Machine learning in 2022 saw significant advancements, particularly with generative AI tools like ChatGPT and DALL-E, and its application in drug discovery, such as Google's DeepMind's achievements. As machine learning transitions from research to practical applications, various trends are predicted for 2023 and beyond. These include the increased use of machine learning in scientific research, the adoption of multi-modal model architectures, and the emergence of large language model services akin to AWS. Additionally, deep learning is becoming more accessible to data scientists, and industry standards for data management are enabling scalable ML model deployment. MLOps is expanding to support pre-trained models, and the democratization of AI tools is facilitating broader adoption across corporate environments. Open-source foundation models could challenge Big Tech's dominance, while decreased corporate spending is prompting a focus on demonstrable ROI from ML tools. Ultimately, machine learning is expected to become ubiquitous in software products, mirroring the integration of networking in the 1990s. Predibase aims to simplify ML deployment with its low-code platform, making it easier for a broad range of users to develop and deploy ML models.