Google launches free Gemini-powered Data Science Agent on its Colab Python platform - Related to bolder, science, summit, fuel, gemini-powered

Cypher 2025: India’s Largest AI Summit Returns—Bigger and Bolder

The AI revolution has found its home in India, and it’s called Cypher. Since its inception in 2015, Cypher has grown from a bold idea into the largest AI conference in the country. Now, as we gear up for its 9th edition, Cypher 2025 is set to make history.

From September 17-19, 2025, the KTPO Trade Centre in Whitefield. Bengaluru, will host a staggering 5,000 attendees daily—the biggest footfall in Cypher’s history. Expect an unmatched convergence of industry leaders, AI pioneers, enterprises, startups, policymakers, and data practitioners, all under one roof to discuss the future of AI.

Over the years. Cypher has built a reputation as the definitive AI summit in India. The 2025 edition is scaling up across every dimension:

5000+ attendees per day , making it the largest AI event in India.

, making it the largest AI event in India 100+ speakers from top enterprises, startups, and academia.

from top enterprises, startups, and academia 100+ exhibitors showcasing cutting-edge AI solutions.

showcasing cutting-edge AI solutions Three days of knowledge sharing, networking, and collaboration.

Whether you’re an AI enthusiast, a data leader, a startup founder, or a corporate decision-maker. Cypher 2025 is where you’ll find the latest breakthroughs in AI, machine learning, and data science.

Hear from the biggest names in AI—founders, industry veterans, researchers, and policy experts—discussing the state of AI and what the future holds. The lineup includes enterprise CXOs, AI researchers, and policymakers shaping AI adoption in India and beyond.

Cypher 2025 will feature an expansive expo floor where top companies, startups, and research institutions will showcase innovations in generative AI, computer vision. NLP, AI ethics, automation, and more. Live demos will provide a hands-on experience with the latest in AI-driven enterprise solutions.

Experience AI in action with real-time product demonstrations from leading AI firms. From autonomous AI agents to next-gen machine learning models, you’ll get an up-close look at AI’s most exciting advancements.

Deep-dive hands-on workshops will cover topics such as LLM fine-tuning, AI deployment, prompt engineering. And AI governance. Led by industry practitioners, these workshops offer practical, skill-based learning for AI professionals and enterprises.

With 5000+ attendees daily. Cypher is the ultimate networking hub for AI professionals. Meet and collaborate with founders, AI engineers, investors, and CXOs shaping India’s AI ecosystem.

Stay ahead with insights from AI labs, leading research institutions. And think tanks. Cypher 2025 will feature .

Cypher 2025 will once again host the prestigious Minsky Awards, recognizing outstanding AI innovation at the enterprise level. These awards highlight excellence in AI-driven transformation across industries like BFSI, healthcare, retail, and manufacturing.

Cypher 2025 brings together the biggest thought leaders in AI. Past speakers have included Mohandas Pai, Ronnie Screwvala, Sunil Gupta, Manu Kumar Jain, Pradeep Gulipalli, and many more. Expect an even bigger lineup of AI visionaries for 2025.

Cypher 2025 offers the perfect platform for companies to showcase their AI innovations, connect with decision-makers. And drive business growth. With over 100 booths available, exhibitors will have the chance to present their solutions to thousands of industry professionals.

For sponsorship and exhibition inquiries, write to .

Tickets for Cypher 2025 are now available with super early bird pricing:

Super Early Bird (₹6000) – Available till April 11, 2025.

Early Bird (₹15000) – April 12 to July 11, 2025.

Regular Pass (₹25000) – July 12 to August 22, 2025.

Late Pass (₹35000) – After August 23. 2025.

All tickets include full 3-day access, conference lunches, and networking opportunities. Group discounts are available for teams and enterprises.

The AI landscape is evolving at a breakneck pace. Cypher 2025 is the place to stay ahead, build connections, and drive AI-led transformation. Don’t miss out on the biggest AI event in India.

Save the Date: September 17-19, 2025 | KTPO, Whitefield, Bengaluru.

Book your pass now and be part of India’s AI future!

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Google launches free Gemini-powered Data Science Agent on its Colab Python platform

AI agents are all the rage, but how about one focused specifically on analyzing, sorting and drawing conclusions from vast volumes of data?

Google’s data science agent does just that: The new, free Gemini AI assistant that automates data analysis is now available to customers aged 18-plus in select countries and languages for free.

The assistant is available through Google Colab, the business’s eight-year-old service for running Python code live online atop graphics processing units (GPUs) owned by the search giant and its own, in-house tensor processing units (TPUs).

