Tag: BioPharma

Serverless Compute in 2023: Top Trends, Challenges & Adoption Patterns in AWS, Google Cloud and Azure

In the ever-evolving landscape of computing, serverless has undeniably established itself as a central pillar. The driving force behind this transition is the growing availability of serverless offerings from major cloud providers such as Amazon Web Services (AWS), Google Cloud, and Azure, along with emerging platforms like Vercel and Cloudflare.

This report provides a comprehensive analysis of how over 20,000 organizations are utilizing serverless technologies in their operations, exploring significant trends and insights drawn from real-world applications of this transformative technology.

Shift Toward Serverless Adoption

Significant growth has been observed in serverless adoption among organizations operating on Azure and Google Cloud, with AWS also showing positive development. For instance, 70% of the AWS customers and 60% of Google Cloud customers now use serverless solutions. Azure isn’t far behind, with 49% of its customers embracing serverless offerings.

This upswing can be attributed to the expanding suite of serverless tools, ranging from FaaS solutions to serverless edge computing, offered by these cloud providers to meet their customers’ unique needs.

The Rise of Container-Based Serverless Computing

Google Cloud, since its launch of Cloud Run in 2019, has led in fully managed container-based serverless adoption. However, this year AWS saw a rise to 26% of serverless organizations running containerized Lambda functions and AWS App Runner. Azure also experienced considerable year-over-year growth, propelled by the launch of Azure Container Apps.

Container-based serverless compute platforms are gaining traction as they facilitate serverless adoption and migration by enabling organizations to deploy existing container images as microservices. Apart from that, these platforms offer wider language support and larger application sizes.

Serverless Platforms: Beyond The Major Providers

While major providers dominate the serverless space, frontend development and Content Delivery Network (CDN) platforms like Vercel, Netlify, Cloudflare, and Fastly also equip developers with specialized serverless compute capabilities. Interestingly, 7% of organizations monitoring serverless workloads in a significant cloud are also running workloads on one or more of these emerging platforms.

Choice of Languages for AWS Lambda

Node.js and Python are the languages of choice for most AWS Lambda developers, with over half of invocations being written in these languages. The rising popularity of custom runtimes indicates a growing interest in serverless containers, which allow developers to work with languages not natively supported by Lambda.

The Challenge of Cold Starts

Cold starts, where a new execution environment is created to serve a request, remain a significant concern. This is especially true for Java-based Lambda functions, which showcase the longest cold start times due to the JVM and Java libraries’ loading time.

The Adoption of AWS Lambda on ARM

The usage of AWS Lambda on ARM has doubled in the past year, primarily due to its combined benefits of faster execution times and lower costs.

Deployment Tools for AWS Lambda

Infrastructure as Code (IaC) tools like the Serverless Framework and Terraform greatly simplify the deployment and configuration of Lambda functions and other resources. As organizations mature and scale, the preference for IaC tools shifts. Larger organizations positively inclined towards Terraform for multi-cloud support and flexibility.

Connection of AWS Lambdas to a Virtual Private Cloud (VPC)

The complexity of integrating serverless functions across the existing infrastructure has led many organizations to connect their Lambda functions directly to the VPCs. According to recent statistics, 65% of Datadog customers have at least five Lambda functions connected to a dedicated VPC in their AWS account.

Serverless technologies today are making developer’s lives easier by being more secure, cost-effective, flexible, and efficient. The prominence of serverless in modern application building is only expected to surge further in the coming years.

Tags: #Serverless #AWSLambda #GoogleCloud #Azure #Terraform #Containerization #VPC #Nodejs #Python #ARM

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Digital Transformation in Life Sciences: Rapid Innovation with AI and Data Trends in 2023

The life sciences industry is witnessing a continuous evolution that is increasingly driven by sophisticated digital technology. The advent of rapid innovation, specialized AI, and data usage is impacting every sector, from research and development to regulatory environments and even talent procurement.

The Speed of Innovation

In scientific and tech-savvy circles, a leading trend is noted – The pace of biopharma innovation. The merger of science and intelligent technology has significantly shortened the invention pipeline encouraging faster and comprehensive discovery and product development.

More and more biopharma executives (around 93%) are attributing this high-speed innovation to advancements in science technology. AI-led drug discovery saw a Compound Annual Growth Rate (CAGR) of 8% in 2021, and there was a cumulative investment of $2.5 billion in 2022. Biopharmaceuticals invested nearly $1 billion in upfront payments – a staggering number, indicating the potential value of this growing industry is estimated to reach $45 billion.

In the last five years, an increasing trend of Generative AI startups observed a growth of 840 million. This trajectory is a leading indicator that the biopharma industry is making non-traditional investments due to advances in GenAI and other tech innovations.

An Era of Tech Collaborations

The industry is experiencing an influx of collaborations between pharmaceutical companies and AI organizations. This shift has reduced development time and cost in the biopharmaceutical industry. For example, GenAI has expedited antibody discovery and fast-tracked development speed in the scientific laboratory environment.

“Zero-shot” GenAI is creating significant strides in the delivery of human-ready molecules such as antibodies, advancing the speed of development and increasing the success rate.

The Implications of Technological Advancements

Accenture’s report identifies three main areas of implications in the life sciences industry stemming from rapid technological development.

1. Technology and Data

The report highlights that handling big data across multiple resources can complexify AI and machine learning (ML) analytical strategies.

2. Organizational and Cultural Shifts

Biotech companies are now obligated to secure talent who can navigate the junction of science and technology. This necessity challenges the companies to amalgamate two radically different work cultures.

3. Regulatory Environment

As the industry evolves, so must the regulations governing it. As such, accommodations must be made within the regulatory frameworks to account for these rapid advancements.

The Power of Generalizing Artificial Intelligence (GenAI)

Beyond just technology, GenAI plays an instrumental part in the success of biopharma organizations. GenAI uses foundation and large language models (LLMs) and has drastically changed the life sciences industry impacting the value chain.

Biopharma executives expect GenAI to facilitate quicker decision-making (70%) and speed up innovation (55%). Other expectations include improved customer experiences (63%) and enhanced communication (60%).

Data: The Lifeblood of Innovation

Data, coupled with data analytics, holds a prominent place in research and development (R&D) processes. Increased scientific collaboration is expected due to improved generation and sharing of data. Managers and executives are now considering new strategies to manage and share data for a broader consortium.

Furthermore, data transparency was another area of focus. About 92% of biopharma executives emphasized the importance of data transparency in an increasingly competitive biotech world, with over 40% stating improved trust as a benefit of data transparency.

Navigating the Digital Identity

According to 90% of biopharma executives, creating a digital identity is a strategic business goal that will improve data sharing while aiding in the application of AI.

Adoption of Responsible AI Strategy

Adopting a responsible AI strategy is paramount in an organization’s journey towards digital transformation. A responsible AI strategy is not merely about setting up a governance board, but about considering a variety of factors like regulations, legal implications, talent, policies, and procedures. It is a continuous process and needs regular monitoring and revision.

As AI evolves, life sciences companies and industry leaders must be prepared to leverage the technology by developing flexible strategies that allow for regulation and incorporation into company practices.

In conclusion, the life sciences industry is on the cusp of a digital revolution that is creating new opportunities for those willing and able to adapt.

  • Tags: #BioPharma #GenAI #DigitalTrends #LifeSciences

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