Quick Start Environments: Simplifying AI Development for Practitioners

As data scientists, ML engineers, and developers, we’ve all faced the frustration of environment setup. The hours spent configuring dependencies, resolving conflicts, and ensuring everything works correctly are hours not spent on actual analysis and model development. Anaconda’s new quick start environments aim to eliminate this pain point entirely.

What Are Quick Start Environments?

Quick start environments are pre-configured, purpose-built environments for Jupyter Notebooks in the Anaconda AI Platform that come with carefully selected packages tailored for specific use cases. Our experts have carefully curated each environment to include the optimal mix of tested, compatible packages, eliminating the complexity that often derails projects and allowing you to leverage configurations that just work.

Think of them as expert-assembled toolkits, ready to use the moment you need them, with all components guaranteed to work together seamlessly. Check out the video below to see quick start environments in action, and read on to learn more about the four quick start environments included in this initial release. 

How to Access Quick Start Environments

Accessing Quick Start Environments is straightforward within the Anaconda AI Platform:

1. Install Anaconda Toolbox from Navigator (or using conda install anaconda-toolbox), or update to v4.20+ (conda update anaconda-toolbox) if you have it installed already.
2. Log into the Anaconda AI Platform
3. Open Jupyter Lab or Notebooks
4. Open the Anaconda Toolbox and select the “Create New Environment” option. 
5. Launch your chosen environment with a single click

The entire process takes minutes rather than the hours often required to configure environments manually. These new preconfigured environments are available to all Anaconda AI Platform users across all tiers.

The Four Quick Start Environments (With More to Come)

In this initial release, Anaconda users have access to four specialized Quick Start Environments:

1. Python Starter Environment

This environment provides essential Python tools and libraries for general development work, perfect for both beginners and those who need a clean, lightweight environment.

This environment provides essential Python packages required for working in Jupyter. Perfect for when you want a guaranteed working environment but want to add your own packages as you work.  

What’s included: Core Python packages, basic data handling libraries, and fundamental development tools with minimal dependencies.

Use case example: Sarah, a data analyst transitioning from Excel to Python, uses the Python Starter environment to learn programming fundamentals without being overwhelmed by specialized packages. She can focus on mastering Python basics while having just enough tools for data manipulation and visualization, creating a smooth learning curve that builds her confidence.

2. Anaconda Distribution Environment

The comprehensive environment containing the full suite of Anaconda’s curated data science and machine learning libraries—equivalent to the classic Anaconda distribution.  The Swiss-Army knife of environments that can be used for everything from Data Visualization, to complex Machine Learning tasks.  

What’s included: NumPy, pandas, scipy, Matplotlib, Plotly, and hundreds of other data science packages, all pre-configured to work seamlessly together.

Use case example: Miguel, a seasoned data scientist working across multiple projects, relies on the Anaconda Distribution environment as his go-to workspace. When asked to quickly analyze a customer churn dataset, he doesn’t waste time installing packages—he simply launches this environment and immediately begins exploring data patterns, fitting models, and generating visualizations for stakeholders, all within minutes of receiving the data.

3. Finance Environment

A specialized environment optimized for financial analysis, modeling, and visualization with libraries specifically curated for financial applications.

What’s included: statsmodels, scikit-learn, quantstats and other specialized packages for time series analysis, risk modeling, portfolio optimization, and financial visualization alongside core data science tools.

Use case example: Mike, a quantitative analyst at an investment firm, uses the Finance environment to rapidly prototype trading strategies. Instead of spending hours configuring specific financial packages and resolving dependency conflicts, he can immediately leverage specialized tools like pandas-datareader for market data acquisition, statsmodels for time series analysis, and PyPortfolioOpt for allocation modeling—all pre-configured and tested together.

4. AI/ML Starter Environment

A focused collection of the most essential machine learning and AI development tools, optimized for performance and compatibility.

What’s included: TensorFlow or PyTorch (depending on configuration choice), along with fastai, supporting visualization tools, preprocessing libraries, and model evaluation frameworks.

Use case example: Kelly, an ML engineer, receives a request to build a prototype image classification model by the end of the day. Rather than spending valuable time configuring deep learning frameworks, she launches the AI/ML Starter environment and immediately begins adapting a convolutional neural network to her specific dataset. The pre-configured environment handles the complex dependencies between deep learning libraries, enabling her to focus purely on model architecture and training.

In the future we plan to expand this selection with additional specialized environments based on user feedback and industry needs.

Benefits for Individual Practitioners

The advantages for individual data scientists and developers are immediately apparent:

• Productivity Boost: Start working on actual problems immediately, rather than fighting with environment setup
• Reduced Frustration: Eliminate common dependency conflicts and version compatibility issues
• Best Practices Built-In: Benefit from expert package selection and configuration
• Focus on Core Work: Spend time on analysis and model development, not infrastructure

Benefits to an Organization

Beyond individual productivity gains, organizations realize several strategic advantages:

• Standardization: Ensure all team members work in consistent environments 
• Onboarding Efficiency: New team members are up and running in minutes rather than hours
• Reduced Support Overhead: Fewer environment-related support tickets for IT teams
• Foster Collaboration: Simplified workstreams when environments are consistent across teams

Part of the Anaconda AI Platform

Ready to eliminate environment headaches and focus on your actual data science work? Create an account or log in to the Anaconda AI Platform and start using our new quick start environments today!

Quick Start Environments are just one capability of the Anaconda AI Platform, which combines trusted distribution, simplified workflows, real-time insights, and governance controls that accelerate AI development with open source. Want to see a full demo of the platform? Reach out to our sales team.