Preloop: Automating ML Deployment to Unlock Scientific Potential

What is Preloop?

Preloop is a groundbreaking startup founded in 2023 with the mission to streamline the deployment of machine learning (ML) models from experimental scripts to production services. This innovative company addresses a critical bottleneck in the ML lifecycle, ensuring that a higher percentage of models transition from the experimentation phase to real-world applications. With a compact yet dynamic team, Preloop is led by co-founders Tejas Hosangadi and Nikith, who bring a wealth of experience and expertise to the table. By leveraging automation, Preloop enhances the efficiency and productivity of ML teams, enabling them to focus more on science and less on the engineering hurdles of deployment.

Why is Deployment a Bottleneck in ML?

In the rapidly evolving field of machine learning, the deployment of models often represents a significant hurdle. Data scientists and ML engineers can spend anywhere from a couple of weeks to over two months just on the deployment process. This translates to a substantial loss of valuable time that could be better spent on developing and refining new models. The primary issues include a lack of user-friendly tools and delays resulting from the need to hand off deployments to separate teams. Preloop aims to mitigate these challenges by automating the deployment process, thereby freeing up scientists to focus on their core work.

The deployment bottleneck is a well-known issue in the ML community. According to industry statistics, only 2 out of 10 ML models ever make it to production. This inefficiency stems from the extensive manual work required to deploy models, which can take weeks or even months. By automating this process, Preloop helps companies unlock more value from their ML teams, allowing scientists to dedicate more time to innovation and less to engineering tasks.

Who are the Founders of Preloop?

Preloop was co-founded by Tejas Hosangadi, who serves as the CEO, and Nikith, both of whom bring a wealth of experience and expertise to the startup. Tejas has a rich background in data science and software engineering. Before founding Preloop, he worked at Amazon, where he successfully scaled a data science team from scratch, delivering four significant projects in the first year alone. His leadership and vision were instrumental in the expansion of the team. At EvolutionIQ, Tejas made impactful improvements to a predictive model within his first month, which played a key role in securing a long-term contract with Prudential.

Nikith, the co-founder, has a strong background in software engineering, particularly in building multi-tenant distributed systems. His most recent experience at AWS on the networking team provided him with the technical skills and insights necessary to develop and scale complex systems. Together, Tejas and Nikith form a formidable team, combining their strengths to drive Preloop's mission of transforming ML model deployment.

What Problem Does Preloop Solve?

Preloop addresses a critical issue in the ML deployment process: the time-consuming and repetitive nature of transitioning models from experimentation to production. Data scientists and ML engineers often find themselves bogged down by the manual work required to deploy their models. This not only delays the deployment process but also diverts attention away from more valuable tasks, such as model development and experimentation.

The inefficiency in the deployment process is a significant pain point for many organizations. Assuming that a team deploys 10 models a year and each model takes an average of two weeks to deploy, this results in 20 weeks of science time spent solely on deployments. This is a considerable loss of productivity and potential innovation. Preloop aims to alleviate this burden by automating the deployment process, enabling teams to deploy models in a matter of hours rather than weeks. This not only accelerates the time-to-market for ML solutions but also allows scientists to focus on what they do best: advancing the field of machine learning.

How Does Preloop Automate ML Model Deployment?

Preloop offers a comprehensive solution that automates the tedious tasks associated with deploying ML models. The platform automatically translates ML training scripts into production services, handling the creation of both training pipelines and REST endpoints. This drastically reduces deployment times from weeks to just a few hours. Preloop's easy-to-use Command Line Interface (CLI) and dashboard allow users to monitor and track their models, ensuring seamless integration and management.

The process begins with Preloop scanning through the existing ML scripts to identify key information about the data, transformations, and models being trained. This information is then used to construct both the training and data pipelines, as well as the inference endpoints that serve the models. The platform also handles auto-scaling based on demand, ensuring optimal performance without manual intervention. Additionally, Preloop offers robust observability and versioning features, allowing users to monitor model performance and easily roll back unintended changes.

For teams with stringent security requirements, Preloop supports on-prem deployments, providing the flexibility and peace of mind needed to meet their security standards. By automating these processes, Preloop not only streamlines the deployment of ML models but also enhances the overall efficiency and productivity of ML teams.

What Features Does Preloop Offer?

