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Modern fields of science and engineering are driven by the hypothesis method. This method involves careful observation and the application of rigorous skepticism about what is observed. It also involves formulating hypotheses based on such observations, experimental and measurement-based testing of deductions drawn from the hypotheses, and refinement, or elimination, of the hypotheses based on the experimental findings. [^1]

In modern research & development (R\&D) projects, researchers follow the Parsimony Principle, also known as the Rule of Simplicity. The Parsimony Principle states that the hypothesis should be as simple as possible but not simpler. As a result, science and engineering are driven by a mode of inquiry that focuses on identifying a relatively small number of causal drivers of underlying phenomena built upon an underlying theory. Modern companies and research academia, consequently, adopt this mindset when developing and launching projects focused on innovation.

Traditional innovation is broken.

1.  Modern innovation requires an engineering talent pool. The labor costs quickly add up as companies have to write the algorithms and maintain it over time.

2.  Automation-oriented innovation requires if-then application development. As business logic is written in code, technical debt quickly adds to the overhead of managing modern software projects.

3.  Software-driven innovation is unable to deal with the changing volume, velocity, and variety of data. Companies are able to store data, but they are not making insights and predictions on that data without involving algorithms or specific technologies.

4.  While R\&D investments are increasing, R\&D returns are decreasing. [^2]

5.  The vast majority of innovation projects fail. [^3]

To catalyze innovation, companies have invested billions in internal venture capital, incubators, accelerators, and field trips to Silicon Valley. Yet according to a McKinsey survey, 94% of executives are dissatisfied with their firms’ innovation performance. [^4] Across industries, one survey after another has found the same thing: Businesses just aren’t getting the impact they want, despite all their spending. Fortunately, it’s possible to hack innovation.

The arrival of deep learning technologies introduces a new research paradigm - using deep learning as a general-purpose method of innovation. Deep learning offers an alternative paradigm based on the ability to predict complex phenomena. Deep learning models abstract away from writing algorithms and instead focus on gathering data. Deep learning technologies do not require domain experts. Instead, deep learning or neural networks force companies to focus on data gathering and data management. In other words, deep learning is an innovation in the method of innovation.

What are the advantages of using deep learning technologies for innovation management and maintenance?

Deep learning allows Fortune 500 companies and global enterprises to:

1.  Focus on fixed-cost investments in AI instead of highly skilled labor

2.  Improve performance in existing research projects

3.  Reduce the level of technical knowledge required

4.  Derive new insights

5.  Predict complex multi-causal phenomena [^5]

Deep learning-driven innovation is the only way companies can survive in the modern competitive landscape.

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References:

[^1]: Wikipedia Contributors (2020). Scientific method. \[online] Wikipedia. Available at: https://en.wikipedia.org/wiki/Scientific_method \[Accessed 2 Mar. 2020].
[^2]: Capgemini (2015). *The Innovation Game: Why & How Businesses are Investing in Innovation…*. \[online] Slideshare.net. Available at: https://www.slideshare.net/capgemini/the-innovation-game-why-how-businesses-are-investing-in-innovation-c \[Accessed 2 Mar. 2020].
[^3]: Capgemini Worldwide. (2015). Capgemini Consulting and Altimeter global report reveals leading businesses continue to struggle with innovation, with traditional R\&D model ‘broken.’ \[online] Available at: https://www.capgemini.com/news/capgemini-consulting-and-altimeter-global-report-reveals-leading-businesses-continue-to/ \[Accessed 2 Mar. 2020].
[^4]: McKinsey & Company. (2020). Leadership and innovation. \[online] Available at: https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/leadership-and-innovation \[Accessed 2 Mar. 2020].
[^5]: Cockburn, Iain M, Henderson, R. and Stern, S. (2018). The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis. NBER, \[online] pp.115–146. Available at: https://www.nber.org/chapters/c14006 \[Accessed 2 Mar. 2020].


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Samir Ibrahim, Founder/CEO at SunCulture, shares why farmers must rethink traditional farming processes and how to improve operations using the latest technologies.

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At [Produvia](http://produvia.com), we produce intelligent software. We also write letters about artificial intelligence (AI) to founders, executives, and decision-makers from all industries. These letters are meant to inspire and motivate companies, government agencies, and countries on the topics of AI, machine learning, and deep learning technologies.

At [Produvia](http://produvia.com), we believe that artificial intelligence technologies will fundamentally change how genomics, biotechnology, and life sciences startups and companies turn data into actionable insights.

Before we talk about artificial intelligence, it is important to understand the genomics industry first.

### Genomics Industry

The global genomics industry is worth $16.4 Billion USD as of 2018 and is expected to reach $41.2 Billion USD by 2025. The genomics industry consists of genomic products and services. The genomic products are expected to dominate the market due to the recurrent use of instruments and reagents for genomics research and the rising number of research programs undertaken by government and private organizations. The genomics services include next-generation sequencing, core genomics, biomarker translations, and many others. \[1]

According to AngelList, there are 160+ genomic; 4,228+ biotechnology; 9,893+ life sciences; and 4,893,827+ startups around the world \[2-5]. In other words, genomic startups represent about 4 percent of the biotechnology industry, 2 percent of the life sciences industry, and 3/1000 percent of all startups.

