Information
Information. Any engineer who has taken the primary steps of gaining data of to work with AI strategies has confronted the foremost mission of the house: sourcing enough excessive nice information to make a process possible. sample items of knowledge might be had, of course, nonetheless working with these shouldn’t be tons amusing for a similar cause that fixing a machine bother for laptop computer science magnificence shouldn’t be tons fun: fairly certainly, itâs not actual.
In reality, the utilization of fake statistics is extraordinarily anathema to the spirit of independently rising software program program: we do it because of the reality fixing precise points, even if theyâre trivial or just our very personal, is fairly nice.
using the occasion dataset from AWS permits a developer to grasp how Amazonâs system gaining data of API works, which is the issue, after all, however most engineers won’t dig too deeply into the troubles and techniques right here, as a result of itâs not thrilling to maintain grinding on some factor itâs been solved by means of tons of of individuals earlier than and to which the engineer has no stake.
So the true problem for an engineer then will develop into: how and by which to get information â enough of it â to hone oneâs AI capabilities and to construct the favored model?
âwhile on the prowl for the newest AI tendencies, it might be useful to grasp that statistics comes first, not the alternative method spherical,â says Michael Hiskey, the CMO of Semarchy, which makes data management software program.
this major hurdle, by which to get the data, tends to be the utmost bedeviling. For people who donât private an software it truly is throwing off deep troves of knowledge, or who donât have access to a historical base of data upon which to assemble a mannequin, the mission might be daunting.
most notable ideas throughout the AI space die correct right here, because of the reality could-be founders end that the information doesnât exist, that getting itâs miles too troublesome, or that what little of it that does exist is simply too corrupted to use for AI.
Climbing over this process, however, is what separates rising AI startups from of us that merely communicate roughly doing it. Proper listed here are a number of tips that could make it occur:
The highlights (further data underneath):
Multiply the electrical energy of your data
increase your information with those which might be comparable
Scrape it
look to the burgeoning TDaaS house
Leverage your tax bucks and tap the authorities
look to open-sourced data repositories
make the most of surveys and crowdsourcing
kind partnerships with business stalwarts who’re wealthy in information
construct a helpful software, provide it away, use the data
Multiply the vitality of your data
a few of these issues could also be solved by means of simple instinct. If a developer seeks to make a deep learning mannequin with a view to grasp snap pictures that comprise the face of William Shatner, sufficient images of the well-known particular person Trek legend and Priceline pitchman could also be scraped from the web â together with even higher random pictures that don’t include him (the model would require every, of path).
past tinkering with data already in hand, however, statistics seekers wish to get progressive.
For AI fashions being educated to understand puppies and cats, one picture can effectively be became 4:One picture of a canine and cat might be circled into many.
increase your statistics with those that are comparable
Brennan White, the CEO of Cortex, which permits formulate organizations content material materials and social media plans through AI, situated a sensible reply while arising fast on information.
âFor our shoppers looking at their very personal statistics, the amount of information is rarely enough to clear up the effort weâre centered on,â he says.
White solved the issue through the use of sampling social media statistics of his clientsâ closest competitors. including that information to the set accelerated the sample through the use of sufficient multiples to offer him a vital mass with which to construct an AI mannequin.
Scrape it
Scraping is how packages get constructed. itâs how half the web got here to be. weâre going to insert the canned warning proper right here about violating web sitesâ phrases of service by means of crawling their web sites with scripts and recording what you may discover â many web sites frown on this, however not all of them.
Assuming founders are working above-board proper right here, there exists almost infinite roads of knowledge that may be pushed with assistance from constructing code which may transfer slowly and parse the online. The smarter the crawler, the upper the data.
that is how quite a few functions and datasets get started. For these scared of scraping errors or being blocked by cloud servers or ISPs that see what youâre as much as, there are human-primarily based mostly options. additional to Amazonâs Mechanical Turk, which it playfully refers to as âsynthetic synthetic Intelligence,â there exist a bevy of choices: Upwork, Fiverr, Freelancer.com, Elance. there could also be moreover a comparable sort of platform, aimed directly at statistics, dubbed 24x7offshoring â which we level out subsequent.