Preloop boasts several key features designed to simplify the ML deployment process:

• Training and Inference Pipelines: Preloop scans existing scripts to identify crucial information about data, transformations, and models, using this data to construct both training and data pipelines. This automation eliminates the need for manual intervention, significantly reducing deployment times.
• Auto-Scaling: The platform handles auto-scaling based on demand, ensuring that the deployed models perform optimally without the need for constant monitoring and adjustment. This feature is particularly valuable for organizations dealing with fluctuating workloads.
• Observability and Versioning: Preloop offers robust observability and versioning features, allowing users to monitor model performance and track changes over time. This ensures that any unintended changes can be quickly identified and rolled back if necessary.
• Security and On-Prem Deployments: For teams with stringent security requirements, Preloop supports on-prem deployments. This provides the flexibility and peace of mind needed to meet security standards while still benefiting from the platform's automation capabilities.
• Easy-to-Use CLI and Dashboard: Preloop's user-friendly Command Line Interface (CLI) and dashboard make it easy for users to monitor and track their models. This ensures seamless integration and management, allowing teams to focus on developing and refining their models rather than getting bogged down by deployment logistics.

How Does Preloop Benefit ML Teams?

By automating the deployment process, Preloop significantly reduces the time and effort required to bring ML models to production. This enables ML teams to focus more on developing innovative models rather than getting bogged down by deployment logistics. The result is a more efficient workflow, faster time-to-market for ML solutions, and ultimately, greater business value derived from ML initiatives.

Preloop's automation capabilities allow ML teams to move faster and be more productive. By eliminating the need for manual intervention in the deployment process, Preloop frees up valuable time that can be better spent on model development and experimentation. This not only accelerates the development cycle but also enhances the overall quality and performance of the models being deployed.

In addition, Preloop's robust observability and versioning features provide ML teams with greater visibility into their models' performance. This ensures that any issues can be quickly identified and addressed, reducing the risk of errors and improving the overall reliability of the deployed models. By providing a more streamlined and efficient deployment process, Preloop helps organizations maximize the value of their ML investments and achieve better business outcomes.

What Inspired the Creation of Preloop?

The inspiration for Preloop came from Tejas's firsthand experience as a data scientist. He observed that while scientists enjoyed the creative process of experimenting and building models, they often dreaded the deployment phase. This pain point, coupled with the lack of automated deployment solutions, motivated Tejas and Nikith to create Preloop. Their goal was to unshackle scientists from the repetitive tasks of deployment, allowing them to focus on what they do best: innovating and advancing the field of machine learning.

Tejas's experience at Amazon and EvolutionIQ provided valuable insights into the challenges faced by ML teams. He witnessed firsthand the inefficiencies and frustrations associated with the deployment process, which often delayed the time-to-market for ML solutions. This inspired him to develop a solution that would streamline the deployment process and enable ML teams to move faster and be more productive. By automating the deployment process, Preloop empowers scientists to focus on innovation and experimentation, driving the advancement of machine learning.

How is Preloop Positioned in the Market?

Preloop positions itself as the "Vercel for model deployments," highlighting its focus on simplifying and accelerating the deployment process. Unlike other tools that require extensive boilerplate code, Preloop offers a streamlined and user-friendly solution. Its compatibility with major ML packages like xgboost, torch, and scikit-learn, along with plans for broader support, makes it a versatile choice for ML teams across various industries.

The market for ML deployment solutions is growing rapidly, driven by the increasing adoption of machine learning across various industries. Preloop's unique value proposition and automation capabilities position it as a leader in this space. By providing a more efficient and streamlined deployment process, Preloop helps organizations maximize the value of their ML investments and achieve better business outcomes.

What is the Future of Preloop?

Looking ahead, Preloop aims to continue expanding its feature set and improving its platform to meet the evolving needs of ML teams. The company is committed to enhancing its automation capabilities, adding support for more ML packages, and improving its user interface to provide an even more seamless and user-friendly experience. With a strong foundation and a clear vision, Preloop is poised to become a leader in the ML deployment space, empowering scientists to drive innovation without the constraints of deployment challenges.

As the demand for efficient and automated ML solutions grows, Preloop's role in the industry will become increasingly vital. The company plans to expand its offerings to include more advanced features, such as support for additional ML frameworks, improved observability and monitoring capabilities, and enhanced security features for on-prem deployments. By continuously innovating and adapting to the needs of

the market, Preloop aims to remain at the forefront of the ML deployment space, helping organizations achieve their machine learning goals more efficiently and effectively.

In conclusion, Preloop is revolutionizing the ML deployment process by automating the transition from experimental scripts to production services. With a strong team, a clear vision, and a commitment to innovation, Preloop is poised to become a leader in the ML deployment space, empowering scientists to focus on what they do best: advancing the field of machine learning.