Today, the genomics industry is booming thanks to the increasing amount of data. Genomics data in the next 10 years is projected to equal and surpass other data-intensive disciplines including social media and online videos. \[6]

### Artificial Intelligence in Genomics

At [Produvia](http://produvia.com), we predict that genomic startups that combine deep learning, computer vision, and natural language processing technologies will establish a competitive edge in the marketplace.

Deep learning, a sub-field of artificial intelligence, is combined with computer vision techniques to analyze the growing amount of genomics imagery data. In computer vision, deep learning algorithms that excel include convolutional neural networks and recurrent neural networks. These machine learning models are solving computer vision tasks such as image classification, semantic segmentation, and image retrieval.

Deep learning is also combined with natural language processing techniques to analyze the expanding amount of genomics-related text found in publically-available research papers. Deep neural networks are solving tasks such as named entity recognition, relation extraction, and information retrieval. Deep learning technologies are ideally suited to deal with natural language processing tasks since they offer state-of-the-art performance and overcome challenges with feature engineering.

At [Produvia](http://produvia.com), we recognize the complexity of artificial intelligence in its applications in the genomics industry. As a result, we wrote this article as a guide for any stakeholders including patients, research participants, public, providers, researchers, advocacy groups, payers, and policymakers.

### How AI and Genomics Will Save The Planet

In 2015, the United Nations (UN) set seventeen Global Goals, also known as Sustainable Development Goals (SDGs). The SDGs were adopted by all UN Member States as a universal call to action end poverty, protect the planet and ensure that all people enjoy peace and prosperity by 2030. \[6]

Of the seventeen SDGs, the [Produvia](http://produvia.com) team identified five goals that can be solved with genomics and artificial intelligence technologies.

#### **AI Goal #1: No Poverty**

Can we really end poverty? Can we grow the middle class? These are really hard questions to answer. Satellite imagery was combined with machine learning to predict poverty \[7]. Poverty has been linked to disease, chronic illness, childhood obesity, elevated blood lead levels, academic achievements, and DNA methylation \[8-12]. How can machine learning help with these genomic causations or correlations? If we can predict disease or DNA methylation across genes, we can take preventative action in the fight against poverty.

#### **AI Goal #2: Zero Hunger**

How can humanity end hunger? Can we achieve a stable food supply? Can we end hidden hunger, also known as micronutrient deficiency? Certain hormones that regulate hunger and satiety \[13]. Hunger can be detected in crying infants using deep learning \[14]. Analyzing how people eat or their consumption patterns can reveal hidden hunger or gaps in micronutrient deficiency. Can people improve nutrition and promote sustainable agriculture? To answer these questions, consider that plant breeding and other agricultural technologies are greatly improved using machine learning. Increasing crop yield production will close the gap between crop output and hunger. Genetically improving cultivars and improving agronomic practices is one way to increase crop productivity \[15]. If we make agricultural more productive, we can reduce world hunger.

#### **AI Goal #3: Good Health and Well-Being**

Can we live a healthier life? Can we promote the well-being of all humanity? Better detection of AIDS, tuberculosis, malaria and neglected tropical diseases are now possible thanks to deep learning. Imagine being able to create personalized genomic profiles of each person on earth. This will allow us to predict the outbreak of diseases knowing where the susceptibility lies. Humanity has the potential to edit human reproduction. With gene editing, we can create the next generation of humans, which are immune to the latest diseases and typical health conditions. Combing gene editing with machine learning will allow humanity to achieve customized genetic and genomic profiles of individuals. If we can better understand how the aging process affects health and longevity, we can create healthier societies. Today, we can use deep learning to detect changes in biomarkers (i.e., physiological variables, composite indices) using data from longitudinal studies.

#### **AI Goal #4: Life Below Water**

Can we conserve ocean life? Can humanity use the oceans, seas and marine resources sustainably? Genomics and machine learning can solve many problems to ensure the continuation of life below water. For example, we can classify ocean acidity to reduce declining fish stock. We can apply conservation genomics with deep learning technologies, to predict the biodiversity of living organisms. Can we improve our aquaculture? Over the past few decades, advancements in agricultural biotechnology have changed the way research is analyzed. Today, genomic data using is analyzed using a variety of computational tools including machine learning or deep learning.

#### **AI Goal #5: Life on Land**

Can we protect our ecosystem? Can we restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification? Lastly, can humanity halt and reverse land degradation and halt biodiversity loss? Understanding complex ecosystems and how genes are affected by the environment is possible thanks to machine learning technologies. Deep learning can meet genome-scale metabolic modeling \[16]. Machine learning technologies have demonstrated the ability to analyze large, complex biological data. Furthermore, the massive and rapid advancements in both biological data generation and machine learning methodologies are promising for further understanding of genomics and biological data. It’s now possible to classify microbial roles in ecosystems using deep learning \[17]. Genomic tools, such as population genomics, meta-omics, and genome editing, can also restore ecosystems and biodiversity. Meta-omics can improve the assessment and monitoring of restoration outcomes. Gene editing can generate novel genotypes for restoring challenging environments. Using machine learning to analyze population genomics, meta-omics, and genome editing data will aid companies in developing solutions to improve life on earth.

