allows data scientists to unlock insights and eliminate data silos quickly. The platform has a high degree of enterprise readiness and delivers business value by enabling practitioners to integrate with other platforms using APIs. Besides, it also empowers data scientists to accelerate their development and deployment in containerized environments to improve the flexibility of AI-based solutions.

Alteryx

Alteryx is a self-service analytics platform that can be utilized across organizations to democratize data. The platform caters to every need of analytics professionals, such as business intelligence, data analyst, data scientist, and non-experts to assist them in quickly solving business problems. It supports analytics modelling without code and advanced modelling with algorithms.

TIBCO Software

TIBCO Software acts as a foundation for digital innovation for data-driven companies, thereby gaining a place in Gartner’s magic quadrant for 2020 as a leader. Integration among platforms has been one of the longest standing predicament for organizations. Thus, TIBCO offers a suite of products like Connect, API-Led Integration, Data Fabric, Unify, Data Science & Streaming, and more, to eliminate challenges for a streamlined data science workflow.

The post TOP 7 WIDELY USED DATA SCIENCE PLATFORMS appeared first on Artificial Intelligence.

A decade ago, when everyone was still wrapping their heads around the idea of cloud computing, one ongoing point of concern was the fear of vendor lock-in. The idea was that if you relied on a single company to provide all of your cloud services from top to bottom, you would be locked in and lack the flexibility to try new services from other vendors until your contract expired — even if another company offered a better product. If you wanted to add other services, you’d be on the hook for substantial expenses to migrate.

Eventually, the tech industry listened and learned that customers don’t want a one-size-fits-all approach; they want the ability to pick and choose different pieces and services depending on their specific needs. Unfortunately, some e-commerce platform vendors didn’t get the memo because they’re still operating as monoliths.

While some e-commerce platforms have learned the value of flexibility, for others, the pendulum has swung in the opposite direction in favor of microservices, an architecture composed of loosely coupled services connected through APIs to create a complete system. Think of microservices like Lego blocks without an instruction manual to piece them together to build that creation you had perfectly envisioned.

A microservices approach can be tempting for online retailers for several reasons. First, commerce is becoming increasingly focused on content and customer experience. By decoupling the front end (the part customers interact with) from the back end (the part that enables transactions and powers the store), developers can customize and manage the two sides separately.

Second, merchants can have multiple front-end experiences connected to one back end, creating opportunities to implement several new customer-facing touch points (e.g., a blog, a traditional website or even a unique app). Third, developers have more flexibility to adjust code on the back end without affecting the part customers interact with. They also have choices for different services to power the site instead of using only what the monolith vendor offers.

While microservices offer some significant advantages over monoliths, that approach has flaws of its own. If you’re an online retailer, you need to think hard about whether you want a monolith or a pure microservice system. The monoliths lock you in, making it difficult to customize your experience without a team of expert developers and a lot of costs.

Microservices, while providing choice and flexibility, put more responsibility for security and payment card industry (PCI) compliance in your hands, as well as leave you to manage uptime and support. Managing the various microservices requires cross-functional, vertical teams that must work together to maintain the site. It can be extremely difficult — and potentially expensive — to put all of those Lego pieces together in a way that creates a compelling and manageable customer experience.

One emerging way to build an open system that prioritizes the customer experience is headless commerce, which provides a safe, secure platform while also enabling simple, fast integrations with other applications to provide the service merchants need and customers want. For experience-focused brands (e.g., lifestyle products), direct-to-consumer brands and companies that rely heavily on influencers, native advertising or other content, headless commerce has its advantages.

Headless gives retailers the flexibility to mix a content-focused customer experience with a safe and secure commerce back end. Many brands today are using content to create unique, personalized and exciting experiences for customers. With headless, they can take advantage of the flexibility of content platforms like WordPress or Drupal without sacrificing security behind the scenes.

While microservices require various teams to manage the different pieces, headless doesn’t require specialized teams. That said, headless does bring together two separate tools and, therefore, requires owners to manage two tools instead of one. However, everything connects through the core e-commerce platform, making it easier to manage. Headless also provides flexibility for developers by allowing them to work in the system they are familiar with. Still, some businesses might not want to do that even with the benefit of creating a more experience-driven site.

Microservices can also require changes to the infrastructure and tools used to monitor them, and those changes will likely add to the total cost of the system. Headless, on the other hand, has the benefit of providing flexibility with fewer changes to existing infrastructure.

If you’ve been using a monolith, it might be intimidating to consider a full replatform, but another benefit of switching to headless is that it can be done incrementally. It doesn’t require big, instant change that would disrupt operations. You can decide which capabilities you want to decouple first, and then transition over time while building out other parts.

The biggest challenge to making the switch to headless is that it requires a shift from a product-first mindset to a content-first mindset. That can be tough for many merchants. In addition to managing inventory, production and all the pieces needed to get those products to customers, headless requires time to develop dynamic content and a commitment to keeping things fresh so it feels current for the audience.