look to the burgeoning 24x7offshoring space
past all of this, there are literally startups that assist companies, or completely different startups, resolve the information bother. The clunky acronym that has sprouted up round these save is 24x7offshoring â schooling knowledge as a service. teams like this provide startups get right of entry to to a labor drive it actually is expert and able to help in gathering, cleansing and labeling knowledge, all part of the vital path to developing a
schooling data as a supplier (24x7offshoring): There are few startups like 24x7offshoring and Mty.ai, which offer schooling statistics all through domains starting from visible statistics (photos, movies for object recognition and so on) to textual content data (used for natural language approach obligations).
contemplate this method as much like using Amazonâs Mechanical Turk, with quite a lot of the precise AI-associated directions and requirements abstracted away. by means of these channels, thereâs additionally a lot much less of a burden on the startup to vet workers and dig through completed jobs to sort for excellent. thatâs what the buildings do for founders.
Leverage your tax bucks and tap the federal government
it is ready to be useful for many folks to look first to governments, federal and state, for information on given subjects, as public our our bodies make an rising variety of of their knowledge troves accessible to be downloaded in helpful codecs. The open information motion inside authorities is precise, and it has a web website â a notable neighborhood to start for engineers making an attempt to get a problem started: data.gov.
Open-supply knowledge repositories
As machine attending to know strategies develop to be extra customary, the infrastructure and teams that help them have grown up as properly. part of that environment contains publicly reachable shops of knowledge that cowl a mess of subjects and disciplines.
24x7offshoring the COO and co-founding father of 24x7offshoring, which makes use of AI to assist prevent retail returns, advises founders to look to these repos sooner than developing a scraper or strolling in circles in search of to scare up data from sources which are much less more likely to be cooperative. there may be an rising set of topics on which data is available through these repos.
a number of repos to check out:
school of California, Irvine
statistics science major
free datasets on 24x7offshoring
make use of surveys and crowdsourcing
Stuart Watt, the CTO of 24x7offshoring, which makes use of AI to assist organizations introduce higher empathy into their communications, has had achievement with crowdsourcing statistics. He notes that it’s critical to be specified and particular in instructions to customers and people who could be sourcing the data. a number of customers, he notes, will try to hurry by means of the required obligations and surveys, clicking merrily away. nonetheless almost all of these instances might be noticed by way of instituting a number of exams for tempo and variance, Watt says, discarding results that don’t fall inside the traditional levels.
Andrew Hearst, a unified search engineer at Bloomberg, additionally thinks that crowdsourced data might be fairly useful and fairly priced â as long as there are controls for positive. He recommends continuously testing the distinctive of responses.
Respondentsâ targets in crowdsourced surveys are easy: full as many devices as viable within the shortest time-frame if you wish to make money. however, this doesnât align with the purpose of the engineer who’s working to get loads of exact data. To make sure that respondents present correct knowledge, Hearst says, they should first bypass a take a look at that mimics the true task. For people who do bypass, further examine questions have to be randomly given all through the enterprise, unbeknownst to them, for positive guarantee.
âlastly respondents be taught which units are exams and which ones will not be, so engineers will wish to constantly create new take a look at questions,â Hearst provides.
kind partnerships with enterprise stalwarts who’re rich in statistics
For startups searching for statistics in a particular topic or market, it may be helpful to kind partnerships with the enterpriseâs core locations to get related information. Forming partnerships will worth startups valuable time, after all, nonetheless the proprietary statistics gained will assemble a natural barrier to any opponents seeking to do comparable issues, components out Ashlesh Sharma, who holds a PhD in computer imaginative and prescient and is co-founder and CTO of Entrupy, which makes use of machine studying to authenticate excessive-give up luxurious merchandise (like Hermès and Louis Vuitton purses).
methods for data assortment for AI
Use open provide datasets
There are quite a few sources of open provide datasets that could be used to teach machine attending to know algorithms, consisting of Kaggle, data.
Gov and others. these datasets provide you with transient get right of entry to to huge volumes of information that will assist to get your AI duties off the ground. however at the same time as these datasets can save time and cut back the value involved with customized data collections, there are various factors to donât overlook. First is relevancy; it is advisable to make sure that the dataset has enough examples of statistics that’s relevant in your distinctive use case.
2nd is reliability; data how the data was collected and any bias it’d include may be very essential whereas figuring out whether or not it must be utilized in your AI enterprise. subsequently, the safety and privateness of the dataset should even be evaluated; make sure to carry out due diligence in sourcing datasets from a Third-birthday get together vendor that makes use of strong security options and exhibits compliance with data privateness suggestions along with GDPR and the California purchaser privateness Act.
