Deep learning has well-known tools: TensorFlow, PyTorch, Pandas and others. These are all essential, day to day tools that help data scientists be more productive. Enterprise deep learning has more comprehensive platforms like Saturn Cloud.

The focus on building increasingly complex models to solve a range of business problems relies on experimentation and rapid iteration. As deep learning moves off the whiteboard and into production, a complete platform is needed to make that jump.

Execution is moving deep learning into the same framework as traditional software development. Full lifecycle tools give businesses control over that execution. Saturn Cloud manages model development to deployment and maintenance. A platform that handles deep learning from start to finish is the management piece that optimizes productivity at each stage.

Evaluating a data science platform; it’s all about asking the right questions:

  • How does the platform integrate with the tools the team’s already using?
  • What’s the learning curve and time to implementation?
  • What stages can the platform manage?
  • How versatile is it? Can it handle evolving business and team needs?

Each question is designed to help the business understand the cost of implementation, not just the platform’s cost. Saturn Cloud makes a strong case at all points of evaluation, not just on pricing. For a complete ROI calculation, investment needs to take pricing and cost of implementation into account. The needs of the business, outside of the data science team are also important. A structured evaluation process includes each point and piece of the business.

Saturn Cloud: Integrating With Model Development Tools

Deep learning models are built with open source libraries. Businesses already have a lot of money invested in deep learning model development so scrapping that foundation isn’t an option. The first point of platform evaluation looks at how much rework is required to start using the platform.

Saturn Cloud is completely integrated with Python. It supports the basics; Scikit-Learn, Pandas, NumPy, and SciPy. It also supports deep learning libraries like TensorFlow and PyTorch. Saturn Cloud facilitates and manages the Jupyter development environment. It integrates with Docker and data scientists can build a custom image to meet their development needs.

Deep learning requires Jupyter to run in more powerful environments. Saturn Cloud manages the environment resources allowing for more CPU/GPU and memory. A managed platform handling cloud resources keeps the environment costs low. Data scientists can focus on building, not managing the back end.

Saturn Cloud: Learning Curve

The learning curve is the biggest part of time to implementation for a data science platform. Frameworks like Spark integrate well with other tools but have a steep learning curve. Implementation can be an enterprise-level project adding to overall costs.

Saturn Cloud uses an open-source library called Dask. Just like Saturn Cloud, Dask is completely integrated with Python. Dask DataFrames are almost identical to Pandas DataFrames. With simple function decorations, a data scientist can create custom workflows using Dask’s delayed or futures functionality.

Dask allows models to optimize local resource usage. Without additional recoding, Saturn Cloud can scale model training and inference across distributed resources. Saturn Cloud leverages Dask to bring the learning curve down significantly.

Time to implementation is very low. There are simple environmental setup steps; creating a Docker image. Models require a minimal migration from Panda’s to Dask DataFrames and Python function decorations. At that point, Saturn Cloud is up and ready to run.

Saturn Cloud: Deep Learning Lifecycle Support

Deep learning needs a path to production. The third point of evaluation looks at the stages in the lifecycle the platform can manage. Once the use case and performance requirements are defined, data wrangling and model experimentation happens in Jupyter on Saturn Cloud.

Source control and data versioning are both managed. The model review can be completed by any team member. Model training and validation is reproducible, simplifying the process. Docker standardizes the environment to avoid variations from system to system.

Saturn Cloud manages scaling initial training and testing to handle complete model creation. Deployment is managed by the platform as well. Maintenance of existing models for periodic updates leverage Jupyter on Saturn Cloud. Scaling existing models for distributed environments is managed by adding Dask to the project then deploying to AWS using Saturn Cloud.

The platform provides management for each stage of the path to production.

Saturn Cloud: Versatility and Fit

Trying to forklift a solution into an existing team isn’t ideal. Current project deadlines are a consideration. What tools the team is currently using is another. The fourth point of evaluation deals with the practical execution of platform rollout.

The first piece of that is transitioning from existing tools to the new platform. Deep learning uses a diverse toolset and data scientists come into the team with their preferred set. Real-world teams often have several, overlapping tools with no real standardization.

Saturn Cloud can be deployed in phases. Individual team members can start using features when they make sense to adopt. Code migration can happen over time, project by project or as part of normal model maintenance.

As deep learning projects become more complex, the team can implement more features of Saturn Cloud. Deep learning resource requirements increase. Optimization needs apply to memory, CPU, and GPU resources. Distributed environments and parallelization are managed by Saturn Cloud. The workflow doesn’t change as resource needs increase.

Business cases evolve and the platform can make the transition from early, generic deep learning models to customized models simpler. Custom models don’t come with built-in optimization. Saturn Cloud uses Dask to keep the additional coding custom models need as minimal and straightforward as possible.

Moving Forward With A Saturn Cloud Implementation

A complete evaluation process gives the data science team a chance to get together on platform selection. Each team member takes away reasons to adopt Saturn Cloud. They have a set of expectations and an understanding of what the platform will do to make their job easier. There’s a clear outline of the benefits and steps to implementation.

Saturn Cloud offers a free trial and the team can get hands-on as part of the evaluation process. Data scientists have time to get used to the platform. There’s no uncertainty of how it will perform in the real world. All these elements keep the platform from being something that’s bought and then sits on the shelf unused.

The most important step to implementation is adoption. Saturn Cloud makes the decision simple and the implementation plan straightforward. Execution is low effort with minimal impacts on project timelines.

Saturn Cloud: Addressing Business Requirements

The business, beyond the data science team, has expectations and needs that the platform should address. Deep learning model development has two main barriers to returning value: uncertain, lengthy development timelines and a path to production.

Saturn Cloud manages the full model development lifecycle. The platform creates a structured, repeatable process. Dask helps implement model development best practices. Jupyter deployed on Saturn Cloud makes collaboration simpler.

The initial phases of the lifecycle are a heavy time sink if they’re done in silos. A model that lives on a data scientist’s laptop isn’t trackable or reviewable. It can be difficult for data science team leadership and project leadership to gauge progress. Saturn Cloud manages those phases to create more transparency.

DevOps and maintenance phases are also managed by the platform. Data scientists use Saturn Cloud to access distributed resources for training, reducing iteration and final model creation time. Saturn Cloud manages model deployment to AWS. The platform removes the barrier between model completion and model availability in a stable, scalable environment.

Deep learning projects are transparent and managed. Timelines are brought in by Saturn Cloud. Barriers to production are eliminated. Data scientists don’t spend time on the DevOps phases of the lifecycle. Time between projects is reduced because data scientists are focused on model development.

The business expects a return on investment and Saturn Cloud accounts for the wider needs as well as the data science team needs. The platform doesn’t force a choice between business and team requirements. Both can complete a platform evaluation and come to a consensus with Saturn Cloud.