Democratized, operationalized, and responsible: the 3 keys to successful AI and ML outcomes

January 12, 2023

Share this Flipbook

How to succeed with artificial intelligence and machine learning

A growing number of companies, from emerging startups to established enterprises, are deploying artificial intelligence (AI) and machine learning (ML) to boost innovation and competitive advantage. So, while AI and ML investments are predicted to top $200 billion by 2025, companies want assurances that their initiatives will bear fruit. This eBook details how democratization, operationalization, and responsibility—the three keys to effective adoption—promote successful outcomes.

Previous Flipbook

Optimize your digital value chain with machine learning

Read this eBook to learn how 4 digitally native companies are using machine learning to solve key challenge...

Next Flipbook

6 steps to a successful machine learning journey

Read this eBook to learn how to achieve success with machine learning. Follow the 6 steps to achieve measur...

Most Recent Flipbooks

TCS generative AI solutions

This session will cover a few of the key generative AI solutions like Contact Center Transformation, Contract Management, and how TCS helps enterprises accelerate the generative AI adoption.

Read eBook »

Deploying large generative AI models on SageMaker

Democratized, operationalized, and responsible: the 3 keys to successful AI and ML outcomes

How to succeed with artificial intelligence and machine learning

Previous Flipbook

Next Flipbook

Most Recent Flipbooks

This session will cover a few of the key generative AI solutions like Contact Center Transformation, Contract Management, and how TCS helps enterprises accelerate the generative AI adoption.

Discover the latest frameworks, sharding techniques, and deployment patterns that can help you scale your generative AI models efficiently.

Learn how to use security and privacy to accelerate going to market with emerging AI/ML technologies such as generative AI.

This presentation discusses the architectures, data flows, and security-related aspects of model fine-tuning, as well as the prompting and inference phases.

Review AWS' strategy and position on generative AI, market opportunity and potential, and AWS' approach on working backwards to develop products and services.

In this presentation, we will discuss the following: a brief overview of stable diffusion models, advanced techniques, a walk through of code examples.

This workshop will cover how to quickly add jumpstart pre-trained models to an application as is or finetune them for a customized experience.

Explore an innovative approach to Intelligent Document Processing (IDP) that utilizes a dialogue-guided query solution.

Learn how you can leverage generative AI products, such as Krikey.AI, in combination with SageMaker Ground Truth Plus to create custom AI Animation models.

In this presentation, we provide an overview of the AI services for IDP, how they can be leveraged with Amazon Bedrock, and show demos of this combination.

This presentation is designed for business and technical decision makers to jumpstart their adoption of Generative AI and quickly demonstrate early and tangible wins.

Learn how you can leverage generative AI with RAG knowledge retrieval to seamlessly connect predictive maintenance solutions on machine data with OT team’s maintenance workflow.

In this presentation, we go over three fine-tuning techniques, namely Instruction fine-tuning, Domain adaptation fine-tuning, and Reinforcement Learning with Human Feedback (RLHF).

This presentation will explore the role of large language model (LLMs) frameworks in bridging the gap between LLMs and enterprise solutions.

This presentation covers large-scale model training using Amazon SageMaker on AWS, focusing on core concepts, data preparation, methodology, and optimization techniques.

Learn how to use foundation models and built-in algorithms to accelerate ML innovation in your business.

Dive into how AWS contact center Intelligence with large language models can revolutionize the customer support experience and explore the benefits of using large language models on AWS.

In this code walk-through, we will discuss mechanisms to build a human-feedback workflow to further fine-tune and improve our model.