<prompt> Ke mathata afe ao likhamphani li tobanang le ona ha li sebelisa lisebelisoa tsa ML?nan  <response>: Instruction in English: What challenges do companies face when using ML systems? . Response in English: Companies face a myriad of challenges when it comes to using Machine Learning ML systems. Some of the main challenges include:1. Data Quality and Availability: ML models are dependent on high-quality data to train on. Sourcing, cleaning, and labeling large volumes of relevant data can be a daunting task for many companies.2. Expertise: Building, training, and deploying ML models require a level of technical expertise that may not always be readily available within the company. Hiring experienced data scientists and ML engineers can be a costly and time-consuming process.3. Infrastructure and Computing Power: ML models often need large amounts of processing power and storage capacity to run efficiently. Companies may need to invest in expensive hardware and cloud infrastructure to meet these requirements.4. Interpretability and Bias: ML models can be complex and difficult to interpret, making it hard to understand how they come to their decisions. Ensuring that ML models are free from bias and that their decisions can be explained in a transparent manner is crucial for gaining customer trust and avoiding legal issues.5. Integration and Maintenance: Integrating ML models into existing business processes and IT systems can be challenging. Companies need to ensure that their ML models are kept up-to-date and perform as expected as the data and the environment change over time.6. Cost: Developing and maintaining ML systems can be expensive, and companies need to ensure that they are getting a good return on their investment. This requires careful planning and an understanding of the costs and benefits associated with ML.Overall, while ML offers many potential benefits, it also requires significant effort and resources to implement effectively. Companies need to be aware of the challenges they might face and plan accordingly to ensure that their ML systems operate smoothly and deliver the expected results. . Response in Sesotho: Likhamphani li tobane le mathata a mangata ha ho tluoa tabeng ea ho sebelisa mechine ea ho Ithuta ka Mechini ML. Tse ling tsa liqholotso tse ka sehloohong li kenyeletsa: 1. Boleng ba Boitsebiso le Boteng ba Boitsebiso: Mefuta ea ML e itšetlehile ka lintlha tsa boleng bo phahameng ho koetlisa ka tsona. Ho fumana, ho hloekisa le ho ngola lintlha tse ngata tse nepahetseng e ka ba mosebetsi o boima ho likhamphani tse ngata. 2. Boitseanape: Ho aha, ho koetlisa, le ho tsamaisa mefuta ea ML ho hloka boemo ba boitseanape bo ka nang ba se fumanehe habonolo ka hara khamphani. Ho hira bo-rasaense ba nang le boiphihlelo ba data le baenjiniere ba ML e ka ba tsebetso e jang chelete e ngata ebile e nka nako. 3. Lisebelisoa le Matla a Khomphutha: Mehlala ea ML hangata e hloka matla a mangata a ho sebetsa le matla a polokelo ho sebetsa hantle. Likhamphani li kanna tsa hloka ho tsetela ho lisebelisoa tse turang le lisebelisoa tsa maru ho fihlela litlhoko tsena. 4. Tlhaloso le Leeme: Mehlala ea ML e ka ba e rarahaneng le ho ba thata ho e hlalosa, ho etsa hore ho be thata ho utloisisa hore na ba etsa liqeto joang. Ho netefatsa hore mefuta ea ML ha e na leeme le hore liqeto tsa bona li ka hlalosoa ka mokhoa o pepeneneng ho bohlokoa bakeng sa ho fumana tšepo ea bareki le ho qoba litaba tsa molao. 5. Kopanyo le Tlhokomelo: Ho kopanya mefuta ea ML mekhoeng e teng ea khoebo le mekhoa ea IT e ka ba phephetso. Likhamphani li hloka ho netefatsa hore mefuta ea tsona ea ML e lula e le morao-rao le ho sebetsa ka mokhoa o lebelletsoeng ha data le tikoloho li fetoha ha nako e ntse e ea. 6. Litšenyehelo: Ho theha le ho hlokomela litsamaiso tsa ML ho ka bitsa chelete e ngata, me likhamphani li lokela ho etsa bonnete ba hore li fumana phaello e ntle matsete a tsona. Sena se hloka moralo o hlokolosi le kutloisiso ea litšenyehelo le melemo e amanang le ML. Ka kakaretso, le hoja ML e fana ka melemo e mengata e ka bang teng, e boetse e hloka boiteko bo matla le lisebelisoa ho kenya tsebetsong ka katleho. Likhamphani li hloka ho tseba mathata ao li ka tobanang le ona le ho rera ka nepo ho netefatsa hore litsamaiso tsa tsona tsa ML li sebetsa hantle le ho fana ka litholoana tse lebelletsoeng. 