### AI Research in Genomics

Artificial intelligence research is driving technological breakthroughs all industry verticals, genomics included. Reading academic papers takes time and the technical language is not easy to understand. At [Produvia](http://produvia.com), on the other hand, we keep up-to-date with the latest academic research papers so you don’t have to. Below, we highlight 20 AI and machine learning use cases for genomics \[18-26]:

#### Genomics

Genomics is an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes. Here are five AI and machine learning applications for genomics:

1.  Extract genomic and epigenomic variants of clinical utility

2.  Identify genes

3.  Predict genomic associations

4.  Predict protein functions

5.  Predict sequence the specificity of DNA and RNA-binding proteins

#### **Regulatory Genomics**

Regulatory genomics is the study of genomic regions or features and how they regulate genes. At [Produvia](http://produvia.com), we list five AI and machine learning applications for regulatory genomics:

1.  Classify gene expression

2.  Predict gene expression from genotype

3.  Predict promoters and enhancers

4.  Predict splicing

5.  Predict transcription factors and RNA-binding proteins

#### Functional Genomics

The field of molecular biology that attempts to describe gene functions and interactions is functional genomics. Here are five AI applications for functional genomics:

1.  Classify mutations and functional activities

2.  Classify subcellular localization

3.  Predict promoters and enhancers

4.  Predict splicing

5.  Predict transcription factors and RNA-binding proteins

#### Structural Genomics

Structural genomics is the field of genomics that involves the characterization of genome structures. At [Produvia](http://produvia.com), we list five AI and machine learning applications for structural genomics:

1.  Classify protein tertiary structures

2.  Classify structures of proteins

3.  Predict contact maps

4.  Predict physical properties

5.  Predict protein secondary structures

### AI Ideas for Genomics

You’re interested in artificial intelligence and machine learning, but don’t know where to start. At [Produvia](http://produvia.com), we brainstormed several ideas for the application of artificial intelligence technologies in genomics. Here are thirty-five AI ideas for genomics:

1.  Annotate genes based on structure and chromosomes

2.  Classify cancer from gene expression profiles

3.  Classify genes

4.  Classify genomic profiles

5.  Classify mutation types

6.  Design targetted therapies

7.  Detect deoxyribonucleic acid regions that are predictive of gene expression

8.  Determine relationships between genotypes and phenotype

9.  Discover drugs for genomic medicine

10. Distinguish between cancer and adenoma

11. Estimate prevalence for chromatin marks

12. Extract transcriptome patterns

13. Identify biomarkers for a disease

14. Identify enhancers

15. Identify pairwise variable associations between genomic data types

16. Identify positioned nucleosomes

17. Identify potentially valuable disease biomarkers

18. Identify promoters

19. Identify subtype of breast cancer tumor

20. Identify transcription factor binding sites

21. Identify transcription start sites, splice sites, exons

22. Interpret regulatory control in single cells

23. Model regulatory elements

24. Partition and label the genome with chromatin state annotation

25. Predict chromatin marks from deoxyribonucleic acid sequences

26. Predict disease phenotype or prognosis

27. Predict gene function

28. Predict genetic interactions

29. Predict protein backbones from protein sequences

30. Predict regulatory functions and relationships

31. Predict sequence the specificity of enhancer and cis-regulatory regions

32. Predict the specificities of deoxyribonucleic acid-binding and ribonucleic acid-binding proteins

33. Predict the splicing activity of individual exons

34. Predict variant deleteriousness

35. Quantify effects of single nucleotide variants on chromatin accessibility

### Challenges and Opportunities in Genomics

The use of artificial intelligence technologies to solve genomics problems poses many challenges. These industry challenges also present opportunities for AI technology providers, such as [Produvia](https://produvia.com/), to solve market problems and create AI solutions. Below, we list three genomics opportunities:

1.  Generating ground-truth labels or genomics datasets can be expensive

2.  “Right to an explanation” laws must be addressed

3.  Longitudinal studies are required

How can AI companies overcome these challenges? At [Produvia](http://produvia.com), we believe that industry collaboration will overcome Challenge #1, algorithmic transparency will overcome Challenge #2, and long-term research projects will overcome Challenge #3.

### Conclusion

The combination of artificial intelligence technologies and genomics has the potential to end poverty, end hunger, protect, restore and promote aquatic and terrestrial ecosystems.

### Next Step

Are you interested in solving genomics problems?

[Schedule a discovery call](/start) with Slava Kurilyak, Founder/CEO at Produvia.

Slava Kurilyak helps purpose-driven organizations to increase revenue and decrease expenses by developing artificial intelligence solutions that drive impact.

At Produvia, we serve companies with $1+ million dollars in revenue to accelerate the development of artificial intelligence technologies.