Generate synthetic data
versus gathering actual-global information, teams can use an artificial dataset, which relies upon an authentic dataset, nonetheless then elaborated upon. artificial datasets are designed to have the equal traits as a result of the unique, with out the inconsistencies (although the power lack of probabilistic outliers may trigger datasets that donât seize all the nature of the issue youâre seeking to resolve). For corporations going by means of strict security, privateness and retention suggestions, which incorporates healthcare/pharma, telco and financial choices, artificial datasets generally is a wonderful route towards growing your AI expertise.
Export information from one algorithm to some other
in some other case known as change attending to know, this strategy of gathering statistics includes utilizing a pre-present algorithm as the muse for coaching a brand new algorithm. There are clear benefits to this technique in that it may well store money and time, nonetheless itâs going to best work while advancing from a widespread algorithm or operational context, to 1 whichâs higher exact in nature. common situations by which change mastering is used include: natural language processing that makes use of written textual content, and predictive modeling that makes use of each video or nonetheless pictures.
Many picture management apps, for instance, use switch attending to know as a method of making filters for friends and family members, to be able to speedy uncover all of the pics an individual seems in.
accumulate major/customized data
generally the standard basis for coaching a ML algorithm includes gathering uncooked data from the sphere that meets your particular necessities. Loosely outlined, this can include scraping data from the web, nonetheless it could cross thus far as rising a bespoke program for capturing snap pictures or different information throughout the self-discipline. And relying on the type of statistics wanted, you may each crowdsource the gathering process, or work with a professional engineer who is aware of the ins and outs of amassing easy data (thus minimizing the quantity of put up-series processing).
sorts of knowledge which are gathered can vary from video and nonetheless imagery to audio, human gestures, handwriting, speech, or textual content utterances. investing in a customized data assortment to generate knowledge that nice matches your use case can take higher time than utilizing an open supply dataset, however the advantages by way of accuracy, reliability, privateness and bias discount make this a worthwhile funding.
whatever the nation of AI maturity of your organisation, sourcing exterior education data is a reliable various, and these information assortment methods and techniques can assist enlarge your AI coaching datasets to fulfill your wants. even so, itâs essential that outdoors and inside sources of education information match inside an overarching AI technique. rising this strategy will provide you with a clearer picture of the information you’ve on-hand, assist to highlight gaps to your information that may lavatory down your business enterprise, and select how it is advisable to accumulate and handle data to maintain your AI enchancment on monitor.
what’s AI & ML coaching data?
AI & ML coaching data is used to coach synthetic intelligence and gadget gaining data of fashions. It consists of labeled examples or input-output pairs that allow algorithms to be taught types and make appropriate predictions or selections. This information is essential for teaching AI techniques to grasp types, apprehend language, classify pix, or perform different duties. coaching statistics could also be gathered, curated, and annotated through human beings or generated by means of simulations, and it performs a important function within the enchancment and efficiency of AI and ML fashions.
The operate of data has emerge as paramount for digitally transforming organisations. whether or not itâs advertising or AI information assortment, companies have find yourself more and more reliant on correct statistics assortment to make knowledgeable alternatives; it’s important to have a transparent technique in location.
With the rising curiosity in information assortment, weâve curated this article to discover data collection and the best way business enterprise leaders can get this important system proper.
whatâs data collection?
actually positioned, statistics assortment is the tactic by which companies accumulate data to analyze, interpret, and act upon. It entails numerous data assortment methods, system, and techniques, all designed to make sure information relevance.
significance of statistics collection
having access to
data permits companies to remain beforehand of the curve, apprehend market dynamics, and create price for his or her stakeholders. furthermore, the achievement of many modern-day expertise additionally relies on the availability and accuracy of the gathered data.
appropriate information collection ensures:
data integrity: guaranteeing the consistency and accuracy of knowledge over its complete lifecycle.
data good: Addressing troubles like inaccurate data or data high-quality issues which may derail enterprise targets.
data consistency: ensuring uniformity in data produced, making it simpler to analysis and interpret.
knowledge assortment Use situations and strategies
This part highlights some the reason why companies want statistics collection and lists a number of approaches to acquire knowledge for that particular cause.
