The value of AI remains an unattainable goal for many companies. Analyst reports and numerous surveys of IT decision-makers regularly confirm the poor state of IT processes related to AI projects as a key blocker to value. For example, “Gartner research has found that only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so.”*
The reality is that existing operations processes for deploying and managing AI projects are full of friction that manifests in a lack of agility, control, and scale. This has significant impacts not only for IT decision makers but also for data leaders who can lose control over their analytics projects. What if you could remove all these frictions at once and finally trigger a positive AI chain reaction across the enterprise?
Let's see how Dataiku and Snowflake can help.
The Agility Challenge:
According to LinkedIn, the machine learning (ML) engineer is the fourth most sought after profession in the U.S. job market. Many organizations are wasting valuable data and ML engineering time with tedious and repetitive tasks which is a serious blocker for success.
Save ML Engineers’ Time & Increase Efficiency
Dataiku and Snowflake give organizations options for operationalizing models. For real-time scoring, monitoring, and retraining, Dataiku offers elastic AI clusters to deploy models as API endpoints. For batch scoring, Dataiku seamlessly integrates with Snowflake’s Snowpark (with Java and Python UDFs) to operationalize models and score directly within Snowflake. For batch scoring, In-Snowflake model inference has shown upwards of an 8x improvement in scoring speeds versus other platforms thanks to Snowflake native runtime for Java UDFs.
Avoid Duplicate Work and Foster Reusability
Many teams looking to put projects into production fail as they are forced to restart from scratch each time they start a new project or update an old one. Dataiku provides a collaborative environment and reusable components including projects and modular components to help both coders and non-coders maximize the reuse of their work.
The Scale Challenge:
Using notebooks running in isolated environments, like laptops, for experimentation limits the scope of many data scientists' work and creates challenges for deployment to production. Such isolated systems may also have limited resources for computation which leads to issues when the system is insufficient for data volume or type of workload. In addition, these disconnected projects are difficult to put into production as they require considerable refactoring and testing on production frameworks and systems.
Integrate With Cloud Infrastructure for Scalability
Dataiku and Snowflake are complementary solutions that combine Snowflake’s multi-language, elastic processing engine with Dataiku’s machine learning and model management capabilities. Not only does Dataiku’s architecture enable scale with self-service deployment, but it also speeds the process of model inference and monitoring, replacement, and redeployment. Many workloads, including data preparation, feature engineering, and model inference can be pushed down to Snowflake to efficiently execute processing close to your data or leverage Dataiku’s fully-managed Kubernetes solution to offload production workloads to compute clusters for elastically scaling.
The Control Challenge:
For organizations that struggle with AI, it is often the case that too many models arrive poorly tested or incompatible with production data or resources. For those already in production, IT managers complain they cannot properly monitor them. Worst of all, businesspeople are not informed about issues and they are often the first impacted, which can destroy trust. In a recent study, McKinsey revealed that "AI high performers" are the ones who master end-to-end AI lifecycle management versus others. This gap between the best and worst was the highest in the study, suggesting that controlling end-to-end AI lifecycle management is a key factor for success.
Manage, Monitor, and Control With MLOps
Dataiku takes care of the MLOps workflow and allows organizations to manage, monitor, and control processes so that teams can spend more time creating value and less on fixing inefficiencies. For example, Dataiku:
- Seamlessly takes projects from experimentation to production for testing and deployment with a few clicks
- Provides full monitoring of data pipelines and models with data and performance drift detection and alerts and automated retraining based on new data.
- Can enable multiple reviews by different stakeholders and a final sign-off before deployment to production, curbing the proliferation of unreliable or biased models
*Gartner - Gartner Identifies Four Trends Driving Near-Term Artificial Intelligence Innovation, 7 September 2021, https://www.gartner.com/en/newsroom/press-releases/2021-09-07-gartner-identifies-four-trends-driving-near-term-artificial-intelligence-innovation GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.