### References

1.  Research, Z. (2019). Global Genomics Market Will Reach USD 41.2 Billion By 2025: Zion Market Research. GlobeNewswire News Room. Retrieved 1 September 2019, from <https://www.globenewswire.com/news-release/2019/04/10/1801776/0/en/Global-Genomics-Market-Will-Reach-USD-41-2-Billion-By-2025-Zion-Market-Research.html>

2.  Genomics Startups. (2019, October 26). Retrieved October 26, 2019, from AngelList website: <https://angel.co/genomics-2>

3.  Biotechnology Startups. (2019, October 26). Retrieved October 26, 2019, from AngelList website: <https://angel.co/biotechnology>

4.  Life Sciences Startups. (2019, October 26). Retrieved October 26, 2019, from AngelList website: <https://angel.co/life-sciences>

5.  All Startups Startups. (2019, October 26). Retrieved October 26, 2019, from AngelList website:<https://angel.co/all-markets>

6.  dpicampaigns. (2018). About the Sustainable Development Goals — United Nations Sustainable Development. Retrieved October 26, 2019, from United Nations Sustainable Development website: <https://www.un.org/sustainabledevelopment/sustainable-development-goals/>

7.  Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. *Science*, *353*(6301), 790–794. <https://doi.org/10.1126/science.aaf7894>

8.  Global genomics disparities in the wake of personalised medical services: International Journal of Medical Engineering and Informatics: Vol 1, No 4. (2009). Retrieved October 27, 2019, from International Journal of Medical Engineering and Informatics website: <https://www.inderscienceonline.com/doi/abs/10.1504/IJMEI.2009.026812>

9.  Newacheck, P. W. (1994). Poverty and Childhood Chronic Illness. *Archives of Pediatrics & Adolescent Medicine*, *148*(11), 1143. <https://doi.org/10.1001/archpedi.1994.02170110029005>

10. Chokshi, D. A. (2018). Income, Poverty, and Health Inequality. *JAMA*, *319*(13), 1312. <https://doi.org/10.1001/jama.2018.2521>

11. Wexler, B. E., Imal, Ahmet Esat, Pittman, B., & Bell, M. D. (2019). Executive Function Deficits Mediate Effects of Poverty on Academic Achievement: An Important Target for Interventions to Enhance Neurocognitive Development in At-Risk Children. Retrieved October 27, 2019, from Ssrn.com website: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3369774>

12. Poverty leaves a mark on our genes. (2019). Retrieved October 26, 2019, from Northwestern.edu website: <https://news.northwestern.edu/stories/2019/04/poverty-leaves-a-mark-on-our-genes/>

13. Mesmar, B., & Steinle, N. (2020). Genomics of Eating Behavior and Appetite Regulation. *Principles of Nutrigenetics and Nutrigenomics*, 159–165. <https://doi.org/10.1016/b978-0-12-804572-5.00020-3>

14. Barajas-Montiel, S. E., & Reyes-Garcia, C. A. (2019). Identifying Pain and Hunger in Infant Cry with Classifiers Ensembles. *International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06)*. <https://doi.org/10.1109/cimca.2005.1631561>

15. Borrill, P., Harrington, S. A., & Uauy, C. (2018). Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat. *The Plant Journal*. <https://doi.org/10.1111/tpj.14150>

16. Zampieri, G., Vijayakumar, S., Yaneske, E., & Angione, C. (2019). Machine and deep learning meet genome-scale metabolic modeling. *PLOS Computational Biology*, *15*(7), e1007084. <https://doi.org/10.1371/journal.pcbi.1007084>

17. Handley, K. M. (2019). Determining Microbial Roles in Ecosystem Function: Redefining Microbial Food Webs and Transcending Kingdom Barriers. *MSystems*, *4*(3). <https://doi.org/10.1128/msystems.00153-19>

18. Akdemir, D. (2013). Locally epistatic genomic relationship matrices for genomic association, prediction and selection. arXiv.org. Retrieved 18 September 2019, from <https://arxiv.org/abs/1302.3463>

19. Hoadley, E. (2011). Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. ArXiv E-Prints, arXiv:1102.4110. Retrieved from <https://ui.adsabs.harvard.edu/abs/2011arXiv1102.4110L/abstract>

20. Wikipedia Contributors. (2019, October 23). Sustainable Development Goals. Retrieved October 26, 2019, from Wikipedia website: <https://en.wikipedia.org/wiki/Sustainable_Development_Goals>

21. Deep Learning in Medical Image Analysis. (2019). [@AnnualReviews](http://twitter.com/AnnualReviews). Retrieved 7 October 2019, from <https://www.annualreviews.org/doi/10.1146/annurev-bioeng-071516-044442>

22. SDGs .:. Sustainable Development Knowledge Platform. (2015). Retrieved October 26, 2019, from Un.org website: <https://sustainabledevelopment.un.org/topics/sustainabledevelopmentgoals>

23. Deep learning for genomics. (2018). Nature Genetics, 51(1), 1–1. doi:10.1038/s41588–018–0328–0

24. Xiong, M., & Ma, L. (2013). An Efficient Sufficient Dimension Reduction Method for Identifying Genetic Variants of Clinical Significance. arXiv.org. Retrieved 18 September 2019, from <https://arxiv.org/abs/1301.3528>

25. Kwak, G. H.-J., & Hui, P. (2019). DeepHealth: Deep Learning for Health Informatics. Retrieved October 28, 2019, from arXiv.org website: <https://arxiv.org/abs/1909.00384>

26. Dinalankara, W., & Bravo, H. (2013). Anomaly Classification with the Anti-Profile Support Vector Machine. arXiv.org. Retrieved 18 September 2019, from <https://arxiv.org/abs/1301.3514>


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# Introduction

Developing machine learning systems is painfully slow. Machine learning (ML) models take months to develop and deploy. By the time ML models are deployed into production, development velocity is at a crawl. Let's understand why. 