Initially launched for trusted testers in December 2024, data science agent is designed to help researchers, data scientists and developers streamline their workflows by generating fully-functional Jupyter notebooks from natural language descriptions, all in the user’s browser.

Furthermore, this expansion aligns with Google’s ongoing efforts to integrate AI-driven coding and data science aspects into Colab, building on past updates such as Codey-powered AI coding assistance. unveiled in May 2023.

It also acts as a kind of advanced and belated rejoinder to OpenAI’s ChatGPT advanced data analysis (previously Code Interpreter), which is now built into ChatGPT when running GPT-4.

Google Colab (short for colaboratory) is a cloud-based Jupyter Notebook environment that enables people to write and execute Python code directly in their browser.

Jupyter Notebook is an open-source web application that enables people to create and share documents containing live code, equations, visualizations and narrative text. Originating from the IPython project in 2014, it now supports more than 40 programming languages, including Python, R and Julia. This interactive platform is widely used in data science, research and education for tasks like data analysis, visualization and teaching programming concepts.

Since its launch in 2017, Google Colab has become one of the most widely-used platforms for machine learning (ML) data science and education.

As Ori Abramovsky, data science lead at , detailed in an excellent Medium post from 2023, Colab’s ease of use and free access to GPUs and TPUs make it a standout option for many developers and. Researchers.

He noted that the low barrier to entry, seamless integration with Google Drive and support for TPUs allowed his team to dramatically shorten training cycles while working on AI models.

However, Abramovsky also pointed out Colab’s limitations, such as:

Session time limits (especially for free-tier customers).

(especially for free-tier consumers). Unpredictable resource allocation at peak usage times.

at peak usage times. Lack of critical functions , like efficient pipeline execution and advanced scheduling.

, like efficient pipeline execution and advanced scheduling. Support challenges, as Google provides limited options for direct assistance.

Despite these drawbacks, Abramovsky emphasized that Colab remains one of the best serverless notebook solutions available — particularly in the early stages of ML and data analysis projects.

The data science agent builds on Colab’s serverless notebook environment by eliminating the need for manual setup.

Using Google’s Gemini AI, customers can describe their analytical goals in plain English (“visualize trends,” “train a prediction model,” “clean missing values”), and. The agent generates fully-executable Colab notebooks in response.

Automating analysis : Generates complete, working notebooks instead of isolated code snippets.

: Generates complete, working notebooks instead of isolated code snippets. Saving time : Eliminates manual setup and repetitive coding.

: Eliminates manual setup and repetitive coding. Enhancing collaboration : elements built-in sharing elements for team-based projects.

: elements built-in sharing elements for team-based projects. Offering modifiable solutions: individuals can adjust and customize generated code.

Data science agent is already accelerating real-world scientific research.

, early testers have reported significant time savings when using data science agent.

For instance, a scientist at Lawrence Berkeley National Laboratory working on tropical wetland methane emissions estimated that their data processing time dropped from one week to just five minutes when using the agent.

The tool has also performed well in industry benchmarks, ranking 4th on the DABStep: Data Agent Benchmark for Multi-step Reasoning on Hugging Face, ahead of AI agents such as ReAct (, Deepseek, Claude Haiku and. Llama 70B.

However, OpenAI’s rival o3-mini and o1 models, as well as Anthropic’s Claude Sonnet, both outclassed the new Gemini data science agent.

consumers can start using data science agent in Google Colab by following these steps:

Open a new Colab notebook. Upload a dataset (CSV, JSON, etc.). Describe the analysis in natural language using the Gemini side panel. Execute the generated notebook to see insights and visualizations.

Google provides sample datasets and prompt ideas to help customers explore its capabilities, including:

Stack Overflow developer survey : “Visualize most popular programming languages.”.

: “Visualize most popular programming languages.” Iris Species dataset : “Calculate and visualize Pearson, Spearman and Kendall correlations.”.

: “Calculate and visualize Pearson, Spearman and Kendall correlations.” Glass Classification dataset: “Train a random forest classifier.”.

Granted, I’m a lowly tech journalist and not a data scientist, but my own usage of the new Gemini data science agent in Colab so far has been less than seamless.

I uploaded five CSV files (comma separated values. Standard spreadsheet files from Excel or Sheets) and asked it “How much am I spending each month and quarter on my utilities?”.

Furthermore, the agent went ahead and performed the following operations:

Merged datasets , handling date and account number inconsistencies.

, handling date and account number inconsistencies. Filtered and cleaned the data , ensuring only relevant expenses remained.

