Mhandisi wa kujifunza kwa mashine
Wahandisi wa kujifunza kwa mashine huchukua miundo iliyojengwa na wanasayansi wa data, au iliyonunuliwa kutoka kwa wasambazaji, na kuifanya ifanye kazi kwa uaminifu katika huduma za moja kwa moja za afya na matunzo. Hujenga mifereji inayofunza, kujaribu, kusambaza na kufuatilia miundo, na kuiunganisha na mifumo ya kitabibu na ya kiutendaji. Huhakikisha miundo inabaki salama, ya kuhifadhiwa, ya haki na yenye ufanisi wakati wagonjwa na wafanyakazi halisi wanaitegemea.
Tafsiri hii ilifanywa kutoka Kiingereza na msaidizi wa akili bandia na bado haijakaguliwa na mzungumzaji asilia. Nukuu kutoka UK GDaD PCF (Mfumo wa Uwezo wa Taaluma ya Kidijitali na Data wa Serikali ya Uingereza) na ESCO zinabaki kwa Kiingereza.
- Familia
- Data
- Jukumu la UK GDaD PCF
- Machine learning engineer (Data)
- Taaluma za ESCO
- artificial intelligence engineer (ISCO-08 2511)
- Kwenye GitHub
- Hati ya jukumu · Data za jukumu
Ngazi
| Daraja | Ngazi ya jukumu | Kiwango cha UK GDaD PCF | Madaraja ya Utumishi wa Umma | Alama za tathmini ya kazi |
|---|---|---|---|---|
| 7 | Mhandisi mwandamizi wa kujifunza kwa mashine | Senior machine learning engineer | SEO/G7/G6 | 495 |
| 8b | Mhandisi kiongozi wa kujifunza kwa mashine | Lead machine learning engineer | G7/G6 | 595 |
Katika taasisi ya huduma za afya za kidijitali
- Miundo inayotumika moja kwa moja inaweza kuteleza idadi ya wagonjwa, mazoezi ya kitabibu, au tabia za kurekodi zinapobadilika, hivyo utendaji hufuatiliwa kwa kila kundi la wagonjwa na miundo hufunzwa upya au kuondolewa inapohitajika.
- Muundo unaoongoza utambuzi, upangaji wa vipaumbele vya wagonjwa, au matibabu unaweza kudhibitiwa kama kifaa tiba, jambo linaloleta mahitaji rasmi ya mzunguko wa maisha wa programu, udhibiti wa mabadiliko na ufuatiliaji baada ya soko.
- Hoja za usalama wa kitabibu zinahitaji ushahidi kuhusu jinsi muundo unavyoshindwa, jinsi matokeo yake yanavyoonyeshwa kwa madaktari, na nini kinatokea usipopatikana.
- Miundo iliyofunzwa kwa data za wagonjwa inaweza kuvujisha taarifa kuhusu watu binafsi, hivyo data za mafunzo, vitu vya muundo na kumbukumbu hulindwa kama data zenyewe.
- AI ya kuzalisha na miundo mikubwa ya lugha huleta hatari zaidi, kama vile maudhui yaliyobuniwa katika maelezo ya kitabibu, ambazo zinahitaji majaribio maalumu na usimamizi wa binadamu.
Maelezo ya jukumu la UK GDaD PCF
A machine learning engineer develops, assures and maintains machine learning models so they can be used in products and services.
In this role, you will:
- be responsible for the software development and technical infrastructure needed to design, train, deploy and scale machine learning models
- provide and maintain effective, secure and sustainable machine learning models for use in products and services
- support all stages of the machine learning life cycle
- help product teams evaluate and choose appropriate machine learning solutions
Taaluma na ujuzi wa ESCO
ESCO huorodhesha ujuzi na maarifa ambayo waajiri kwa kawaida huhitaji (muhimu) au wanaweza kuhitaji (hiari) kwa kila taaluma. Yatumie kama orodha ya ukaguzi kwa mazungumzo ya maendeleo.
artificial intelligence engineer
Artificial intelligence engineer apply methods of artificial intelligence in engineering, robotics and computer science to design programs which simulate intelligence including thinking models, cognitive and knowledge-based systems, problem solving, and decision making. They also integrate structured knowledge into computer systems (ontologies, knowledge bases) in order to solve complex problems normally requiring a high level of human expertise or artificial intelligence methods.
Ujuzi na maarifa ya Muhimu (31)
Ujuzi na umahiri
- analyse big data
- analyse business requirements
- apply ICT systems theory
- create data sets
- creatively use digital technologies
- define technical requirements
- deliver visual presentation of data
- design process
- develop creative ideas
- develop statistical software
- use data processing techniques
Maarifa
- Python (computer programming)
- algorithms
- artificial neural networks
- business process modelling
- computer programming
- computer simulation
- data mining
- data models
- data science
- information architecture
- information categorisation
- information extraction
- information structure
- principles of artificial intelligence
- resource description framework query language
- systems development life-cycle
- task algorithmisation
- unstructured data
- visual presentation techniques
Mengineyo
Ujuzi na maarifa ya Hiari (58)
Ujuzi na umahiri
- apply systemic design thinking
- assess ICT knowledge
- build business relationships
- build predictive models
- build recommender systems
- design application interfaces
- design database scheme
- identify processes for re-engineering
- manage ICT data classification
- manage ICT semantic integration
- manage business knowledge
- perform dimensionality reduction
- utilise machine learning
Maarifa
- Agile project management
- Assembly (computer programming)
- C#
- C++
- COBOL
- CoffeeScript
- Common Lisp
- Erlang
- Groovy
- Haskell
- ICT project management methodologies
- Java (computer programming)
- JavaScript
- LINQ
- Lisp
- MATLAB
- ML (computer programming)
- Microsoft Visual C++
- N1QL
- Objective-C
- OpenEdge Advanced Business Language
- PHP
- Pascal (computer programming)
- Perl
- Process-based management
- Prolog (computer programming)
- R
- Ruby (computer programming)
- SAP R3
- SAS language
- SPARQL
- Scala
- Scratch (computer programming)
- Smalltalk (computer programming)
- Swift (computer programming)
- TypeScript
- VBScript
- Visual Basic
- business analytics
- business intelligence
- computer vision
- database development tools
- deep learning
- lean project management
- operational research
Contains ESCO v1.2.1 data (https://esco.ec.europa.eu/). © European Union. Reuse authorised under Commission Decision 2011/833/EU.