# Background

Developing ML systems is a complex endeavour. Machine learning practitioners (researchers, developers, engineers, data scientists) are called in to save the day. Yet despite best intentions, machine learning teams struggle to operationalize and productionize models. ML systems in production are influenced by four factors: developer velocity, complexity, technical debt, and workflows.

## Machine Learning Developer Velocity

As developers write "crappy code", the development velocity decreases over time. Developer velocity is a measure of how quickly ML developers ship code. This metric captures how much time is spent doing a task or completing a feature. During research or prototyping stages, the development velocity of ML practitioners is high. In the prototyping stage, developers focus on machine learning code. As ML projects shift into production, the development velocity decreases. In production, developers spend most of their time on non-ML code. 

ML practitioners often take months to deploy ML models into production. In October 2019, [Algorithmia](https://info.algorithmia.com/2020) conducted a study about the state of machine learning. Out of 745 respondents, 40% of companies took more than a month to deploy a machine learning model into production, 28% of respondents taking eight to 30 days, and only 14% spent seven days or less.

## Machine Learning Complexity

In the research or prototype stage, most ML models are simple, with core functionality, basic configuration, and data collection code. As ML practitioners are tasked to deploy their models online, they turn to crappy code to glue everything together.

In the production stage, most ML system are complex, requiring "glue code" or supporting code written to get data into and out of general-purpose ML packages and frameworks ([Tensorflow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), [Keras](https://keras.io/)). According to [Google](http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems), a "mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code." As ML practitioners deploy ML models into production, they are faced with new challenges that extend beyond ML code, including configuration, data collection, feature extraction, data verification, ML management, analysis tools, process management tools, serving infrastructure, and monitoring. 

![gvizcw](https://slava-kurilyak.s3.us-west-1.amazonaws.com/notebook/gvizcw.jpg)

## Machine Learning Technical Debt and Workflows

Companies face two major challenges when developing machine learning models: technical debt and overly complex machine learning workflows. The first issue is technical debt, which comes in many forms: bad architecture choices, overly complex code, and lack of code documentation. The second issue is the complex ML workflows. Traditional ML workflows require explicit resource management and over-provisioning. 

ML complexity leads to over-provisioning and impacts development speed. The typical ML workflow consists of many steps, including, but not limited to, preprocessing, training, and tuning. This complexity makes it challenging for ML practitioners to correctly provision and manage resources. This complexity also constitutes a significant burden that frequently causes over-provisioning and impairs user productivity and developer velocity.

As ML practitioners move from prototype to production, they accumulate technical debt. Here are top reasons for technical debt: bad architecture choices, overfly complex code, lack of code documentation, inadequate testing, obsolete technology, according to [Neil Ernst](https://insights.sei.cmu.edu/sei_blog/2015/07/a-field-study-of-technical-debt.html).

![img](https://insights.sei.cmu.edu/assets/content/chart3_ranking_sources_technicaldebt.png)

# Solution

The development velocity should remain constant throughout the entire machine learning project. The only way to achieve high velocity is to use serverless architectures, which allow ML practitioners to focus on code, not serving infrastructure. Serverless computing provides managed infrastructure and automatic scaling. This allows ML practitioners to solve business problems, not manage servers.

> Use serverless architectures to achieve high machine learning developer velocity!

Machine learning developers have a choice: keep writing crappy code or stop writing crappy code (see Figure below). This choice is the difference between using serverful architecture ([AWS EC2](https://aws.amazon.com/ec2/)) or serverless ([AWS Lambda](https://aws.amazon.com/lambda/)).

![img](https://i0.wp.com/blog.crisp.se/wp-content/uploads/2013/07/TechnicalDebt-StopWritingCrap-vs-KeepWritingCrap.png)

*Figure: Velocity vs Time ([Crisp](https://blog.crisp.se/2013/07/12/henrikkniberg/the-solution-to-technical-debt))*

## Company Benefits

How do companies benefit from adopting serverless machine learning? Companies that leverage serverless architectures move faster than their competition. Serverless eliminates manual scaling, simplifies workflows, and increases velocity for ML teams and practitioners. 

### Reduced Maintenance Costs

Companies must understand the [real cost of machine learning systems](https://produvia.com/posts/real-costs-of-machine-learning-systems/) in production. The biggest cost of maintaining existing ML models in production is the opportunity cost of developing new models. The more time is spent on maintaining or managing machine learning models in production, the less time is spent on developing new models that can impact business revenue and growth.