AI improvement
statistics is required within the traits system of AI fashions, this part highlights 2 principal areas the place data is required within the AI tendencies approach. if you wish to work with a statistics assortment service firm to your AI initiatives, check out out this guide.
1. constructing AI fashions
The evolution of artificial intelligence (AI) has necessitated an improved consciousness on data assortment for companies and builders worldwide. They actively purchase enormous portions of statistics, vital for shaping superior AI fashions.
amongst these, conversational AI, like chatbots and voice assistants, stand excellent. Such buildings demand 86f68e4d402306ad3cd330d005134dac, related data that mirrors human interactions to hold out obligations naturally and efficaciously with clients.
previous conversational AI, the broader AI spectrum moreover hinges on exact data assortment, inclusive of:
machine studying
Predictive or prescriptive analytics
Generative AI
natural language processing (NLP), and plenty of others.
This knowledge assists AI in recognizing patterns, making predictions, and emulating duties previously completely different to human cognition. For any AI model to acquire its peak efficiency and precision, it crucially depends upon the positive and amount of its coaching statistics.
some well-known methods of amassing AI schooling data:
Crowdsourcing
Prepackaged datasets
In-residence knowledge collection
automated statistics assortment
internet scraping
Generative AI
Reinforcement gaining data of from human feedback (RLHF)
decide 1. AI data collection methods
AI visible listing the highest 6 AI data collection strategies listed previously.
2. bettering AI fashions
as quickly as a gadget mastering model is deployed, it must be superior. After being deployed, the general efficiency or accuracy of an AI/ML mannequin degrades over time (dad or mum 2). that’s significantly as a result of information, and the events by which the mannequin is getting used, alternate over time.
for instance, a distinctive assure machine utilized on a conveyor belt will perform sub-optimally if the product that itâs miles learning for defects changes (i.e., from apples to oranges). as well as, if a model works on a particular populace, and the populace changes over time, in order to moreover influence the general efficiency of the mannequin.
decide 2. efficiency of a mannequin decaying overtime1
A graph exhibiting the efficiency decay of a mannequin which isnât expert with clear information. Reinstating the importance of knowledge assortment for bettering AI fashions.
discern three. A typically retrained mannequin with contemporary data
A graph displaying that as a result of the model is retrained with clear data the general efficiency will increase and begins offevolved to fall as soon as extra untill its retrained. Reinstating the importance of data collection for AI improvement.
To analysis further about AI improvement, you may study the next:
7 steps to growing AI buildings
AI companies that may aid you construct your AI reply
endeavor analysis
research, an quintessential issue of tutorial, enterprise, and medical processes, is deeply rooted within the systematic assortment of knowledge. whether or not itâs market research aimed toward data buyer behaviors and market traits or tutorial research exploring difficult phenomena, the inspiration of any analysis lies in amassing pertinent data.
This data acts as a result of the bedrock, providing insights, validating hypotheses, and in the end serving to reply the distinctive analysis questions posed. moreover, the wonderful and relevance of the information collected can drastically influence the accuracy and reliability of the research results.
In these daysâs digital age, with the enormous array of knowledge collection strategies and tools at their disposal, researchers could make sure their inquiries are each complete and exact:
3. major information collection strategies
embody on-line surveys, consideration companies, interviews, and quizzes to collect primary data directly from the provision. you too can leverage crowdsourcing buildings to collect huge-scale human-generated datasets.
4. Secondary data collection
makes use of current information sources, often known as secondary data, like reviews, research, or third-birthday celebration information repositories. the utilization of internet scraping tools can assist accumulate secondary data accessible from on-line sources.
on-line promoting
companies actively purchase and study numerous sorts of statistics to brighten and refine their on-line advertising methods, making them extra tailor-made and efficient. through data patron habits, potentialities, and suggestions, companies can design further centered and related promoting campaigns. This customized approach can assist enhance the overall achievement and return on funding of the promoting and advertising efforts.
Listed below are some approaches to collect statistics for on line promoting:
5. on line survey for market analysis
promoting and advertising survey instruments or choices seize direct consumer feedback, presenting insights into potentialities and capability areas for improvement in merchandise and advertising strategies.
6. Social media monitoring
This strategy analyzes social media interactions to gauge consumer sentiment and examine the effectiveness of social media promoting strategies. Social media scraping instruments could also be used for this form of statistics.
