Pular para o conteúdo principal

Preencha os detalhes para desbloquear o webinar

Ao continuar, você aceita nossos Termos de Uso, nossa Política de Privacidade e que seus dados são armazenados nos EUA.

Falantes

Saiba Mais

Treinar 2 ou mais pessoas?

Obtenha acesso à biblioteca completa do DataCamp, com relatórios, atribuições, projetos e muito mais centralizados
Experimente O DataCamp for BusinessPara uma solução sob medida , agende uma demonstração.

Data & AI Trends & Predictions 2024

January 2024
Compartilhar

As we enter the new year, the pace of progress in data science and artificial intelligence has never been faster. From awe-inspiring generative AI tools to increased investments in data & analytics, the data & AI space has never been more exciting. So, what’s in store for the industry in 2024?

In this webinar, Adel Nehme, VP of Media at DataCamp, and Richie Cotton, Data Evangelist at DataCamp, and co-hosts of the DataFramed podcast, will take out their crystal balls and share their data & AI trends & predictions for 2024.

Key Takeaways:

  • Revisiting DataCamp’s 2023 data & AI trends and predictions
  • Key data, generative AI, and upskilling trends headed into 2024
  • Best practices for navigating the year ahead

Additional Resources:

Slides

[WHITEPAPER] Data & AI Trends & Predictions 2024

[PODCAST] Subscribe to the DataFramed podcast

[TUTORIAL] Fine-Tuning LLaMA 2: A Step-by-Step Guide to Customizing the Large Language Model

[WORKSPACE] AI Assistant

[CHEAT SHEET] Data Governance Fundamentals Cheat Sheet

Summary

As we prepare for 2024, the field of data and AI is set for significant shifts. The webinar transcript highlighted the transition from the excitement of AI breakthroughs in 2023 to the practical application of these technologies in 2024. Large language models continue to be influential, yet the conversation is moving towards practical and cost-effective applications. The year ahead promises the widespread use of generative AI, with companies investigating both open and closed-source models to balance innovation with security. The focus is increasingly on incorporating AI into regular business operations, emphasizing efficiency and value creation. As organizations aim to utilize AI's potential, data governance and literacy become vital to ensure successful implementation and ethical use. The changing role of AI in professional settings emphasizes the need for a workforce skilled in both technical and strategic aspects, highlighting the importance of aligning technological abilities with business objectives.

Key Takeaways:

  • Generative AI is moving from excitement to mainstream, with a focus on practical applications.
  • Organizations are balancing between open-source and closed-source AI models to optimize performance and security.
  • Data governance and literacy are essential to ensuring effective and ethical AI deployment.
  • Practical AI use cases, which focus on efficiency and automation, will generate significant value.
  • AI literacy is becoming a necessary skill across various professions.

Deep Dives

Generative AI Goes Mainstream

The widespread use of generative AI in 2024 signifies an impor ...
Ler Mais

tant moment for technology integration in business. While tools like ChatGPT have become well-known, with even non-technical users interacting with them, the real challenge lies in applying these technologies within organizational frameworks. According to the WaveStone 2024 Data and AI Leadership Executive Survey, a large portion of Fortune 1000 companies are still in the experimental phase with AI, indicating a vast opportunity for growth and implementation. The focus is on moving from experimentation to deployment, ensuring that AI applications provide tangible value. The webinar highlighted the need for organizations to overcome obstacles such as data management and compute costs to effectively integrate AI solutions.

Open-Source vs. Closed-Source AI Models

The discussion between open-source and closed-source AI models is heating up as organizations aim to use the best of both worlds. Open-source models like Mistral and Lama 2 offer transparency and community-driven improvements, which can enhance security and performance. However, closed-source models often provide more comprehensive support and proprietary innovations. The choice between these models often depends on data security concerns and organizational needs. Haga Lopesco from Mosaic ML highlighted the risks associated with closed-source providers, emphasizing the importance of data privacy. As companies weigh these options, the trend towards a blended approach, using both open and closed models, is expected to grow.

Practical AI Use Cases

While advanced AI applications grab headlines, it is the routine, yet essential, AI use cases that are set to deliver substantial value in 2024. These include automating standard tasks such as data entry, document processing, and customer support. AI's ability to simplify these processes can lead to significant efficiency gains, freeing up human resources for more strategic tasks. For instance, AI tools can handle tasks like summarizing meeting notes or generating responses to customer inquiries, enhancing productivity. The emphasis on applying such 'practical' AI use cases emphasizes the potential for AI to transform business operations in practical ways.

Data Governance and AI Literacy

As AI technologies become more embedded in business operations, the importance of data governance and AI literacy cannot be overstated. The accuracy and reliability of AI outputs are heavily dependent on data quality, necessitating comprehensive governance frameworks. Scott Taylor's concept of 'artificial stupidity'—a result of poor data quality—highlights the risks involved. Moreover, AI literacy is becoming a fundamental skill across professions, with a growing need for individuals to understand AI's capabilities and limitations. As organizations invest in data and AI literacy, they empower their workforce to effectively use AI tools, promoting a culture of innovation and informed decision-making.

View Slides

Relacionado

white paper

2022 Data Trends and Predictions

Read about 9 trends shaping data science in 2022 and beyond

webinar

The State of Data & AI Literacy in 2024

Join this webinar to learn how and which data & AI skills are becoming increasingly pervasive in organizations across industries, how leaders are adapting their teams and workforce to the era of data & AI literacy and more.

webinar

Building Your Organization’s Data & AI Maturity

Adel Nehme, VP of Media at DataCamp, details the path to become a data & AI mature organization.

webinar

DataCamp 2024 Q3 Roadmap

Whether you’re investing in your data and AI skills or responsible for your organization’s professional development, join us for an overview of new features and content coming to DataCamp in the third quarter of 2024.

webinar

DataCamp 2024 Q1 Roadmap

What’s next for DataCamp? Whether you’re investing in your data and AI skills or responsible for your organization’s professional development, join us for an overview of new features and content coming to DataCamp in the first quarter of 2024.

webinar

Expert Sessions: How to Break into AI in 2024

In this webinar, Sadie St. Lawrence, Chief AI Officer at SSL Innovations and Founder of Women in Data, will provide an in-depth exploration of best practices for breaking into AI in 2024.

Hands-on learning experience

Companies using DataCamp achieve course completion rates 6X higher than traditional online course providers

Learn More

Upskill your teams in data science and analytics

Learn More

Join 5,000+ companies and 80% of the Fortune 1000 who use DataCamp to upskill their teams.

Don’t just take our word for it.