Using serverless computing, companies can save money on infrastructure thanks to the pay-per-use model. If no one is using your machine learning model, you are not paying for the model. If one million people visit your model, you pay per invocations.

### Automatic Scaling

As companies or products are featured online ([ProductHunt](https://www.producthunt.com/), [Reddit](https://reddit.com/), [Hacker News](https://news.ycombinator.com/)) or by traditional media, servers are often overloaded with new visitors. The unexpected surge of demand often takes down servers and customers are faced with 404 or server not responding errors. With serverless, companies can save face. With serverless, companies automatically scale to accommodate demand. 

### Competitive Advantage

Startups, companies, and organizations are in a race against time. This race is won by the companies that implement features into their products or services. Companies that thrive are those who develop faster. Companies that thrive are those who leverage serverless architectures, stop worrying about operations, and focus on ML development.

## Developer Benefits

How do developers benefit from adopting serverless machine learning? ML teams that leverage serverless architectures crush developer productivity. Serverless greatly reduces technical debt, simplifies workflows, and increases velocity for ML teams and practitioners.

### Low Technical Debt

Machine learning teams reduce technical debt thanks to opinionated serverless architecture, simplified code, and low cost. As companies write more code, they accumulate more technical debt. With serverless, technical debt is greatly reduced since the cloud provider takes care of the server infrastructure. With serverless, technical debt is managed by cloud vendors ([Amazon Web Services](https://aws.amazon.com/), [Google Cloud](https://cloud.google.com/), [Microsoft Azure](https://azure.microsoft.com/)) which have large development and operations teams. Serverless architectures allow ML practitioners to write less code and have less technical debt compared to traditional ML teams.

### Simplified Machine Learning Workflows

Thanks to serverless, ML teams leverage managed infrastructure, automatic scaling, availability, and fault-tolerance. As companies write glue code, they introduce complexity. With serverless, glue code is greatly reduced since cloud functions and cloud storage takes care of serving infrastructure. Serverless architectures allow ML practitioners to not worry about servers and instead focus on core ML code compared to traditional ML teams. 

### High Developer Velocity

As companies build their technology, the maintenance of software or management of technical debt becomes an issue. If developers are able to maintain high development speed or velocity, they are able to compete against other companies and reach the market faster.

# Conclusion

Serverless machine learning allows developers to improve productivity, simplify workflows, and reduce code complexity. Serverless ML allows companies to deploy ML models with reduced maintenance costs, automatic scaling, and competitive advantage.

> Modern machine learning teams use serverless architectures to ship faster.

### Next Step

At Produvia, we partner with $1+ million-dollar companies to accelerate the adoption of artificial intelligence technologies.

Are you interested in solving genomics problems?

[Schedule a discovery call](/start) with Slava Kurilyak, Founder/CEO at Produvia. 

Machine Learning Developer Velocity

Let's talk about "full organizational autonomy".

Have you ever stopped to think about what organizations might look like once people are no longer required to manage or operate them? The organizational constraints imposed by the human desire to create offices have been the prevalent mindset of most businesses for over a century. With the desire to reach full autonomy, organizations are rethinking office spaces and focusing their time on automation, software, and robots.

Organizations have been designed around the way people communicate and make decisions. With the rise of remote work, the cost of communication dropping to zero, and the explosion of machine learning models, assumptions about the role of organizations are now starting to be questioned. With the spread of COVID-19, many organizations are asking themselves: "Is there a better way?"

At Produvia, we believe modern companies must aim to achieve full organizational autonomy not only to survive but also to thrive in the post-COVID world.

Traditional organizations are based on social status. Centralized organizational structures, hiring human workers, and physical spaces are the norm. Decentralized organizational structures, hiring robot agents, and digital workspaces are the new norm of modern companies.

What are "autonomous organizations"?

Autonomous organizations are legal entities that are operating autonomously. Autonomous companies require less supervision and oversight than traditional companies based on human teams. Autonomous organizations are able to make decisions and react in real-time based on changing market conditions. The speed of decision making is powered by artificial intelligence and blockchain technologies.

Imagine a car is an organization. Taylor Pearson describes this concept in [The Blockchain Man](https://www.ribbonfarm.com/2017/10/10/the-blockchain-man/):

> "What happens when you mash blockchains, Uber and Self-driving cars together? The self-owning car. A car that pays for its lease, its insurance, and its gas, by giving people rides. A car that is not owned by a corporation. It is a corporation. The car exists as an autonomous financial entity, potentially with no human ownership."

Why do companies want full autonomy?

Modern companies want to generate revenue, maximize shareholder value, and crush their competition. To achieve these business goals, businesses must use the latest technologies to be more autonomous.

At Produvia, we propose there six levels of organizational autonomy:

### Level 0 - No Automation

*   Human teams: human attention or interaction is required.

*   Manual control. The human worker performs all tasks.

*   Every action and decision in the business is performed by a human agent.

### Level 1 - Worker Assistance

*   Human-robot teams.

*   The organization uses software to augment the human agent.

*   Business is controlled and operated by human coworkers, but certain functions performed with the aid of robots or software.

### Level 2 - Partial Automation

*   The organization uses software to achieve automation.

*   People still monitor all tasks and can take control at any time.