7. internet analytics
web analytics tools monitor web site shopper habits and website guests, serving to within the optimization of web site design and on-line promoting methods.
8. e mail monitoring
e mail monitoring software program program measures the achievement of e-mail campaigns by way of monitoring key metrics like open and click-thru costs. you too can use e mail scrapers to amass related information for e mail promoting and advertising.
9. Competitor analysis
This technique screens competitorsâ actions to glean insights for refining and bettering oneâs personal promoting strategies. you can leverage aggressive intelligence gear that may aid you receive related data.
10. on-line communities and boards
Participation in on-line teams affords direct notion into purchaser opinions and points, facilitating direct interplay and suggestions assortment.
11. A/B testing
A/B trying out compares two promoting property to find out that’s extra highly effective in participating shoppers and driving conversions.
consumer engagement
corporations collect data to boost shopper engagement by way of understanding their options, behaviors, and feedback, taking into account higher customized and significant interactions. listed here are a number of methods companies can accumulate relevant statistics to enhance consumer engagement:
12. remarks varieties
teams can use feedback instruments or evaluation to build up direct insights from shoppers about their tales, selections, and expectations.
13. buyer help interactions
Recording and studying all interactions with the shoppers, which incorporates chats, emails, and calls, can help in understanding consumer troubles and bettering supplier delivery.
14. purchase historical past
analyzing shoppersâ buy histories helps companies customise gives and suggestions, enhancing the procuring expertise.
study further roughly buyer engagement gear with this information.
hazard administration and compliance
information permits companies understand, analyze, and mitigate potential risks, guaranteeing adherence to regulatory requirements, and selling sound, cozy business enterprise practices. right hereâs a list of the types of knowledge that organizations collect for hazard administration and compliance, and the best way this information could also be gathered:
15. Regulatory compliance data
companies can subscribe to regulatory substitute choices, work together jail groups to dwell educated roughly related legal guidelines, and insurance policies, and make use of compliance management software program to track and manipulate compliance data.
16. Audit statistics
habits regular inside and out of doors audits utilizing audit administration software program program to systematically accumulate, hold, and analyze audit data, along with findings, pointers, and resolutions.
17. Incident statistics
you should use incident management or response buildings to document, music, and analyze incidents; encourage workers to file troubles and use this information to boost hazard administration procedures.
18. worker schooling and coverage acknowledgment data
you may enforce learning management buildings to track worker education and use digital techniques for personnel to well-known protection data and compliance.
19. vendor and 1/3-birthday get together danger evaluation data
For this form of statistics, chances are you’ll appoint vendor intelligence and security likelihood evaluation tools. statistics gathered from these tools can help study and display screen the hazard ranges of out of doors events, ensuring that they adhere to the required compliance requirements and don’t current unexpected risks.
How do I clear my statistics with My AI?
To delete present content material materials shared with My AI throughout the closing 24 hoursâŚ
lengthy-press the message to your Chat with My AI
faucet âDeleteâ
To delete all previous content material shared with My AIâŚ
On iOS:
faucet your Profile icon and tap to go to Settings
Scroll right down to âprivateness Controlsâ
faucet âclear dataâ
faucet âclear My AI dataâ and make sure
On Android:
faucet your Profile icon and faucet to go to Settings
Scroll right down to âAccount actionsâ
faucet âclear My AI statisticsâ and confirm
Order Specs
for AI Datasets for Machine Learning
Are you seeking to make an inquiry relating to our Managed Companies âAI Datasets for Machine Studyingâ?
Right hereâs what we have to know:
- What’s the common scope of the duty?
- What sort of AI coaching knowledge will you require?
- How do you require the AI coaching knowledge to be processed?
- What sort of AI datasets do you want evaluated? How would you like them evaluated? Do you require us to observe a particular instruction set?
- What do you want examined or run by means of a set of processes? Do these duties require a particular kind?
- What’s the dimension of the AI coaching knowledge mission?
- Do you require ffshoring from a particular area?
- What sort of high quality management necessities do you’ve?
- Which knowledge format do you want the datases for machine studying / knowledge to be delivered in?
- Do you require an API connection?
For Images:
- Which format do you require the images to be?
technology
of Datasets for system learning
amassing massive quantities of 86f68e4d402306ad3cd330d005134dac AI schooling data that meet all necessities for a specific up-to-date goal is recurrently one of the vital as much as dateughupdated duties concurrently working on a gadget up-to-date problem.
