eBooks

Explore cutting-edge advancements, data optimization strategies, and ethical considerations to unlock AI's full potential for your enterprise.

Discover how combining Retrieval Augmented Generation (RAG) with human expertise drives high-quality AI results.

How chain of thought prompting enhances LLM reasoning - featuring an expert case study on mathematical reasoning for a leading technology company.

Launching Customer-centric AI for Autonomous Vehicles and Smart Cars The auto industry is undergoing a profound transformation, with AI and electric changing the way companies build cars and influencing the type of consumer that will ultimately buy or use those cars. The future of transportation will be built with world-class AI, ultra-fast connectivity, and environmental impact in mind. The potential

Approaches, Applications, and Best Practices using the Appen Data Annotation Platform Human vision systems have the advantage of learning from a lifetime of experiences how to contextualize the things we see. Machine learning models, on the other hand, usually need a substantial number of real-world scenarios to learn from to be able to product reliable computer vision (CV) outputs

Artificial Intelligence (AI) has the potential to add $13 trillion to our global economy by 2030, according to McKinsey. Those who have successfully deployed AI see high returns on investment and increased customer satisfaction through more personalized, efficient AI tools. With AI investment growing and teams rushing to find ways to bring it into the core of their business

Key Steps to Building Customer-Centric AI According to Cambridge Centre for Alternative Finance, by 2022, nearly 64% of financial service companies expect to be “mass adopters” of AI — using it for revenue generation, process automation, risk management, customer service, and client acquisition — compared with just 16% currently. Yet success isn’t guaranteed. In fact, industry analysts find that the

Why Your AI Model is Only as Good as Its Training Data There’s a saying of garbage in, garbage out when it comes to artificial intelligence and machine learning. It’s common knowledge that every machine learning solution needs a good algorithm powering it, but what gets far less press is what actually goes into these algorithms: the training data itself

Learn why creating an AI Center of Excellence will facilitate increased returns on AI investments. It will likely take time and cycles, tons of training data, algorithms, use cases, and several stakeholders involved in order to uncover its value. Yet it will be worth it as you build momentum for your team and business. Building an AI Center of Excellence

Over a petabyte of data gets consumed by many companies, but repurposing that high-quality training data for machine learning and artificial intelligence initiatives can be a heavy uplift. This high-quality training data is critical for moving AI and ML algorithms from experiment, all the way to core business. Business leaders are turning to AIOps and professional services to guide the