Referensi ML
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Pengembangan ML memerlukan penggunaan berbagai alat dan framework yang terus berkembang. Alat ML baru terus muncul sebagai cara untuk menangani
jenis data yang kompleks, kemajuan dalam hardware, dan teknik untuk mengatur
pipeline terus berkembang.
Akibatnya, perusahaan, organisasi, dan tim menerapkan
solusi ML menggunakan berbagai alat dan framework, yang kemungkinan
berubah dari waktu ke waktu.
Meskipun framework umum dan praktik terbaik muncul, perlu diingat bahwa
sifat masalah tertentu mungkin memerlukan solusi kustom dalam kasus
tertentu. Bagian berikut menyediakan link ke referensi untuk memulai
pengembangan ML dan AI.
Langkah berikutnya
Lanjutkan pendidikan ML Anda dengan menjelajahi kursus lain di
developers.google.com/machine-learning.
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
Terakhir diperbarui pada 2025-07-27 UTC.
[null,null,["Terakhir diperbarui pada 2025-07-27 UTC."],[[["\u003cp\u003eMachine learning (ML) development involves the use of various evolving tools and frameworks, leading to diverse implementation approaches across different entities.\u003c/p\u003e\n"],["\u003cp\u003eWhile common practices are emerging in ML, custom solutions may be necessary depending on the specific problem.\u003c/p\u003e\n"],["\u003cp\u003eGoogle provides resources for ML development, including tools and frameworks, as well as a community for sharing models and datasets.\u003c/p\u003e\n"],["\u003cp\u003eFurther learning opportunities are available through Google's machine learning courses.\u003c/p\u003e\n"]]],[],null,["# ML resources\n\nML development requires using a variety of constantly\nevolving tools and frameworks. New ML tools continue to emerge as ways to handle\ncomplex data types, advances in hardware, and techniques for orchestrating\npipelines continue to develop.\nAs a result, companies, organizations, and teams implement ML solutions using different tools and frameworks, which likely change over time.\n\n\u003cbr /\u003e\n\nWhile common frameworks and best practices are emerging, keep in mind that the\nnature of your particular problem might require custom solutions in certain\ncases. The following sections provide links to resources for getting started\nwith ML and AI development.\n\n- Tools and frameworks for building ML and AI applications and products:\n\n - [developers.google.com/focus/ai-development](https://developers.google.com/focus/ai-development)\n - [ai.google/build](https://ai.google/build)\n- AI and ML community for sharing ML models and datasets:\n\n - [Kaggle](https://kaggle.com)\n\nWhat's next\n-----------\n\nContinue your ML education by exploring other courses at\n[developers.google.com/machine-learning](https://developers.google.com/machine-learning/)."]]
