How to Take the Next Step: From MVP to Full-Scale Product
Let’s Bridge the AI deployment gap and bring your PoC into production.
Together We Scale Up Your Machine Learning Projects
We show you how to efficiently reach the next step of your ML lifecycle.
With just one day of intensive work, your team, together with our technical experts, will analyze the architecture as well as the maturity of your PoC and develop a clear plan on how to set up your Machine Learning Operations for deployment.
Managers with decision-making authority but little technical background find it difficult to assess the relevance of new technologies.
Which prerequisites need to be met?
Companies struggle to get to the next level of their ML lifecycle and transition from PoC to production.
Together we close the gap between the status quo and strategic goals with a detailed tech strategy. You will get recommendations for further actions and concrete projects in combination with time and cost overviews.
An optimized Machine Learning tooling and project infrastructure.
What Is It?
We see a lot of companies struggle when it comes to the transition from Proof of Concept to production and scale-up in their Machine Learning lifecycle. The reason for this has become increasingly become clear: Succeeding across the ML lifecycle requires highly sophisticated machine learning operations (MLOps). Our “How to scale your MVP” sprint is a tailor-made experience where, together, we analyze your specific use case and project maturity, and based on that we develop a clear strategy on how to effectively scale your MVP.
What to Expect?
Two times half a day workshops of intense collaborative sparring prepared by our technical experts. Together we identify shortcomings of your current solution with special regard to your production setup, analyze your existing Machine Learning tooling and infrastructure, and present approaches as well as best practices to mitigate current gaps and issues.
What Are the Outcomes?
Our Scale Your MVP sprint will lead to a clear architecture and infrastructure maturity assessment (for MVP) and specific recommendations to scale your Machine Learning Operations.
10 Pitfalls when Operationalizing AI Applications
In our experience most ML projects struggle with one or more of these pitfalls when scaling up:
Data Analytics and Big Data already brought some changes to the infrastructure landscape with them, like massive storage capabilities for different kinds of data as well as distributed systems for processing. AI and in particular ML has additional needs when it comes to performant computing, like GPUs or TPUs that need to be easily accessible to developers. So before getting started, make sure you have the necessary infrastructure (in the cloud or on-premises) to bring the power of AI to your business application.
New tools that support some parts of the ML lifecycle pop up almost every day. Be aware that evaluating them by functionality and potential for adoption requires extensive scanning of the field and deep technical knowledge for comparisons of differences and overlaps in capabilities. There is no need to always stay up to date with the most cutting and therefore sometimes bleeding edge tools, but keep in mind that you’ll need to establish a system and processes to stay informed and be able to introduce relevant new tools regularly.
Given the vast ML(Ops) Tool Landscape, it is tough to choose the right tools. They should fit together nicely and fulfill the requirements of individual use case projects or even across multiple use cases within a department or organization. Depending on the respective maturity level, covering some requirements with a selection process is more important than with others.
IT Governance does not stop at ML solutions. Setting access controls for all models in production, versioning all models, creating the right accompanying documentation, and monitoring models and their results is paramount to adhering to existing IT policies. Responsibilities for this new type of artifact need to be clarified and underpinned with outlining the requirements as well as the organizational authorities.
As the stakes and complexity around AI/ML initiatives grow, so does the need for collaborative, cross-functional AI/ML teams that span departments. Strong, sustained results absolutely depend on close alignment between multiple departments and stakeholders.
Based on the recent report from Algorithmia, only 11% of organizations can put a model into production within a week, and 64% take a month or longer. If you are rather new to the ML game, your organization probably will belong to the latter group. Since up until deployment the new model does not create any value, the training to deployment time is a crucial KPI to evaluate ML maturity of an organization as well as individual projects.
38% of organizations spend more than 50% of their Data Scientists’ time on deployment —and that only gets worse with scale. Data Scientists usually focus on algorithms and models to solve business problems but are not as well versed in software development and in particular infrastructure topics. Let your Data Scientists focus on what they are best at and have ML engineers support them with topics like the deployment of ML models.
Be aware that the way you deploy ML models and in which fashion they are served influences whether the use case requirements can be fulfilled. So, make sure to consider all different aspects to select a suitable deployment pattern. There is e.g. the option to do predictions in a batch or one-by-one on an as-needed basis. You should also consider that models can be provided as part of the user application or as an endpoint. Also, the deployment as well as the serving setup changes in case of high availability needs, e.g. in use cases involving data streams.
ML systems need extensive testing in additional areas compared to traditional software applications. In particular, tests regarding data and the ML model increase complexity and scope within the classical test pyramid.
Within ML applications there are multiple areas with potential for automation. Since software development best practices are often not yet fully adopted, CI/CD pipelines are one area of automation often missed. With some adaptions, they can however bring the same value to AI projects as to classical software projects. Even more so, due to the increased amount of (automated) testing needed for a fully fletched ML system.
In preparation for the first part of the sprint, we will review your MVP use case and the general setup. We will create a question catalog on the maturity stage of the project and its architecture.
How to bring your PoC to production
In this first 4h sprint, we will go through our question catalog in order to understand the specific use cases and project maturity. We will then identify shortcomings of the current solutions with special regard to the production setup. Together we will map the existing Machine Learning tooling and the infrastructure of your department or company.
ML Ops Maturity Analysis
After the first session, we spend some time assessing your MVP architecture based on ML Ops best practices and optimizing it according to your specific needs with the goal to make it production-ready. We will also prepare an extensive gap analysis.
ML Ops for your MVP and company
In the second part of the sprint, we will discuss our suggestions and showcase current gaps and solution approaches. We will present best practices for your future ML Ops setup and define the next steps.
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