*   Business is still controlled and operated by human workers.

### Level 3 - Conditional Automation

*   The organization can perform most tasks, but the human worker override is still required.

*   Business is guided by people.

*   Robotic agents perform most of the operational activities.

### Level 4 - High Automation

*   The organization performs all tasks under specific circumstances. A human agent override is still an option.

*   Full autonomy of the business exists, but only in certain, restricted problem spaces.

*   Robotic agents perform most, if not all, operational activities.

### Level 5 - Full Automation

*   Robot teams: zero human attention or interaction is required.

*   The organization performs all tasks under all conditions.

*   Robot workers have completely replaced the need for human workers across all business domains or functions.

Most organizations are still operating under Level 1 (Worker Assistance) or Level 2 (Partial Automation). Some organizations operate under Level 3 (Conditional Automation) or Level 4 (High Automation). Modern organizations strive to achieve Level 5 (Full Automation). At Level 5, the organization has reached nirvana, operating with one or no principals or owners.

Companies are transitioning from no automation to worker assistance, partial automation, conditional automation, high automation, and full automation. Companies are transitioning from human-only teams to human-robot teams and robot-only teams.

To reach full autonomy, organizations must accept robotic coworkers and embrace robotic autonomy. Modern organizations must use artificial intelligence and distributed ledger technologies to be more autonomous.

### Next Step

Most companies struggle to reach full autonomy. At Produvia, we develop artificial intelligence technologies so organizations operate more autonomously.

Are you interested in organizational autonomy? Schedule a [discovery call](https://produvia.com/) with Produvia.

#### Notes

This post was inspired by Matt Mullenweg's [Five Levels of Autonomy](https://ma.tt/2020/04/five-levels-of-autonomy/) and Craig Armour's [Six Levels of Autonomous Organizations](https://www.kcarmour.com.au/the-six-levels-of-autonomous-organisations/) and Society of Automotive Engineers (SAE)'s [Levels of Driving Automation](https://www.sae.org/standards/content/j3016\_201806/).


What are the six levels of organizational autonomy?

Organizational Levels of Autonomy

Interested to work with the Produvia team?

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Vivamus tempus ornare urna, eget imperdiet quam venenatis ac. Donec fermentum massa id aliquet vulputate. Nam vestibulum ornare est, sed tincidunt mauris interdum a. In hac habitasse platea dictumst. Pellentesque sit amet arcu sed risus vehicula fermentum sit amet a neque. Sed imperdiet lacinia magna sit amet dapibus. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean non purus a quam placerat luctus. Proin orci risus, lobortis ut dictum at, gravida at nulla. 