For each particular person mission clickworker can offer you exact and newly created AI datasets, up-to-date pics, audio and video recordings up-to-date texts up-to-date aid you in growing your updated knowupdated-based upupdated algorithm.
Labeling & Validation
of Datasets for machine studying
In most situations properly organized AI education information inputs are solely achievable through human annotation and often play an important place in efficaciously education a up-to-date-up to datetallyupdated algorithm (AI). clickworker can help you in preparing your AI datasets with a global crowd of over 6 million Clickworkers although tagging and/or annotating textual content as well as upupdated imagery up-to-date in your wants
equally up to date that our crowd is prepared guarantee your current AI education statistics complies up-to-date specs and even evaluates output outcomes out of your algorithm by means of human logic.
about Dataset
Dataset Description:
The â5000 AI gear Datasetâ is a complete assortment of synthetic intelligence (AI) upupdated curated updated help data fanatics, researchers, and professionals within the topic of machine studying and statistics technological know-how. This dataset carries valuable statistics roughly an enormous number of AI up-to-date, consisting of their names, descriptions, pricing fashions, beneficial use instances, prices (if relevant), person opinions, deviceupdated hyperlinks, and important classes.
information Fields:
AI up-to-date identify: The decision of the AI deviceupdated or software program.
Description: A fast description of the as much as dateolâs capabilities and expertise.
free/Paid/different: signifies whether or not the as much as dateol is availableupdated freed from cost, has a paid subscription model, or falls under one other pricing class.
Useable For: Describes the first use instances or functions for which the AI up-to-date is acceptable.
bills: Specifies the value or pricing construction up-to-date the deviceupdated (if related).
evaluation: consumer-generated evaluations and rankings updated present insights inup-to-date the updatedâs efficiency and person satisfaction.
as much as dateupupdated hyperlink: URL or hyperlink updated as much as dateupdated the AI updatedâs legit web website or down load web page.
elementary class: Categorizes the AI up-to-date inup-to-date broader domains or lessons, upupdated natural language processing (NLP), computer imaginative and prescient, knowledge analytics, and higher.
Use instances:
analysis and analysis: Researchers can uncover the dataset updated discover out AI up-to-date relevant up-to-date their take a look at areas.
up-to-date evaluation: data professionals can use this dataset up to date consider and select essentially the most appropriate AI up-to-date for his or her initiatives.
market analysis: information-driven insights could also be derived upupdated the popularity and pricing tendencies of AI updated.
suggestions: system gaining data of fashions might be educated up to date advise AI up-to-date based mostly upupdated on particular requirements.
information supply:
The dataset is compiled from an expansion of sources, updated skilled up-to-date internet sites, shopper critiques, and official AI up-to-date direcup-to-dateries.
Licensing:
The dataset is made up-to-date underneath an open data license for analysis and tutorial capabilities.
Disclaimer:
concurrently efforts have been made updated make sure the accuracy of the statistics on this dataset, as much as datemersupdated are endorsed updated confirm particulars and refer upupdated dependable as much as dateupupdated web sites for the utmost 3177227fc5dac36e3e5ae6cd5820dcaa information and licensing phrases.
Acknowledgment:
We acknowledge and acknowledge the contributions of the AI group, updated builders, and up-to-date in growing and sustaining this invaluable support.
How do I get knowledge for my AI? Information Information. Any engineer who has taken the primary steps of gaining data of to work with AI strategies has confronted the foremost mission of the house: sourcing enough excessive nice information to make a process possible. sample items of knowledge might be had, of course, nonetheless ⌠Learn extra
How AI can be utilized finest in huge knowledge?
https://24x7offshoring.com/how-ai-can-be-used-best-in-big-data/?feed_id=128255&_unique_id=668736ae9f146
https://24x7offshoring.com/wp-content/uploads/2023/11/960×0.webp
#24x7offshoring #Information #machinelearning
https://24x7offshoring.com/how-ai-can-be-used-best-in-big-data/?feed_id=128255&_unique_id=668736ae9f146 https://24x7offshoring.com/how-ai-can-be-used-best-in-big-data/?feed_id=128255&_unique_id=668736ae9f146 #dataservice dataservice, 24x7offshoring, Information, machinelearning