, ensuring only relevant expenses remained. Grouped transactions by month and quarter to calculate spending.

by month and quarter to calculate spending. Generated visualizations , such as line charts for trend analysis.

, such as line charts for trend analysis. Summarized findings in a clear, structured analysis.

Before execution, Colab prompted a confirmation message, reminding me that it might interact with external APIs.

It did all this very rapidly and smoothly in the browser. In a matter of seconds. And it was impressive to watch it work through the analysis and programming with visible step-by-step descriptions of what it was doing.

However, it ultimately generated an inaccurate graph showing just one month’s utility spending. Failing to recognize the sheets included a full year’s worth broken out by months. When I asked it to revise, it gamely tried, but ultimately couldn’t produce the correct code string to answer my prompt.

I tried from scratch with the exact same prompt on a new notebook in Google Colab, and it produced a far advanced, yet still odd result.

I’ll have to try troubleshooting it some more, and as I mentioned, the initial erroneous result may be due to my own lack of experience using data science tools.

While Google Colab remains free, customers who need additional compute power can upgrade to paid plans:

Colab pro ($ : 100 compute units. Faster GPUs, more memory, terminal access.

: 100 compute units, faster GPUs, more memory, terminal access. Colab pro+ ($ : 500 compute units, priority GPU upgrades, background execution.

: 500 compute units, priority GPU upgrades, background execution. Colab enterprise : Google Cloud integration, AI-powered code generation.

: Google Cloud integration, AI-powered code generation. Pay-as-you-go: $ for 100 compute units, $ for 500 compute units.

In addition to data science agent, Google has been expanding AI capabilities within Colab.

Google collects prompts. Generated code and user feedback to improve its AI models. While data is stored for up to 18 months, it is anonymized, and deletion requests may not always be fulfilled. consumers are advised not to submit sensitive or personal information, as human reviewers may process prompts. Additionally, AI-generated code should be reviewed carefully, as it may contain inaccuracies.

Google encourages customers to provide feedback through the Google Labs Discord community in the #data-science-agent channel.

With AI-driven automation becoming a key trend in data science, Google’s data science agent in Colab could help researchers and. Developers focus more on insights and less on coding setup. As the tool expands to more consumers and regions, it will be interesting to see how it shapes the future of AI-assisted analytics.

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Freshworks’ Founder Launches Together AI Studio to Fuel Indian AI Startups

Together Fund has introduced Together AI Studio, a venture-building initiative aimed at fostering AI-first startups in India. The program seeks to equip AI entrepreneurs with essential resources, industry mentorship, and capital support to drive innovation.

The Together AI Studio program, set to commence on April 15, 2025. Will run for 12 weeks, with 11 weeks in Bengaluru and one week in the US. Participating startups will receive up to $1 million in funding, mentorship from leading AI experts, and access to an extensive US-India network to accelerate global expansion.

Additionally, the program offers over $600,000 in cloud credits from OpenAI, AWS, Google Cloud, and Microsoft Azure, along with dedicated office space in Bengaluru.

“At Together Fund, we are doubling down on our commitment to back bold AI founders who are reimagining the future,” stated Girish Mathrubootham, founder of Freshworks and founding partner at .

Along with Mathrubootham. Together AI Studio is led by entrepreneurs and investors including Manav Garg, Lakshmi Shankar, Shubham Gupta, Sethu Chidambaram, Shivam Kant, and Kaushik Srinivasan. The team provides AI founders with access to industry expertise, market insights, and hands-on guidance.

Together AI Studio is particularly focused on supporting AI-driven business applications that optimise enterprise functions such as customer relationship management, automation. And cybersecurity. The healthcare sector remains a key area of interest, with AI innovations driving improvements in diagnostics, patient care, and workflow efficiency. Additionally, the program is fostering advancements in frontier AI research, including quantum computing, brain-computer interfaces, and. AI applications in defense and security.

Moving to another aspect, they have already invested in several AI-driven companies spanning diverse industries. These include Chaotix, an AI-powered game creation platform, and Composio, which integrates AI agents and large language models with enterprise applications. And other AI startups.

A number of leaders are commencing programs to support AI startups in India. in recent times, Project EKA, led by AI startup Soket Labs, was introduced as an initiative to develop advanced foundation models optimised for India’s linguistic and socio-economic diversity while competing globally.

Bringing together AI researchers, engineers, and institutions. The initiative aims to build high-efficiency, multilingual AI models within an open, ethical, and impactful ecosystem. Experts from IITs, IISc, and global research centers are collaborating to create a self-reliant AI infrastructure spanning education, finance, national security. And agriculture.

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