# Infrastructure

Artificial Intelligence (AI) is reshaping the tech landscape, attracting a diverse array of investors eager to capitalize on its potential. In this article, we explore the key players driving investments in AI, from tech giants and venture capital funds to angel investors, and examine the companies they are betting on.

We aim to … [Read more about The AI Investment Landscape: Who’s Funding the Next Wave of Innovation?](/content/ai-investment-landscape/index.html)

The robotics industry stands on the brink of a significant transformation, with many experts – including NVIDIA CEO Jensen Huang – suggesting that we might be approaching a "ChatGPT moment" for robotics.

At the core of this revolution is the use of neural networks to create versatile robotic "brains" that enable robots to tackle various tasks much … [Read more about Humanoid Robots on the Rise: Industry Advances, Key Players, and Adoption Timelines](/content/humanoid-robots-overview-2024/index.html)

In a significant development that underscores the strategic importance of semiconductors in the global economy, the White House has recently announced a groundbreaking agreement with Taiwan Semiconductor Manufacturing Company (TSMC). The deal will see the U.S. government extend $11 billion in grants and loans to TSMC for the chip manufacturer to establish three advanced … [Read more about Navigating the Complexities of the Semiconductor Supply Chain](/content/semiconductor-supply-chain-overview/index.html)

The surge of interest and investment in artificial intelligence (AI) has cast a spotlight on an industry that, while often operating behind the scenes, is fundamental to technological advancement: the semiconductor industry. Semiconductors, or chips, are the heartbeats of modern electronics, from the simplest household gadgets to the most complex supercomputers powering … [Read more about Semiconductor Titans: Inside the World of AI Chip Manufacturing and Design](/content/ai-semiconductors-industry-overview/index.html)

Enterprises in every industry and corner of the globe are rushing to integrate the power of large language models (LLMs) like OpenAI’s ChatGPT, Anthropic’s Claude, and AI12Lab’s Jurassic to boost performance in a wide range of business applications, such as market research, customer service, and content generation.

However, building an LLM … [Read more about Step-By-Step LLM Product Development For Business Leaders](/content/llm-product-development-technology-stack/index.html)

Building search systems is hard. Preparing them to work with machine learning is really hard. Developing a complete search engine framework integrated with AI is really really hard.

So let’s make one. ✌️

In this post, we’ll build a search engine from scratch and discuss how to further optimize results by adding a machine learning layer using Kubeflow and Katib. This … [Read more about Building a Complete AI Based Search Engine with Elasticsearch, Kubeflow and Katib](/content/ai-search-engine-elasticsearch-kubeflow-katib/index.html)

By our definition, "enterprise" technology companies create tools for workplace roles and functions that a large number of businesses use.

Plenty of enterprise companies use combinations of automated data science, machine learning, and modern deep learning approaches for tasks like data preparation, predictive analytics, and process automation. Many are well-established … [Read more about The Essential Landscape of Enterprise AI Companies (2020)](/content/enterprise-ai-companies-2020/index.html)

To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. The resources I used include:

- Full stack deep learning
- LF AI Foundation landscape
- AI Data Landscape
- Various lists of top AI startups by the media
- Responses to my tweet and LinkedIn post
- People (friends, strangers, VCs) share … [Read more about An AI Researcher’s Exploration of 200 Machine Learning Tools](/content/tools-for-machine-learning-production/index.html)

# Introduction

The biggest issue facing machine learning is how to put the system into production. Machine learning systems differ from traditional software in two fundamental ways:

- Machine learning is never fully deterministic; therefore, the performance of an ML system can’t be evaluated against a strict specification. Instead, it should always be evaluated against … [Read more about The 5 Components Towards Building Production-Ready Machine Learning Systems](/content/building-production-ready-machine-learning-systems/index.html)

Creating a great machine learning system is an art.

There are a lot of things to consider while building a great machine learning system. But often it happens that we as data scientists only worry about certain parts of the project.

But do we ever think about how we will deploy our models once we have them?

I have seen a lot of ML projects, and a lot of them are doomed … [Read more about Take Your Machine Learning Models To Production With These 5 Simple Steps](/content/taking-ml-models-to-production-5-steps/index.html)