Scale ai

Summary. AI is embedding itself into the products and processes of virtually every industry. But implementing AI at scale remains an unresolved, frustrating issue for most organizations. Businesses can help ensure success of their AI efforts by scaling teams, processes, and tools in an integrated, cohesive manner. This is all part of an emerging discipline called MLOps. AI is no longer exclusively for digital native companies like Amazon, Netflix, or Uber. Dow Chemical Company recently used machine learning to AI is most valuable when it is operationalized at scale. For business leaders who wish to maximize business value using AI, scale refers to how deeply and widely AI is integrated into an organization’s core product or service and business processes. Unfortunately, scaling AI in this sense isn’t easy. Getting one or two AI models into production is very different from running an entire enterprise or product on AI. And as AI is scaled, problems can (and often do) scale, too. For example, one financial company lost $20,000 in 10 minutes because one of its machine learning models began to misbehave. With no visibility into the root issue — and no way to even identify which of its models was malfunctioning — the company was left with no choice but to pull the plug. All models were rolled back to much earlier iterations, which severely degraded performance and erased weeks of effort. Organizations that are serious about AI have started to adopt a new discipline,defined loo...

June 5, 2023Today, we’re launching QuantumBlack Horizon, a set of AI development tools from QuantumBlack, AI by McKinsey. Horizon was built within QuantumBlack Labs, our AI and machine learning innovation hub. This center consists of more than 250 technologists dedicated to driving AI innovation and supporting and accelerating the work of its more than 1300 data scientists across over 50 locations. QuantumBlack Horizon is a first-of-its-kind product suite helping organizations realize value from AI. Market studies indicate that approximately 90 percent of data science projects do not make it into production and usage in the field, suggesting the last five years of digital transformation have been defined more by proof-of-concept AI than operationalized value. “This launch is the culmination of significant investments in technical talent and R&D over the last three years. It includes critical McKinsey acquisitions like The “When it comes to AI, business leaders can learn a lot from their counterparts in Formula One racing, where QuantumBlack has its heritage using AI to dramatically improve car performance,” says • Data from all internal systems and external sources is clean, organized and accurate • AI4DQ: the award-winning solution to solve data quality issues, which was recently awarded “best use of AI for software development” by the AI Journal • Data scientists across an organization are creating models with a similar structure rather than “reinventing the wheel” so th...

Create Upload your data and easily review and edit your prompt. Compare Quickly compare experiments across different LLMs, prompts, and fine tuning strategies. Tune Fine tune on your existing data to continuously improve model performance. Deploy Deploy promising variants to production- ready API endpoints with built in monitoring and analytics in one click.

Sometimes the problem with But that’s all changing thanks to pre-trained, open source Starting from this foundation model, you can start solving automation problems easily with AI and using very little data—in some cases, called few-shot learning, just a few examples. In other cases, it’s sufficient to just describe the task you’re trying to solve. Solving the risks of massive datasets and re-establishing trust for generative AI Some foundation models for natural language processing (NLP), for instance, are pre-trained on massive amounts of data from the internet. Sometimes, you don’t know what data a model was trained on because the creators of those models won’t tell you. And those massive large-scale datasets contain some of the darker corners of the internet. It becomes difficult to ensure that the model algorithms outputs aren’t biased, or even toxic. This is an open, hard problem for the entire field of AI applications. At IBM, we want to infuse trust into everything we do, and we’re building our own foundation models with transparency at their core for clients to use. As a first step, we’re carefully curating an enterprise-ready data set using our data lake tooling to serve as a foundation for our, well, foundation models. We’re carefully removing problematic datasets, and we’re applying AI-based hate and profanity filters to remove objectionable content. That’s an example of negative curation—removing things. We also do positive curation—adding things we know our c...