Artificial Intelligence (AI) has exploded in popularity and ubiquity over the past decade. From smart assistants like Alexa and Siri to content recommendation engines on streaming platforms, AI now plays a central role in technology services that enrich people’s information, communication and entertainment experiences.
However, developing these complex AI systems requires mastering an array of programming frameworks, cloud platforms and automation suites. So what are some of the essential ingredients in today’s AI builder’s toolkit? Let’s explore the key tools empowering businesses to unlock value through AI.
Two open-source libraries sit at the heart of modern AI programming — TensorFlow and PyTorch. Developed by tech giants Google and Facebook respectively, they provide the core infrastructure to construct, train and run machine learning models.
In particular, they excel at enabling deep learning — an advanced ML technique that mimics neural pathways in the human brain. The flexible neural architectures created via TensorFlow and PyTorch can learn complex concepts like speech, images and video.
For instance, by leveraging TensorFlow and deep learning, Netflix was able to create highly accurate movie recommendation algorithms that understand users’ tastes. The intuitive PyTorch interface has also powered breakthroughs in medical diagnosis systems built by startup PathAI.
Although TensorFlow usage is more widespread currently, PyTorch adoption is rapidly rising within AI research circles for its coding efficiency and ease of customization. As AI programming becomes mainstream, these libraries are cementing their place as must-have components of the modern technology stack.
While TensorFlow and PyTorch provide state-of-the-art capabilities, their intricate tooling can steepen the learning curve for junior AI talent. This is where the Scikit-Learn Python library bridges the gap.
Living up to its motto of “making machine learning simple and efficient”, Scikit-Learn makes implementing AI…
Be the first to comment