Twenty years ago, monoliths or fully custom builds were the only options for e-commerce and other technology systems, but market demand for choice, flexibility and innovation has taken hold. For e-commerce brands, an open API-driven approach is a powerful way to provide positive customer experiences and distinguish themselves from the competition. And in today’s highly competitive online retail environment, everyone could use more ways to set themselves apart from the crowd.

The post E-Commerce Monoliths Or Microservices? The Answer May Be In The Middle appeared first on Artificial Intelligence.

Data Science has demonstrated to be a boom to both the IT and the business. The innovation incorporates getting value from information, understanding the information and its examples and afterwards foreseeing or producing results from it. Data science is much popular by organizations to analyze their enormous volume of data sets and generate optimized business insights from them, in this manner expanding profits for the organization.

Picking the correct seller and solution can be an entangled procedure, one that requires in-depth research and regularly boils down to something other than the solution and its technical abilities. To make your hunt somewhat simpler, we’ve profiled the best data science platforms and tools.

Altair

Altair Knowledge Works (some time ago Datawatch) offers an advanced data mining and predictive analytics workbench called Knowledge Studio. The product includes licensed Decision Trees, Strategy Trees, and a work process and wizard-driven graphical UI. It additionally incorporates capacities for data preparation tasks, visual data profiling, advanced predictive modeling, and in-database analytics. Users can import and export using common languages like R and Python, as well as data types like SAS, RDBMS, CSV, Excel, and SPSS.

Mozenda

Mozenda is an enterprise cloud-based web-scraping platform. It assists organizations with gathering and sorting out web information most productively and cost-effectively possible. The tools have a point-to-click interface with an easy to understand UI. The tools have two sections: an application to construct the data extraction project and Web Console to run agents, organize results, and export data. It is anything but difficult to incorporate and permits users to distribute results in CSV, TSV, XML, or JSON format. The tools additionally give API access to get information and have inbuilt storage integrations like FTP, Amazon S3, Dropbox, and much more.

Anaconda

Anaconda is an open-source Python and R data science platform. The tool empowers you to perform data science and machine learning on Linux, Windows, and Mac OS. The platform permits users to download in excess of 1,500 Python and R data science packages, oversee libraries, dependencies, and environments, and analyze data with Dask, NumPy, pandas, and Numba. You would then be able to imagine results produced in Anaconda with Matplotlib, Bokeh, Datashader, and Holoviews.

Octoparse

Octoparse is a customer- side web scraping programming for Windows. It is a web-scraping template that transforms unstructured or semi-structured information from sites into an organized data set without coding. It is helpful for individuals who are not knowledgeable about programming. A web scraping layout is a simple yet amazing element. Its motivation is to input the target website/keywords in the parameters on the pre-formatted tasks, so the user doesn’t need to design any scraping rules nor composing code.

Databricks

Databricks offers a cloud and Apache Spark-based brought together analytics platform that joins data engineering and data science functionality. The platform uses a variety of open source languages and incorporates exclusive highlights for operationalization, performance and real-time enablement on Amazon Web Services. A Data Science Workspace empowers users to explore data and build models collaboratively. It additionally gives single click access to preconfigured ML conditions for augmented machine learning with popular frameworks.

OnBase

OnBase is a tool created by Hyland, is a single enterprise information platform that is intended to deal with user’s content, procedures, and cases. The tool essentially brings together user’s business content in a protected area and afterwards conveys important data to the user when they need it. OnBase permits the enterprise to turn out to be progressively agile, efficient, and capable, subsequently increasing productivity, delivering excellent customer service, and reduce risk across their enterprise.

KNIME Analytics Platform

KNIME makes understanding the information and designing data science workflows and reusable components available to everybody by being natural, open, and ceaselessly integrating new developments. KNIME permits the user to browse 2000 nodes to build workflow, model each step of the analysis, control the flow of data, and ensures the work is updated. The product likewise mixes tools from various areas with KNIME native nodes within a single workflow, incorporating scripting in machine learning, Python or R, or connectors to Apache Spark.

Dataiku

Dataiku offers an advanced analytics solution that permits companies to make their own data tools. The organization’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified structure for advancement and deployment gives prompt access to all the features expected to plan data tools without any preparation. Users would then be able to apply machine learning and data science systems to build and deploy predictive data flows.

Rapid Miner

Fast Miner is a data science platform developed fundamentally for non-programmers and analysts for quick analysis of information. The user has a thought in their brain, and effectively makes processes, import data into them, run them over and throw a prediction model. The tool supports importing ML models as well as to web applications like flask or nodeJS, android, iOS, and more, thereby unifying the entire spectrum of the Big Data Analytics Lifecycle.

DataRobot

DataRobot offers an enterprise AI platform that automates the end-to-end process for building, deploying, and maintaining AI. The product is controlled by open-source algorithms and can be utilized on-prem, in the cloud or as a completely overseen AI service. DataRobot incorporates three independent yet fully integrated tools (Automated Machine Learning, Automated Time Series, MLOps), and each can be deployed in different manners to coordinate business needs and IT necessities.

The post TOP 10 DATA SCIENCE PLATFORMS AND TOOLS OF 2020 appeared first on Artificial Intelligence.