What’s Trending in Data Science and ML? Preparing for 2026

One thing is clear, the companies who will lead the way in this Agentic Analytics shift, will be those who have the foresight to build a strong data engineering foundation and invest in their semantic modeling

With some fine-tuning, they can even outperform larger models on focused tasks.

newer architectures now use an orchestrator agent that breaks tasks into smaller pieces and delegates them to specialized sub-agents.

This “divide and conquer” approach also opens the door for sLMs to play a bigger role. Since each sub-agent only needs to handle a narrow task, even lightweight models can perform well when combined in a well-orchestrated system.

Instead of training a new model for every metric or product, foundation models like TimeGPT are starting to handle forecasting, anomaly detection, and other analytic tasks straight from raw data