## Unordered and Ordered Lists

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```
let sequence = [1, 1, 2, 3, 5, 8, 13];
for (let i = 0; i < sequence.length; i++) {
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}
```

## Inline Elements

Emphasis, aka italics, with *asterisks* or _underscores_.

Strong emphasis, aka bold, with **asterisks** or __underscores__.

Combined emphasis with **asterisks and _underscores_**.

Strikethrough uses two tildes. ~~Scratch this.~~

Inline `code` with `back-ticks around` it.

This is <mark>marked or highlighted</mark> text.

This is [an example](http://example.com) link.

## Tables

<div class="responsive-table">
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Aliquam aliquam porta massa, et aliquam tortor mollis quis. Sed sodales leo vulputate metus gravida cursus. In fringilla imperdiet elit. Etiam eget sapien elementum, facilisis arcu vitae, finibus ipsum. Aliquam erat nibh, commodo id urna ut, fermentum tristique metus. Morbi egestas ullamcorper vulputate. Mauris dignissim varius arcu in laoreet. In mollis erat velit, in hendrerit arcu lobortis in. Donec facilisis malesuada sapien, non mollis lacus faucibus quis. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. 

## Donec Vitae Tincidunt Erat

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In et diam congue, porta dui ac, rhoncus diam. Quisque imperdiet ex nec lorem gravida ultrices. Fusce ut volutpat metus. Morbi sollicitudin mi vel erat consectetur gravida. Donec consequat velit eget turpis dapibus, sed scelerisque sem pulvinar. Phasellus sit amet arcu eu metus auctor finibus. Phasellus a mauris ut urna auctor ornare. Morbi mollis laoreet orci at iaculis. Aliquam at velit eu elit iaculis cursus. In eu neque leo. Proin felis risus, maximus at ornare non, feugiat ac dui. Nam ornare feugiat sem, ac pretium urna bibendum quis. In hac habitasse platea dictumst. Curabitur sapien ante, molestie sit amet maximus id, malesuada eget felis. Praesent nec imperdiet neque. 

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In et diam congue, porta dui ac, rhoncus diam. Quisque imperdiet ex nec lorem gravida ultrices. Fusce ut volutpat metus. Morbi sollicitudin mi vel erat consectetur gravida. Donec consequat velit eget turpis dapibus, sed scelerisque sem pulvinar. Phasellus sit amet arcu eu metus auctor finibus. Phasellus a mauris ut urna auctor ornare. Morbi mollis laoreet orci at iaculis. 

## Aliquam at Velit eu Elit Iaculis Cursus

In eu neque leo. Proin felis risus, maximus at ornare non, feugiat ac dui. Nam ornare feugiat sem, ac pretium urna bibendum quis. In hac habitasse platea dictumst. Curabitur sapien ante, molestie sit amet maximus id, malesuada eget felis. Praesent nec imperdiet neque.

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How will AI change the work environment? Experts from Canada and Germany discussed this question at the Future of Work & AI Conference held by the [Transatlantic Dialogue Initiative](https://transatlanticdialogue.ca/) on September 18, 2019, in Toronto, Canada. Entrepreneurs, talent acquisition professionals, researchers, professors, and officers of AI-based companies came to listen and join the conversation on how AI will affect recruiting, employee retention, and the economy in the future.

# Recruiting with AI

For talent acquisition professionals, improving their capacity to understand data is key to providing sound recommendations. Of course, the fundamental quality of being people-centric with empathy and relationship building skills would still be the core requirements of the profession. A highly skilled and high touch recruiter is becoming less of a luxury and more of an expectation.

As AI automates menial tasks like sourcing, scheduling, and resume screening, talent acquisition professionals and recruiters will be free to focus on improving the candidate experience. They will have more time to nurture candidates and provide amazing, personalized experiences.

Freeing up recruiters’ time for more impact work sounds exciting and progressive. But it’s important to recognize that we have a lot of work to do in minimizing bias in machine learning models. For example, we’ve seen companies unintentionally screen out[ women](https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G), people of color, and people with disabilities from internal job posts by applying historical data that favors white males.

We may never eliminate hiring bias, but we can do our best to minimize it through mandatory unconscious bias training and the cultivation of labeled data sets.

# Employee retention

Another panel focused on retaining and developing existing talent by using AI to address common reasons people leave companies, such as inadequate compensation and lack of career growth.

Increased corporate transparency is making compensation data more widely available. This data can augment discussions on equitable compensation.

For career growth, innovative companies promote internal mobility by leveraging data that provides actionable insights. Companies like [PaddleHR](https://www.paddlehr.com/) use existing employee data to help people find new roles that may or may not be on traditional tracks within their own companies.

AI will also be able to help companies retain employees by helping human resources professionals better understand and support employees. Available data can already predict job satisfaction. AI can help companies in retaining employees and provide insights into employee’s knowledge, skills, and personality traits.

# AI and the economy

We’re using technology and data in new ways as we go through what some call the[ Fourth Industrial Revolution](https://www.weforum.org/about/the-fourth-industrial-revolution-by-klaus-schwab). We expect a wide range of workforce adjustments. According to the Future Skills Center by the Diversity Institute of Ryerson University, AI will:

*   Enhance 75% of jobs

*   Eliminate 42% of jobs

*   Automate 12% of jobs

*   Create 13% more jobs

(You may notice this adds up to more than 100%; some of these categories overlap. For example, as we discussed earlier, AI may enhance recruiters’ jobs through automation of more menial tasks.)

We must recognize the socioeconomic impact of AI on the future workforce. For instance, AI will eliminate many of the jobs traditionally held by people with little to no post-secondary education. We need to build better profiles of people in this group, so we can support them with appropriate training opportunities. With proper training, people can positively contribute to the workforce. At the same time, companies need to rethink traditional recruiting strategies and create new strategies for diversity and inclusiveness.

# Answers… and more questions

Much of the intellectual conversation at the conference left me with more questions than answers. Companies should already be removing barriers to employment for specific groups and creating inclusive workplace cultures. As AI changes the workforce, preparing and up-skilling workers are key social responsibilities for which we must start planning today.

*At Produvia, we help companies implement artificial intelligence technologies. Interested in exploring these or similar AI use cases for talent acquisition or employee retention? Book a *[*discovery call*](/start)* with the Produvia team.*


Recap of The Future of Work and AI Conference

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Produvia Inc. is a software development agency based in Vancouver, Canada. As a creative services firm, we partner with startups and enterprises to research and develop state-of-the-art technologies. 


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Produvia Inc. was incorporated in Vancouver, Canada in 2013. Since 2013, we have worked with global startups, businesses, and enterprises to research, develop, and deploy artificial intelligence, blockchain, and quantum technologies. We have grown to a network of 600+ freelancers (researchers, engineers, developers, and data scientists).


Produvia began in 2013.

Produvia Story

We believe that it's possible to solve hard problems using data and artificial intelligence (AI) technologies.

At Produvia, we serve $1M+ in revenue companies by developing AI technologies and technologies solving the world’s hardest problems.


Here is what we believe.

Produvia Mission

We believe that companies that leverage exponential technologies, such as artificial intelligence, machine learning, or deep learning, will establish an unfair competitive-advantage over data-driven companies.

At Produvia, we help one-million-plus companies accelerate the adoption of artificial intelligence technologies.


Here is what we envision.

Produvia Vision

Marine Gateway,
450 SW Marine Dr,
Vancouver, BC V5X 0C3,
Canada


Vancouver (Headquarters)

Our Officess

Do you have a project in mind? Let's schedule a \[discovery call]\(/start).


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