In Gartner's 2025 Magic Quadrant for Data Science and Machine Learning (DSML) platforms, Databricks ranks first, surpassing last year's co-leaders Microsoft and Google. The report evaluates 16 vendors and highlights the shift towards generative AI and agentic AI, with foundation models playing a central role.
Databricks ranks first in Gartner's 2025 Magic Quadrant for DSML platforms.
Google's Vertex AI and Microsoft's Azure ML take second and third places, respectively.
Since ChatGPT's launch in late 2022, generative AI has sparked massive interest and hype. Organizations are racing to adopt GenAI, but there's a gap between desire and readiness. A recent IDC survey shows nearly 80% of executives are confident their companies will use GenAI, yet only 30% are well prepared. The article explores build vs. buy decisions, use cases (productivity, business functions, industry-specific), and key prerequisites like governance, privacy, and data architecture.
ChatGPT's launch ignited a GenAI frenzy, but most companies are not fully prepared. Only 30% of executives say their organizations are ready.
Companies face a critical build vs. buy decision: building custom models offers data control but is expensive and complex; buying APIs is easier but raises concerns about IP leakage and data governance.
The digital skills gap persists, with AI/ML shortages particularly acute. A SAS survey found 63% of decision-makers cite AI/ML as the biggest skills gap. O'Reilly's report shows surging interest in NLP and deep learning, while data engineering and cloud skills remain in high demand. Organizations prioritize training and upskilling to address the gap.
63% of decision-makers identify AI/ML as the biggest skills shortage.
Interest in NLP and deep learning grew 42% and 23% year-over-year.
Databricks acquires MosaicML for $1.3 billion to boost generative AI capabilities. MosaicML offers open-source models like MPT-7B and MPT-30B. The deal aims to democratize AI and reduce training costs.
Databricks announces $1.3B acquisition of MosaicML.
MosaicML's MPT-30B rivals GPT-3 with fewer parameters.
The article explores the impact of AI on application developers. While AI tools like ChatGPT and GitHub Copilot can boost productivity, they lack creativity and can hallucinate, so human developers remain essential. A survey shows only 33% of organizations use such tools, but actual usage is likely higher. Training is the biggest challenge, not ease of use.
AI tools like ChatGPT and Copilot assist programmers but cannot replace human creativity.
A survey indicates only 33% of organizations use AI coding tools, but actual usage is likely higher.
European lawmakers overwhelmingly approved the draft EU AI Act, which categorizes AI by risk levels, requiring impact assessments for high-risk systems and banning unacceptable risk uses. The act may become a global standard but faces criticism for lack of flexibility.
European Parliament approved the AI Act draft with 499 votes in favor, 28 against.
AI applications are classified into minimal, limited, high, and unacceptable risk tiers.
Cohere, the provider of an AI development platform, announced it has raised $270 million in a Series C round led by Inovia Capital, with participation from Nvidia, Oracle, Salesforce Ventures, and others. The funding will accelerate its enterprise AI platform and customization offerings.
Cohere raises $270M Series C led by Inovia Capital, with Nvidia, Oracle, Salesforce Ventures, and others.
The company offers a cloud-agnostic enterprise AI platform with multiple deployment options.
Weights & Biases unveiled two new products at its inaugural in-person conference: W&B Weave, an application building toolkit, and W&B Production Monitoring, an observability tool for ML models.
W&B Weave is an open-source toolkit for building data- and ML-powered applications with composable UI elements.
W&B Production Monitoring is an observability tool for troubleshooting and monitoring model performance, with custom alerts and user feedback integration.
An Altair survey reveals high adoption of data and AI strategies, but projects stall due to organizational, technical, and financial friction. Talent shortages, low AI literacy, legacy systems, and upfront costs are key barriers. Despite high failure rates, organizations continue AI investments for long-term gains.
75% of respondents struggle to find data science talent; 35% report low AI literacy.
33% of data science projects never reached production in two years.