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While an increasing number of companies are investing in AI , many arestruggling to get AI - power projects into yield — much less delivering meaningful return on invested capital .
The challenges are many . But a commonly recurring one is datum management . The data that society need to train , rivulet and fine - tune AI models is disorganized , siloed , and otherwise unoptimized . In a 2022surveyby Great Expectations , an undefended source data benchmarking program , 77 % of organizations enjoin they were implicated about their data quality .
inauguration that predict to fix these data job are raking in funding .
Weka CEO Liran Zvibel and Weka ’s other co - founders , Maor Ben - Dayan and Omri Palmon , get together while make the data memory board inauguration XIV , which IBMacquiredfor $ 350 million in 2007 . The triad stayed on with IBM for a identification number of year but eventually left to pursue other , independent ventures .
The problem of manage data continued to eat at at Zvibel , though , he says .
“ I was frustrated and disillusioned with find out client forced to utilize disparate , siloed information base solutions that were uneconomical , costly and complex to deploy , manage and maintain , ” he said . “ The job became especially evident with the rise of cloud calculation and the coming of eminent - performance computing , simple machine learning and the early AI work load . ”
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So in 2013 , Zvibel enroll Ben - Dayan and Palmon to build a young set of data tool — a set that might bring about a better coming to hive away , managing and moving datum .
“ We see a chopine sinewy enough to support the performance demands of next - generation compute hardware and expectant - scale , datum - intensive workload in the demanding and distributed surround , ” Zvibel . “ To fit the pauperization of innovative workload , we knew it would necessitate to be able to action tens of terabytes of information and be deploy anywhere . ”
Weka ’s core offer is a parallel single file system , a eccentric of distributed file system that can unfold and orchestrate datum tasks ( for example copy files ) across multiple places ( for example servers and workstations ) at once . On top of this , Weka sells service and capabilities to corroborate AI and machine learnedness , visual effects and in high spirits - execution compute work load in environments spanning on - assumption data center , public clouds and hybrid swarm .
Zvibel claims that one key vantage of Weka ’s architecture is that it can hie up AI model training by repress the amount of time it take to copy data across entrepot placement . “ A typical reproductive AI data point word of mouth admit multiple steps of copying data sets , which emaciate lively education time , ” he say . “ Weka keeps simulation training hardware always feed with data so models can be trained quicker . ”
Weka competes with datum platform like DataDirect , Pure Storage , NetApp and Vast Data . Vast is among the more formidable of the bunch , havingcloseda $ 118 million Series E funding round in December 2023 that treble the startup ’s valuation to $ 9.1 billion .
But Weka appear to be holding its own , with a customer basis of over 300 brand including AI startup Stability AI , 11 of the Fortune 50 , and several unrevealed domesticated and strange government agencies .
Even with its relatively large head count ( ~400 employees worldwide , with programme to turn that number 25 % in the next year ) , Zvibel say that the Silicon Valley - based company now has a “ line of peck ” to cash flow positivity by December 2024 .
“ The modish wage increase was reckon based on favorable market status and proactive investor interest , which enabled us to raise at exceedingly favorable and advantageous term for Weka , ” he added . “ Our ordinary suntan pace is expect to be less than half a million per month before reaching that milepost . We ’ve outmatch $ 100 million in annual recurring taxation and are maintaining a hyper - increment flight . ”
